Final Report Summary - BONAS (BOmb factory detection by Networks of Advanced Sensors)
Executive Summary:
The discovery of hidden bomb factories is of primary importance in the prevention of terrorist activities. In particular, the first stage of IED preparation will take considerably more time as compared to the successive phases of transport to the target and deployment. Therefore, in the early stages of IED preparation of a terrorist attack, investigations can be conducted with fewer time constraints and with greater accuracy than at later stages in support to intelligence personnel in order to monitor their activities to obtain clues, whether an object might actually be misused as an illicit “bomb factory”.
BONAS project (BOmb factory detection by Networks of Advanced Sensors, FP7-SEC-2010 N° 261685) was proposed to realize a substantial contribution to the “Early warning system concerning explosives”, which is “Priority 1” of the “Prevention measures” in the Action Plan on Enhancing the Security of Explosives.
Presently, there are no specific sensors available for precursor detection, so BONAS technological solutions have been created for this very purpose. BONAS have implemented five technologies to address precursor detection in different states: liquid, particle and vapour. Furthermore, these technologies have been linked in a devoted network for their remote control.
The wireless sensor network feature a variety of orthogonal sensing devices and systems (in‐situ and remote), that jointly provide broad chemical spread and low false alarm rates. In particular, BONAS has developed:
1. Innovative particle and gas samplers.
2. A compact lidar/DIAL (Differential Absorption Lidar) system.
3. A QEPAS (Quartz Enhanced Photo‐Acoustic Spectroscopy) sensor.
4. A SERS (Surface Enhanced Raman Spectroscopy) sensor.
5. An electrochemical sensor.
The network of sensors has already been tested in two field campaigns. The first in June 2014 at the Pratica di Mare (Rome - Italy) Military Airport and in September 2014 at the FOI (Swedish Defence Research Agency) facility near Stockholm, jointly with the EMPHASYS project. The field tests have provided the possibility to monitor different suspicious substances.
The project perfectly meets the request to develop new sensors for precursors not only in the device realization but also in their management with a Command a Control system (C2). The C2 centre includes also an expert system, which processes the data from the sensors in order to evaluate the global alarm level. The C2 system is equipped with a machine/user interface with a easy to use touch screen display. All the modules are managed by a master control board interfacing all the modules and managing power supply.
BONAS has reached its goals in realizing new sensors for a large number of compounds, being tested in two different campaigns. All the devices can be camouflaged while some are handheld. In particular, BOANS has contributes to the introduction to the market of a handheld miniRaman spectrometer.
Project Context and Objectives:
BONAS project, during 45 months of activity, has achieved the all the proposed objectives and Milestons in realizing different sensors including a sampling devices for the wireless sensor network on the detection of traces of precursors used in IEDs (Improvised Explosive Devices) production (particulates, gases and/or waterborne) present in the environment surrounding the vicinity of a “bomb factory”.
The wireless sensor network feature a variety of orthogonal sensing devices and systems (in-situ and remote), that will jointly provide broad chemical spread and low false alarm rates. In particular, BONAS has developed:
Innovative particle and gas samplers (WP3).
A compact lidar/DIAL (DIfferential Absorption Lidar) system (WP4).
A QEPAS (Quartz Enhanced Photo-Acoustic Spectroscopy) sensor (WP5).
A SERS (Surface Enhanced Raman Spectroscopy) sensor (WP6).
An electrochemical sensor (former immunosensor) (WP6).
A Command and Control System
An Expert System
A SAW (Surface Acoustic Waves) sensor (WP5).
The EADS sampling system consists of a sampling cone that can be cooled for condensing and freezing vapour from the ambient air and then heated to release the collected vapour onto the SERS surface positioned below. The SERS surfaces are mounted on a movable stage (Figure 3). The Raman spectrometer is mounted in such a way that the SERS surface with the target substance can be automatically moved to the correct measurement position after sampling.
The Raman spectrometer module starts the sampling sequence when instructed to do so by the higher level control system.
Figure 3 – The sampling unit matched with the miniRaman spectrometer; 1 SERS substrate loading; 2) Sampling and freezing; 3) Drop transfer to SERS surface; - 4) Liquid injection (Ammonia); 5) Heating/evaporation; 6) Raman signal collection
The sensor system consists of a miniaturized Raman spectrometer in combination with a sampling system for collecting particles and/or vapors from the surrounding air. The sampling system collects air through an inlet fan and directs this to a cooled SERS-surface were particles are being trapped onto the surface. The SERS-substrate is then dried by heating and placed in position for measurement by the Raman spectrometer. The resulting spectral information is transferred to the command center for analysis and comparison with a spectral library of known precursors.
An advantage of this sensor is its small size and low power requirements that makes it possible to hide it in various places relatively easily. Very low levels are potentially possible to detect depending on the enhancement factor of the surface and the capabilities of the sampling system. It is also possible to detect several different substances with the same sensor since many of the relevant precursors are Raman active (Figure 4).
The BONAS Surface-Enhanced Raman Spectrometry (SERS) based sensor is used for detecting particles and/or vapors in the surrounding air around a place where bombs are being prepared.
A special nanostructured surface coated with a metal (typically gold or silver) is able to enhance the normally weak Raman signal obtained in regular Raman spectroscopy. This makes it possible to detect very small amounts of a substance present on the surface.
The sensor system consists of a miniaturized Raman spectrometer in combination with a sampling system for collecting particles and/or vapors from the surrounding air. The sampling system collects air through an inlet fan and directs this to a cooled SERS-surface were particles are being trapped onto the surface. The SERS-substrate is then dried by heating and placed in position for measurement by the Raman spectrometer. The resulting spectral information is transferred to the command center for analysis and comparison with a spectral library of known precursors.
An advantage of this sensor is its small size and low power requirements that makes it possible to hide it in various places relatively easily. Very low levels are potentially possible to detect depending on the enhancement factor of the surface and the capabilities of the sampling system. It is also possible to detect several different substances with the same sensor since many of the relevant precursors are Raman active.
Figure 4 - The sampling device integrated with the Serstech sensor as deployed during the FOI campaign
The ENEA Lidar/Dial sensor was developed in collaboration with LDI and CSEM. It was successively tested July 2013 in Tallinn and successively at the ENEA Frascati Centre as described in D4.1 and D4.2. The apparatus installed inside a truck was located in the test area of the PDM airport (Figure 5).
Figure 5 – Lidar sensor installed in its truck inside the testing area.
A detailed view of the sensor installed inside the truck is shown in Figure 6 together with the telescope frame view from inside while pointing the caravan used as bomb factory.
Figure 6 – Inner view of the apparatus (right side) and line of sight (right side)
The optical beam of the lidar was pointed toward the open window of the caravan used as bomb factory, located at a distance of 108 m.
The sensor was operation in DOAS mode (DIfferential Optical Absorption Spectroscopy), with a resolution time (range): 8ns (1.2m) number of samples: 1024, number of laser pulses: 10, over an average of 30 measurements. The beam size at lidar was about 25 mm, while the beam size at front of caravan was about 300 mm, with a divergence: about 3 mrad (this value fits well with telescope field of view = 1/363.5 = 2.75 mrad).
While virtues of a lidar/DIAL for stand-off detection of IED precursors include its mobile use from a minivan and its range-resolved medium distance mapping capabilities in a few minutes without raising much suspicion, operating a lidar from an airborne platform offers wide areas coverage capability, increasing the BONAS network detection capability. Therefore, an airborne DIAL/lidar would work inconspicuously as an upper-layer tool in the sensor network, to find a large area that has to be more thoroughly looked at by mid-layer sensors such as ground lidar/DIAL. Data achieved with the lidar have been used for the design of an airborne version of the system (Figure 7).
Figure 7 – Design of the integrated optical device in the ONERA fling platform.
The realisation of an airborne DOAS, based on QCLs, is feasible in a long-path DOAS at nadir or closed to it, where the path is near the terrain. In such solution, a number of lasers may be used, simultaneously operating, each fixed on one wavelength «on» or «off» of either the acetone or some of the impurity gases (here H2O and CH4 have been considered).
A SAW (Surface Acoustic Waves) sensor was already developed by CEA and joined BONAS to exploit the possibility of field tests in a network of sensors. Successively, CEA has integrated this sensor in a portable device to detect and identify explosive vapors based on 14 sensors (2 Quartz Crystal Microbalance sensors, 8 surface acoustic waves sensors and 4 fluorescent sensors). The three different technologies were successfully integrated in a multi-sensor chamber. The sensor is autonomous, stand-alone operated and with Wireless connectivity. A specific data processing was developed in order to detect and identify the main explosive threats with a good probability. The new device, named T-REX , is in the process of industrialisation (Figure 8).
Figure 8 – The new device
A point sensor for the detection and identification of IED’s precursors in the vapour phase has been developed in the frame of the BONAS project. Most of the work was focused on sensors based on Quartz Enhanced Photo Acoustic Spectroscopy (QEPAS) that is one of the many forms of InfraRed Absorption Spectroscopy, particularly interesting for the extremely small size and reduced costs of its analysis cell.
BONAS has realized one of the first sensors that exploit the combination of external cavity quantum cascade lasers (EC-QCLs) and QEPAS for trace detection of IEDs, and to achieve both high sensitivity, and selectivity towards a wide spread of chemicals. Also, it is the first QEPAS sensor that integrates a Vapour Phase Concentration Unit (VPC), with advanced methods and devices specifically designed to sample and pre-concentrate IED substances and precursors.
A laser beam is focused between the two prongs of a quartz tuning fork (QTF), and pulsed at the resonant frequency of the QTF. When the small air volume between the prongs contains a gas that absorbs the laser radiation, acoustic waves are generated that induce the oscillation of the tuning fork. The QTF converts these mechanical oscillations into an electrical output signal that can be demodulated by a lock-in amplifier system. The demodulated signal is proportional to the concentration of the gas, its absorption coefficient, the optical power of the laser, the quality factor of the oscillator, and inversely proportional to the resonant frequency. Absorption spectra can be recorded by scanning the laser wavelength.
Main features,
Integrated sampling, pre-concentration and sensing.
Compactness and portability.
Fully battery operated.
Easily camouflaged.
Stand alone operation and remote control.
Detection and Identification capability against a wide family of VOCs, including: IED’s precursors, CWAs and TICs.
The system has been successfully tested in the lab with several target compounds and interferents, and also in two field campaigns, (Pratica di Mare, Grindsjön), showing its capability to detect vapours of suspicious substances escaping from simulated bomb factories.
Field tests have been carried out at the military airport of PDM to detect IED’s precursors in their vapour phase as they are produced by simulating the production or producing IE inside the ENEA caravan with at least one open window. In order to use realistic amounts of substances, and since the QEPAS sensor detects only gas/vapours, controlled amounts of liquids were previously poured into a glass pot inside the van (according to Deliverable 2.3) then the resulting vapours were sampled from outside in open air. The tests have been carried out with the QEPAS sensor hidden inside a dumpster for camouflage (Figure 9), autonomously operating with rechargeable batteries.
Figure 9 - The QEPAS sensor inside the dumpster used for camouflage
Before the trials, the sensor was set and prepared in the laboratory where refrigerating blocks have been introduced aside (Figure 10 right side) and the battery recharged (Figure 10 left side).
Figure 10 – The QEPAS sensor in the laboratory before the outside installation while battery are recharged (left side) and with the refrigerating devices (right side).
During the outside trials, the dumpster was positioned at defined distance from the van and properly set to collect, sample, analyse them and transfer data (Figure 11).
Figure 11 – The QEPAS sensor close to the caravan ready to operate.
Three compounds included in the priority list for BONAS have been tested with the QEPAS demonstrator, namely: B10, B02 and Int10. In Table 1 a summary list of the experimental tests carried out is reported. For each test: the name of the compound (Compound); The initial state of the compound to be sampled (Initial State); the amount of compound used for the test (Amount); the method used to sample the compound (Sampling) and the stand alone operating conditions of the sensor (Operating Conditions) are reported.
Table 1 - List of tested compounds
Compound Initial State Amount Sampling Operating conditions
B10 liquid 100-500 ml open air by means of a mini pump battery operated
remote data transfer
B02 liquid 100-500 ml open air by means of a mini pump battery operated
remote data transfer
Int10 liquid 200 ml open air by means of a mini pump battery operated
remote data transfer
The QEPAS sensor has demonstrated good detection and identification capability towards B10 and B02. But, as expected, the capability to detect precursors proved to be strongly dependent on the wind strength and direction. The sensor should be positioned downwind to maximize its detection capability. Some issues emerged during the tests, due to the harsh environmental conditions at PDM in June (temperature of about 30°C and strong solar irradiation). Efficient cooling of the sensor inside the dumpster was found necessary to avoid failure of the laser head.
Electrochemical Sensor
For the BONAS project, the UCBL team is developing an electrochemical sensor which will be dedicated to the achievement of simultaneous monitoring of different explosive precursors. With this new sensor, Hexamine, H2O2, NH4NO3 and NaOCl could be detected, by cyclic voltammetry measurement on a homemade chip.
The BONAS electrochemical (EC) sensor uses electrodes to oxidize or reduce molecules soluble in water. The BONAS EC sensor uses four types of electrodes to generate EC signatures, one carbon electrode and three electrodes covered by different noble metals. These signatures are then compared to an EC database generated during the project by analyzing target compounds in water, soapy water and artificial waste water in both laboratory and in-field conditions. As this system aims to contribute to the discovery of bomb factories by the detection of precursors in wastewater, we focused on soluble precursors that could exist in sewage water from improvised explosive (IE) preparation and were included in the BONAS Project Advisory Board’s list of the most interesting targets, based on the current trends of terrorism. The EC sensor was particularly effective in detecting three precursors. Advantages of the system include portability, easily hidden in the sewage system, battery stand-alone operability, and wireless operability. The system has been successfully tested in two field campaigns (Pratica di Mare and Grindsjön (Figure 12)), showing its capability to detect target precursors directly in sewage water drains from simulated bomb factories (Figure 13).
Figure 12 - BONAS EC sensor during the joint demo at FOI Grindsjön
Figure 13 - Operation principle of the BONAS EC sensor. In order to take full advantage of the information provided by the sensor, a unique signature is created for each sample by merging the signals given by the individual surface electrodes as a sequence. For any new acquired measurement, a predicted probability value of the presence of a target is obtained by applying classification models based on a pattern recognition technique.
System architecture - On-board control & processing system
BONAS has developed a novel wireless sensor network able to provide semi-permanent detection capabilities. The BONAS system architecture comprises various individual components that are present in every network node, such as sensors, sampling systems, processing units, communication devices, connectors, etc. Each network node will essentially contain three different subsystems: sensor, control and processing system and network:
Sensors – The BONAS system integrates a variety of different types of technologies and therefore there is a difference in performance achievement by each of the different component. Nonetheless, all components must guarantee the correct interoperability with each other and assure the overall quality of the functionalities of the system.
Control and Processing System – This subsystem is basically composed of a computer with input/output bus (to receive and send data from and to the sensors), local storage (in order to store relevant information in case of temporary miscommunication or to establish patterns avoiding false alarms), central processing unit (processing unit that controls and drives the communications and memory hardware), ad-hoc communication capabilities (antennas and SDR-based (Software Defined Radio) Front-End that allow each network node to forward and receive information from the BONAS central unit), human-machine interface – HMI (this component is only present in the central unit in order to provide the operator with some system interaction and relevant visual data), etc.
The Network represents the virtual environment that each BONAS node will use in order to communicate with each other (including the central unit).
The overall BONAS system architecture can be seen in Figure 14. This architecture structures and decomposes the BONAS problem into several aspects which must be properly addressed, mainly focusing on the On-Board Processing System (including Input/Output aspects) and Network. This architecture, however, requires that sensors and sampling systems provide digital Input/Output interfaces to the On-Board Processing System (specific connectors and communications protocols will be detailed during the requirements phase). Moreover, a mobile BONAS central unit will be developed too, which means that a slightly different on-board processing system must be designed.
Figure 14 – The BONAS system architecture
In detail, the on-board control & processing system basic functions required:
On-board processing
Communication
Human-machine interface
Power management
Data storage
Communication devices
Communication interfaces are provided by the Wireless Adaptive Communication (WAC) nodes (Figure 15). The summary description of each communication interface is listed next:
GPS – receive location information;
USB interface – connect to sensor control devices;
Ethernet interface – connect to sensor control devices or server computers or networks;
2.4GHz wireless interface – connect to wireless sensor network.
Figure 15 – Communication interfaces
For each particular sensor, a specific physical integration was considered. For some of the BONAS sensors, it was used USB interface and, for others, it was used Ethernet interface. The physical integration involved several network communication nodes that were connected to sensors controller devices, see Figure 16. Basically, the communication nodes should collect and forward sensor data wirelessly up to the command and control centre.
Figure 16 – Communication nodes and sensor controller physical interface
Human Machine Interface
The HMI of the system has been designed to be user-friendly and intuitive for any user who shall employ it. The intuitiveness of the system will decrease the amount of time required to train a person to use the system and therefore increase the performance of the system.
Physically, the C2 centre is connected to the BONAS sensor network through an Ethernet interface. A communication network node connected to the C2 centre server is used to forward data from sensors to the C2 centre and vice-versa.
The HIM allows to observe on Google Maps view, the sensor’s displacement on the area under investigation (Figure 17) and their status of connectivity.
Figure 17 – Panel of the sensor’s displacement on the area investigated.
Figure 18 - Sensor’s control panel.
Figure 19 – Status of the different sensors
In the successive figures are displayed the data transferred (Figure 18) and the row data collected (Figure 19). The final panel release the integrated information for the end-user with the help of the expert system (described below).
Cryptography is one of the utilized tools introduced in BONAS to keep information confidential and to ensure its integrity and authenticity. The approach implemented includes the symmetric key AES, Rijndael, RC5, Twofish, Triple DES, Serpent among others as well as the widely used RSA public key algorithm.
The cryptography algorithm must be applied to secure network traffic. One of the most important protocols for communication security is the IPsec (IP security). With this type of security it is possible to send the bits composing the payload secretly and without modification from source to destination keeping unwanted bits out of the message.
Whereas the BONAS system incorporates both wired and wireless sensor devices, wireless security measure has been taken into account. The 802.11 standard prescribes a data link-level security protocol called WEP (Wired Equivalent Privacy), which is designed for the security of wireless connections. When this type of security is enabled, each station possesses a secret key shared with the base station, in the case of the BONAS system, each of the BONAS sensors would have a secret key shared with the command center. In this type of security, the only issue that remains outside the security protocol is how the shared key is indeed shared.
EXPERT SYSTEM
The BONAS expert system performs pattern recognition of the data collected by each network sensor, estimating the predicted probability of detection for each target. The system then integrates the responses from each sensor to provide the end-user with a global alarm level, summarizing information from the entire network into a unique evaluation of the possible criminal threat of an improvised explosives (IE) production site.
The expert system follows a three step workflow while providing a real time response for any new measurement:
The first step is estimating the predicted probability of the presence of each target for each sensor included in the BONAS network. The classification models adopted have been computed by means of a pattern recognition technique on a dataset of experimental data acquired from laboratory and field conditions. These classification models are then applied to any new data received to obtain the corresponding predicted probability value.
The expert system integrates the output given from multiple sensors measuring the same target to provide the user with a unique alarm value for each compound. In this context, sensor location is also considered and only output from sensors located within a user-defined distance range are integrated.
At the last step, the output from the previous steps is further integrated to provide the user with a defined global alarm. The initiated alarm considers the number of detected targets as well as the simultaneous detection of specific targets known to be used together to prepare a specific IE. The alarm value is thus increased when specific couples of targets defined by the user are simultaneously detected. Again, only output from sensors located in the user-defined distance range is considered.
A compact representation of the information provided by the expert system is shown in the dedicated user interface developed in collaboration with TEKEVER. A schematic representation of the three step approach developed for the BONAS expert system is shown in Figure 20.
Figure 20: Schematic representation of the three step approach developed for the BONAS expert system. The alarms due to individual targets are provided as output of Step 2 while the global alarm level is obtained as output of Step 3.
During both test campaigns, the command and control (C2) centre has shown its ability to communicate with all the sensors included in the network and to process, by means of the expert system, the data received. The live alarm monitor end‐user application developed by Tekever allowed a real-time monitoring of the information provided by the expert system. In fact, the user interface reports:
on the left, the alarm obtained for each target substance considering the information of all the sensors able to monitor it (step 2 output);
in the middle, the global alarm which summarizes the information from the entire network into a unique evaluation of the possible criminal threat of an IE production site (step 3 output).
on the right, a map showing the location of the different sensors is also reported. To indicate sensors’ position, markers colored according to the color code of Step 2 are used in order to show a possible positive detection of the sensor. Furthermore, a circle with a radius equal to value maximum distance threshold for data integration is also reported for each sensor. In this way, it is also possible to monitor the data of which sensors are being integrated.
Figure 21: Example of user interface obtained during the test campaign at FOI which shows the positive detections for three targets obtained by two sensors.
An example of user interface obtained during the test campaign at FOI is shown in Figure 21. It can be noticed that, as in the previous cases, three target substances are being detected by two sensors. However, since target B02 and target B15 are one of the couples of targets known to be used together in the preparation of a specific IE, a much higher global alarm level (equal to 60) is obtained in this case as output of Step 3.
The validation and performance assessment of the expert system developed for the project “BOmb factory detection by Networks of Advanced Sensors” (BONAS) has been successfully carried out, according to Task 8.5 and 8.6. Particular attention was paid to evaluate the classification models in terms of false positive and false negative results in order to optimize the performances of the expert system. Two thresholds were considered for each model on the basis of the ROC curve, one to maximize classification efficiency and one that allows to minimize false positive results.
The performance of the developed sensor network were evaluated under relevant environmental conditions of the scenarios from WP2, that is in the presence of interferents and pollutants in both the air and the water, in the Italian Air Force base in Pratica di Mare (Italy) and in Swedish Defence Research Agency (FOI) facility in Grindsjön (Sweden).
The BONAS expert system performed pattern recognition of the data collected by the network of sensors tested, estimating the predicted probability of detection for each target, and then integrating the responses from each sensor. The final aim was to provide the end-user with a global alarm level, summarizing information from the entire network into a unique evaluation of the possible criminal threat of an improvised explosives (IE) production site.
The QEPAS sensor was placed inside a dumpster used for camouflage and used to analyse vapours of IE precursors generated during an IE production (or the simulation of an IE production). The two target substances B02 and B10 included in the priority list for BONAS were tested, resulting in positive detections obtained in all the four days of tests in Pratica di Mare and positive detections obtained during all the tests carried out during the demo at FOI without any false positive detections.
The electrochemical (EC) sensor was successfully tested for main targets such as B08 and target B15, the latter both in pure form and in two commercial products containing target B15 and target B14. During all the experiments performed on field in Pratica di Mare, the EC sensor connected to the sink configuration was able to successfully acquire data and to communicate with both the network and the expert system and all the solutions obtained from the performed experiments were later analysed by ion chromatography in order to obtain the targets’ concentration.
The tests performed during the demo at FOI confirmed the capability of the SERS sensor, hidden in a toolbox, in sampling particles of target material from the atmosphere and successfully acquire spectra.
An extensive validation of the capability of the expert system to effectively integrate the information provided by the different sensors has been also carried out during the two test campaigns at the Italian Air Force base and at the Swedish Defence Research Agency. The end-users were effectively provided with a global alarm level, summarizing information from the entire network (both BONAS and EMPHASIS networks of sensors) into a unique evaluation of the possible criminal threat of an improvised explosives (IE) production site.
Investigation on new Nanostructured supports
The objective of the work was to design metallic nanostructures to provide a strong electromagnetic field enhancement in a spectral range required to address the wavelength of the Raman spectrum of the explosive precursors. This is due to the strong dependence of the Raman signal on the local electric field strength, leading to the significantly enhanced signal of surface enhanced Raman scattering (SERS) allowing to improve sensitivity of the molecular detection. While SERS signal can be obtained from a single molecule in a laboratory environment, this requires state of the art spectrometry with well controlled polarization, collimation and angle of incidence to make full use of resonant electromagnetic field enhancement on the plasmonic substrate, which is very sensitive to these conditions. The objective of this work was to design a robust and cost-effective SERS substrate which can be employed in the project scenarios with the determined sample collection approach. This involves 1) relaxed requirements on angle of incidence, so that there is no need to for collimated illumination but instead strongly focused illumination can be effectively used, 2) relaxed requirements on the polarisation of the excitation, so that there is no need to control polarization of the excitation light, 3) despite the above, to provide strong wavelength resonant enhancement at the required excitation and scattering wavelength, and 4) take into account the working conditions with respect to the decided sample collection scenario.
The objectives have been achieved using modified surface plasmon polaritonic crystals on specifically roughened Au films. The adopted approach provide a practical solution to the SERS detection in the scenarios of the project providing: 1) environmentally stable SERS substrates since Au films are used; 2) the substrates capable to withstand the laser powers needed for the operation of the Raman spectrometer without damage and deterioration; 3) the substrates compatible with the decided sample collection scenario; and 4) the substrates providing the sensitivity of SERS detection superior to the commercially available (e.g. Klarite © substarte) in the decided sample collection scenario.
The design was based on a controllably roughened Au films with the overlayered periodic patterns (the so-called plasmonic crystals) to provide “photonic-crystal”-like structure for surface plasmon waves. The roughness is used for generation of plasmonic hotspots in the nanoscale morphology of metallic surfaces. To further enhance the electric field in these hotspots, the plasmonic crystals can be made to provide additional pathway through which light can interact with the surface by resonantly coupling the incident light to the plasmonic excitations. These crystals have many geometrical parameters that can be tailored for the BONAS applications. The sample collection scenario constrains mean that the self-organisation process of the formation of nanocrystalites of target molecules after the drop casting of analyte solution from the sample collection apparatus will also benefit the overall enhancement. The resulting surface topologies and plasmonic crystals and their performances were presented in Deliverable D6.4.
Taking into account the specifications for the SERS substrates required in the framework of this project: robustness, weak dependence on the illumination conditions and compatibility with the selected sample collection scenario, it was shown that plasmonic crystal substrates based on roughened gold provide the required SERS detection parameters for RDX detection with a 100 fg limit. Moreover, they are thermally stable against extended exposure the high laser powers used and outperformed a commercial SERS substrate (Klarite) for the detection of RDX in the required scenarios of the sample collection.
SERS sensing technique is ubiquitous in many applications where chemical specificity and high sensitivity. Thus, the developed approach to SERS substrate design and tailoring can be used to address other sensing scenarios in environmental, chemical and biological sciences. While the design parameter can be readily tuned to address one or another substance in question, the specific attention in future applications should be paid to the sample collection approach which is different in different applications and can affect the overall performance of the nanostructure.
SERS activity: QUB substrates
Under this deliverable the QUB team investigated a number of substrates for surface enhanced Raman spectroscopy (SERS) with the purpose of deployment in the condenser-sampler system developed at EADS which incorporates the Serstech mini-Raman spectrometer. Three types of substrate were examined or used to a large extent during the course of the project (see Figure 22):-
KlariteTM (discontinued March 2014) – this was a commercial substrate from Renishaw which was based on inverted pyramidal structures etched in silicon and coated with a proprietary Au deposit (Figure 22 (a)).
Gold nanowires (NWs), based on the electrochemical deposition of Au in a porous alumina template where the host alumina matrix was generally subsequently stripped, leaving a free-standing array of Au NWs (Figure 22 (b) and (c)-(f)). A variation in the fabrication procedure utilising a sacrificial polypyrrole core in the alumina pores yielded Au nanotubes as shown in Figure 22 (g).
Gold coated arrays of nanodomes (NDs) as shown in Figure 1(h) and (i). The substrates were formed by a nano-imprint lithography process (R. Winfield, Tyndall National Lab., Ireland under NAP project 376) and subsequently Au-coated at QUB.
The polymer base-material in which the ND arrays were formed was found to be very prone to thermal damage on exposure to the type of high power laser (>150 mW) incorporated in the Serstech system. These substrates were therefore not pursued any further in the context of BONAS. The gold NWs (and nanotubes) served as a platform for rigorous analysis of the SERS performance in terms of 3 different plasmon modes supported in the system (Figure 22 (d)-(f)). The modes involved are a transverse mode (a surface plasmon resonance across the diameter of the free-standing NWs), a longitudinal mode (along the length of the NWs), and a cavity mode, supported only between two adjacent NWs that are in very close proximity (< 10 nm) along their length. The detail of the interplay between these modes and their influence on the Raman response is discussed in detail in ref [1] and need not be developed here. The key point is that the principle of operation depends on the extreme concentration (by the nanostructured surface) of electromagnetic energy into highly confined volumes to exploit the approximate fourth-power dependence on the magnitude of the incident electric field, |E|4. Thus a molecule placed in a region with a 10x field enhancement (relative to the free-space background for the same laser input) will yield a Raman signal that is amplified by 104 relative to that which it
Figure 22 Various SERS substrates trialled during BONAS (a) SEM image of KlariteTM substrate showing inverted square pyramid structures in silicon with magnified detail showing structured, wavy gold deposit within the pyramid structures (Image from H. Wackerbarth et al., Applied Optics, 49, 4362 (2010)). (b) Au NWs grown at QUB in porous alumina template (scale bar 100 nm) with (c) schematic illustration of structure showing (d) transverse, (e) longitudinal and (f) cavity plasmon modes supported in the system. (g) Au nanotube variant of NW substrate (scale bar 100 nm). (h) Top-down view and (i) 45o view of Au ND substrate (scale bars are 100 and 200 nm in (h) and (i) respectively).
would yield in bulk solution. As it turned out the enhancement factor (EF) for the NW substrates was relatively modest, mostly in the 103 range, but they proved extremely useful for physical analysis and understanding. In addition, unlike the nanodome substrates, they proved reasonably robust under laser radiation.
For much of the experimental work we used Klarite on account of the good uniformity across a given substrate and reproducibility from substrate to substrate and batch to batch. We considered these features to be of primary importance in making comparative tests of a range of samples over longer periods of time. Also, while the EF was not outstanding (up to 104 at best), they proved extremely robust thermally. These substrates formed the basis for our work on the novel detection of very dilute acids by means of derivatization – this has recently been reported in Analytical Chemistry[2] where, again, BONAS was clearly acknowledged. Some of the common acids, such as nitric and sulphuric acid can be created as effluent of bomb making either directly or via the production and subsequent interaction of NO2 or SO2 gases with water vapour. The weak acid solutions in atmosphere cannot normally be detected by Raman spectroscopy or even SERS. In SERS the condensation of a weak acid on the substrate is, in itself, ineffective since only a tiny fraction of the target molecular species exists at surface sites of significant field intensity that drive the enhancement. Allowing a droplet of weak acid solution to dry on the SERS substrate is also ineffective since the target analyte evaporates contiguously with the water. The key to solving this detection problem is to form a derivative (salt in this case) of the analyte species (the relevant 〖"NO" 〗_3^- or 〖"SO" 〗_4^(2-)anion) through reaction with excess ammonium hydroxide. In this scenario the derivatized material, NH4NO3 or (NH4)2SO4, is deposited directly on the enhancing surface as the excess water evaporates, yielding significant signal enhancement; the reaction scheme for the case of nitric acid, for example, is:-
HNO3 (aq) + NH4OH(aq) → NH4NO3 (aq) + H2O → NH4NO3 (s) (1)
A depiction of the derivatization scheme, with relevant SERS spectra (comparing spectra from acid solution droplets with those from the derivative species) is shown in Figure 23. This figure illustrates is an improvement of the derivatization methodology over detection in solution by a factor of more than 1500, while the low-concentration detection threshold (~100 ppb) is ~ 4 orders of magnitude times lower compared with liquid phase detection. These findings represent very significant gains.
Figure 23. (a) Sketch illustrating the derivatization drop-casting technique where contaminant in solution forms salt and is then allowed to air dry; magnified 3D schematic of SERS surface illustrates higher concentration of derivative molecules very close to the surface after drying. (b) and (c) show SERS spectra from nitric acid and sulphuric acid solutions respectively:- 5% (black), 1% (blue) concentration liquid solutions on SERS surface before evaporation and 100 ppm (red) solution derivatized using ammonium hydroxide and allowed to dry as described in the text. Each set of spectra is plotted on same Raman intensity scale with individual spectra offset for clarity.
Prospects
While the derivatization technique successfully addressed deliverable 6.3 of BONAS, we consider that it could significantly extend the applicability of the SERS technique in general. The next step would be to demonstrate the technique’s efficacy in relation to the detection of other trace species in solution. The second advance that is required - perhaps surprisingly, after all the 40 years of the existence of SERS as a technique – is the fabrication of a SERS substrate that (i) displays a high EF (ideally >106) and is (ii) reproducible, (iii) uniform across its surface, (iv) thermally robust and (v) cheap. While Klarite scored highly on (ii) to (iv), the EF was relatively modest and it was always way too expensive (by at least a factor of 10) for routine application in chemical lab environments. As noted above, Renishaw withdrew this product earlier in the year. Replacement commercial substrates which are much cheaper are so because they are based on replication processes resulting in polymer or plastic substrates which makes them very prone to thermal damage from the input laser required from Raman spectroscopy. The production of a SERS substrate with all the properties (i) to (v) is a priority. The generally perceived challenge inherent in (i) to (iii) is the production of uniform nanoscale features on the surface. However, our efforts on the Au nanodome structures (Figure 22 (h), (i)) represent an attempt to depart from this philosophy by producing the necessary enhanced field regions through the excitation of a Fano resonance on a microstructured (rather than nanostructured) surface, thus considerably relaxing the spatial resolution requirements of the fabrication process. Physically, our initial results show that this process is at least partially successful and would be worth pursuing; the main drawback is that the current substrates are prone to thermal damage, but this is a materials problem that could be addressed.
In an additional, initial line of experiments we have recently been producing our own silicon pyramid structures through the well-establish KOH anisotropic etching route (Figure 24). These direct ‘Klarite-replacement’ style substrates could also be configured for exploiting Fano resonance in SERS and offer a line of investigation which we are keen to pursue in the future.
Figure 24 - (a) Array of inverted pyramidal pits in Si(100) surface, produced by anisotropic KOH etch and subsequent coating with Au. The windows in the photoresist defining the etch regions were circular, but the etch pits are inherently square in (horizontal) section, hence the shadowing of the Au deposit at the corners. (b) ‘Template stripped’ single Au pyramid which has been extracted from the Si substrate by deposition of resin and subsequent lift-out.
Immunosensor
For the BONAS project, the UCBL team is developing an immunosensor which will be dedicated to the achievement of simultaneous monitoring of different explosives. With this new sensor, RDX and TNT could be detected. The test selectivity is proven by simultaneous detection of the two explosives with different possible cross-reactants.
The UCBL team has selected different targets and associated antibodies from commercial sources. Two explosive compounds were chosen according to their importance in bomb fabrication: TNT and RDX were purchased from LGC Standards, France. The associated antibodies, anti-TNT and anti-RDX were supplied by Strategic Diagnostics, USA. In order to test their selectivity (cross-reactivity), pollutants from different sources (3 pesticides, 1 toxin and 1 control molecule) which can be found in wastewater were also selected: 2-chloro-4-ethylamino-6-isopropylamino-1,3,5-triazine (atrazine), 2,4-dichlorophenoxyacetic acid (2,4-D), 2,4,5-trichlorophenoxyacetic acid (2,4,5-T), 4-benzoylbenzoic acid (4-BBA) and okadaic acid from Prorocentrumconcavum were obtained from Sigma-Aldrich, France
Figure 25 - The competitive multiplex immunoassay set-up.(a) Composition of the different matrices used. (b) Principle of the adhesive microarray assembly with the bottomless plate
Project Results:
BONAS project, during 45 months of activity, has achieved the all the proposed objectives and Milestons in realizing different sensors including a sampling devices for the wireless sensor network on the detection of traces of precursors used in IEDs (Improvised Explosive Devices) production (particulates, gases and/or waterborne) present in the environment surrounding the vicinity of a “bomb factory”.
The wireless sensor network feature a variety of orthogonal sensing devices and systems (in-situ and remote), that will jointly provide broad chemical spread and low false alarm rates. In particular, BONAS has developed:
Innovative particle and gas samplers (WP3).
A compact lidar/DIAL (DIfferential Absorption Lidar) system (WP4).
A QEPAS (Quartz Enhanced Photo-Acoustic Spectroscopy) sensor (WP5).
A SERS (Surface Enhanced Raman Spectroscopy) sensor (WP6).
An electrochemical sensor (former immunosensor) (WP6).
A Command and Control System
An Expert System
A SAW (Surface Acoustic Waves) sensor (WP5).
The EADS sampling system consists of a sampling cone that can be cooled for condensing and freezing vapour from the ambient air and then heated to release the collected vapour onto the SERS surface positioned below. The SERS surfaces are mounted on a movable stage (Figure 3). The Raman spectrometer is mounted in such a way that the SERS surface with the target substance can be automatically moved to the correct measurement position after sampling.
The Raman spectrometer module starts the sampling sequence when instructed to do so by the higher level control system.
Figure 3 – The sampling unit matched with the miniRaman spectrometer; 1 SERS substrate loading; 2) Sampling and freezing; 3) Drop transfer to SERS surface; - 4) Liquid injection (Ammonia); 5) Heating/evaporation; 6) Raman signal collection
The sensor system consists of a miniaturized Raman spectrometer in combination with a sampling system for collecting particles and/or vapors from the surrounding air. The sampling system collects air through an inlet fan and directs this to a cooled SERS-surface were particles are being trapped onto the surface. The SERS-substrate is then dried by heating and placed in position for measurement by the Raman spectrometer. The resulting spectral information is transferred to the command center for analysis and comparison with a spectral library of known precursors.
An advantage of this sensor is its small size and low power requirements that makes it possible to hide it in various places relatively easily. Very low levels are potentially possible to detect depending on the enhancement factor of the surface and the capabilities of the sampling system. It is also possible to detect several different substances with the same sensor since many of the relevant precursors are Raman active.
The BONAS Surface-Enhanced Raman Spectrometry (SERS) based sensor is used for detecting particles and/or vapors in the surrounding air around a place where bombs are being prepared.
A special nanostructured surface coated with a metal (typically gold or silver) is able to enhance the normally weak Raman signal obtained in regular Raman spectroscopy. This makes it possible to detect very small amounts of a substance present on the surface.
The sensor system consists of a miniaturized Raman spectrometer in combination with a sampling system for collecting particles and/or vapors from the surrounding air. The sampling system collects air through an inlet fan and directs this to a cooled SERS-surface were particles are being trapped onto the surface. The SERS-substrate is then dried by heating and placed in position for measurement by the Raman spectrometer. The resulting spectral information is transferred to the command center for analysis and comparison with a spectral library of known precursors.
An advantage of this sensor is its small size and low power requirements that makes it possible to hide it in various places relatively easily. Very low levels are potentially possible to detect depending on the enhancement factor of the surface and the capabilities of the sampling system. It is also possible to detect several different substances with the same sensor since many of the relevant precursors are Raman active.
Figure 4 - The sampling device integrated with the Serstech sensor as deployed during the FOI campaign
The ENEA Lidar/Dial sensor was developed in collaboration with LDI and CSEM. It was successively tested July 2013 in Tallinn and successively at the ENEA Frascati Centre as described in D4.1 and D4.2. The apparatus installed inside a truck was located in the test area of the PDM airport (Figure 6).
Figure 5 – Lidar sensor installed in its truck inside the testing area.
A detailed view of the sensor installed inside the truck is shown in Figure 7 together with the telescope frame view from inside while pointing the caravan used as bomb factory.
Figure 6 – Inner view of the apparatus (right side) and line of sight (right side)
The optical beam of the lidar was pointed toward the open window of the caravan used as bomb factory, located at a distance of 108 m.
The sensor was operation in DOAS mode (DIfferential Optical Absorption Spectroscopy), with a resolution time (range): 8ns (1.2m) number of samples: 1024, number of laser pulses: 10, over an average of 30 measurements. The beam size at lidar was about 25 mm, while the beam size at front of caravan was about 300 mm, with a divergence: about 3 mrad (this value fits well with telescope field of view = 1/363.5 = 2.75 mrad).
While virtues of a lidar/DIAL for stand-off detection of IED precursors include its mobile use from a minivan and its range-resolved medium distance mapping capabilities in a few minutes without raising much suspicion, operating a lidar from an airborne platform offers wide areas coverage capability, increasing the BONAS network detection capability. Therefore, an airborne DIAL/lidar would work inconspicuously as an upper-layer tool in the sensor network, to find a large area that has to be more thoroughly looked at by mid-layer sensors such as ground lidar/DIAL. Data achieved with the lidar have been used for the design of an airborne version of the system.
Figure 7 – Design of the integrated optical device in the ONERA fling platform.
The realisation of an airborne DOAS, based on QCLs, is feasible in a long-path DOAS at nadir or closed to it, where the path is near the terrain. In such solution, a number of lasers may be used, simultaneously operating, each fixed on one wavelength «on» or «off» of either the acetone or some of the impurity gases (here H2O and CH4 have been considered).
A SAW (Surface Acoustic Waves) sensor was already developed by CEA and joined BONAS to exploit the possibility of field tests in a network of sensors. Successively, CEA has integrated this sensor in a portable device to detect and identify explosive vapors based on 14 sensors (2 Quartz Crystal Microbalance sensors, 8 surface acoustic waves sensors and 4 fluorescent sensors). The three different technologies were successfully integrated in a multi-sensor chamber. The sensor is autonomous, stand-alone operated and with Wireless connectivity. A specific data processing was developed in order to detect and identify the main explosive threats with a good probability. The new device, named T-REX , is in the process of industrialisation (Figure 9).
Figure 8 – The new device
A point sensor for the detection and identification of IED’s precursors in the vapour phase has been developed in the frame of the BONAS project. Most of the work was focused on sensors based on Quartz Enhanced Photo Acoustic Spectroscopy (QEPAS) that is one of the many forms of InfraRed Absorption Spectroscopy, particularly interesting for the extremely small size and reduced costs of its analysis cell.
BONAS has realized one of the first sensors that exploit the combination of external cavity quantum cascade lasers (EC-QCLs) and QEPAS for trace detection of IEDs, and to achieve both high sensitivity, and selectivity towards a wide spread of chemicals. Also, it is the first QEPAS sensor that integrates a Vapour Phase Concentration Unit (VPC), with advanced methods and devices specifically designed to sample and pre-concentrate IED substances and precursors.
A laser beam is focused between the two prongs of a quartz tuning fork (QTF), and pulsed at the resonant frequency of the QTF. When the small air volume between the prongs contains a gas that absorbs the laser radiation, acoustic waves are generated that induce the oscillation of the tuning fork. The QTF converts these mechanical oscillations into an electrical output signal that can be demodulated by a lock-in amplifier system. The demodulated signal is proportional to the concentration of the gas, its absorption coefficient, the optical power of the laser, the quality factor of the oscillator, and inversely proportional to the resonant frequency. Absorption spectra can be recorded by scanning the laser wavelength.
Main features,
Integrated sampling, pre-concentration and sensing.
Compactness and portability.
Fully battery operated.
Easily camouflaged.
Stand alone operation and remote control.
Detection and Identification capability against a wide family of VOCs, including: IED’s precursors, CWAs and TICs.
The system has been successfully tested in the lab with several target compounds and interferents, and also in two field campaigns, (Pratica di Mare, Grindsjön), showing its capability to detect vapours of suspicious substances escaping from simulated bomb factories.
Field tests have been carried out at the military airport of PDM to detect IED’s precursors in their vapour phase as they are produced by simulating the production or producing IE inside the ENEA caravan with at least one open window. In order to use realistic amounts of substances, and since the QEPAS sensor detects only gas/vapours, controlled amounts of liquids were previously poured into a glass pot inside the van (according to Deliverable 2.3) then the resulting vapours were sampled from outside in open air. The tests have been carried out with the QEPAS sensor hidden inside a dumpster for camouflage (Figure 3), autonomously operating with rechargeable batteries.
Figure 9 - The QEPAS sensor inside the dumpster used for camouflage
Before the trials, the sensor was set and prepared in the laboratory where refrigerating blocks have been introduced aside (Figure 4 right side) and the battery recharged (Figure 4 left side).
Figure 10 – The QEPAS sensor in the laboratory before the outside installation while battery are recharged (left side) and with the refrigerating devices (right side).
During the outside trials, the dumpster was positioned at defined distance from the van and properly set to collect, sample, analyse them and transfer data (Figure 5).
Figure 11 – The QEPAS sensor close to the caravan ready to operate.
Three compounds included in the priority list for BONAS have been tested with the QEPAS demonstrator, namely: B10, B02 and Int10. In Table 1 a summary list of the experimental tests carried out is reported. For each test: the name of the compound (Compound); The initial state of the compound to be sampled (Initial State); the amount of compound used for the test (Amount); the method used to sample the compound (Sampling) and the stand alone operating conditions of the sensor (Operating Conditions) are reported.
Table 1 - List of tested compounds
Compound Initial State Amount Sampling Operating conditions
B10 liquid 100-500 ml open air by means of a mini pump battery operated
remote data transfer
B02 liquid 100-500 ml open air by means of a mini pump battery operated
remote data transfer
Int10 liquid 200 ml open air by means of a mini pump battery operated
remote data transfer
The QEPAS sensor has demonstrated good detection and identification capability towards B10 and B02. But, as expected, the capability to detect precursors proved to be strongly dependent on the wind strength and direction. The sensor should be positioned downwind to maximize its detection capability. Some issues emerged during the tests, due to the harsh environmental conditions at PDM in June (temperature of about 30°C and strong solar irradiation). Efficient cooling of the sensor inside the dumpster was found necessary to avoid failure of the laser head.
Electrochemical Sensor
For the BONAS project, the UCBL team is developing an electrochemical sensor which will be dedicated to the achievement of simultaneous monitoring of different explosive precursors. With this new sensor, Hexamine, H2O2, NH4NO3 and NaOCl could be detected, by cyclic voltammetry measurement on a homemade chip.
The BONAS electrochemical (EC) sensor uses electrodes to oxidize or reduce molecules soluble in water. The BONAS EC sensor uses four types of electrodes to generate EC signatures, one carbon electrode and three electrodes covered by different noble metals. These signatures are then compared to an EC database generated during the project by analyzing target compounds in water, soapy water and artificial waste water in both laboratory and in-field conditions. As this system aims to contribute to the discovery of bomb factories by the detection of precursors in wastewater, we focused on soluble precursors that could exist in sewage water from improvised explosive (IE) preparation and were included in the BONAS Project Advisory Board’s list of the most interesting targets, based on the current trends of terrorism. The EC sensor was particularly effective in detecting three precursors. Advantages of the system include portability, easily hidden in the sewage system, battery stand-alone operability, and wireless operability. The system has been successfully tested in two field campaigns (Pratica di Mare and Grindsjön(Figure 12)), showing its capability to detect target precursors directly in sewage water drains from simulated bomb factories.
Figure 12 - BONAS EC sensor during the joint demo at FOI Grindsjön
Figure 13 - Operation principle of the BONAS EC sensor. In order to take full advantage of the information provided by the sensor, a unique signature is created for each sample by merging the signals given by the individual surface electrodes as a sequence. For any new acquired measurement, a predicted probability value of the presence of a target is obtained by applying classification models based on a pattern recognition technique.
System architecture - On-board control & processing system
BONAS has developed a novel wireless sensor network able to provide semi-permanent detection capabilities. The BONAS system architecture comprises various individual components that are present in every network node, such as sensors, sampling systems, processing units, communication devices, connectors, etc. Each network node will essentially contain three different subsystems: sensor, control and processing system and network:
Sensors – The BONAS system integrates a variety of different types of technologies and therefore there is a difference in performance achievement by each of the different component. Nonetheless, all components must guarantee the correct interoperability with each other and assure the overall quality of the functionalities of the system.
Control and Processing System – This subsystem is basically composed of a computer with input/output bus (to receive and send data from and to the sensors), local storage (in order to store relevant information in case of temporary miscommunication or to establish patterns avoiding false alarms), central processing unit (processing unit that controls and drives the communications and memory hardware), ad-hoc communication capabilities (antennas and SDR-based (Software Defined Radio) Front-End that allow each network node to forward and receive information from the BONAS central unit), human-machine interface – HMI (this component is only present in the central unit in order to provide the operator with some system interaction and relevant visual data), etc.
The Network represents the virtual environment that each BONAS node will use in order to communicate with each other (including the central unit).
The overall BONAS system architecture can be seen in Figure 8. This architecture structures and decomposes the BONAS problem into several aspects which must be properly addressed, mainly focusing on the On-Board Processing System (including Input/Output aspects) and Network. This architecture, however, requires that sensors and sampling systems provide digital Input/Output interfaces to the On-Board Processing System (specific connectors and communications protocols will be detailed during the requirements phase). Moreover, a mobile BONAS central unit will be developed too, which means that a slightly different on-board processing system must be designed.
Figure 14 – The BONAS system architecture
In detail, the on-board control & processing system basic functions required:
On-board processing
Communication
Human-machine interface
Power management
Data storage
Communication devices
Communication interfaces are provided by the Wireless Adaptive Communication (WAC) nodes (Figure 9). The summary description of each communication interface is listed next:
GPS – receive location information;
USB interface – connect to sensor control devices;
Ethernet interface – connect to sensor control devices or server computers or networks;
2.4GHz wireless interface – connect to wireless sensor network.
Figure 15 – Communication interfaces
For each particular sensor, a specific physical integration was considered. For some of the BONAS sensors, it was used USB interface and, for others, it was used Ethernet interface. The physical integration involved several network communication nodes that were connected to sensors controller devices, see Figure 10. Basically, the communication nodes should collect and forward sensor data wirelessly up to the command and control centre.
Figure 16 – Communication nodes and sensor controller physical interface
Human Machine Interface
The HMI of the system has been designed to be user-friendly and intuitive for any user who shall employ it. The intuitiveness of the system will decrease the amount of time required to train a person to use the system and therefore increase the performance of the system.
Physically, the C2 centre is connected to the BONAS sensor network through an Ethernet interface. A communication network node connected to the C2 centre server is used to forward data from sensors to the C2 centre and vice-versa.
The HIM allows to observe on Google Maps view, the sensor’s displacement on the area under investigation (Figure 11) and their status of connectivity.
Figure 17 – Panel of the sensor’s displacement on the area investigated.
Figure 18 - Sensor’s control panel.
Figure 19 – Status of the different sensors
In the successive figures are displayed the data transferred (Figure 12) and the row data collected (Figure 13). The final panel release the integrated information for the end-user with the help of the expert system (described below).
Cryptography is one of the utilized tools introduced in BONAS to keep information confidential and to ensure its integrity and authenticity. The approach implemented includes the symmetric key AES, Rijndael, RC5, Twofish, Triple DES, Serpent among others as well as the widely used RSA public key algorithm.
The cryptography algorithm must be applied to secure network traffic. One of the most important protocols for communication security is the IPsec (IP security). With this type of security it is possible to send the bits composing the payload secretly and without modification from source to destination keeping unwanted bits out of the message.
Whereas the BONAS system incorporates both wired and wireless sensor devices, wireless security measure has been taken into account. The 802.11 standard prescribes a data link-level security protocol called WEP (Wired Equivalent Privacy), which is designed for the security of wireless connections. When this type of security is enabled, each station possesses a secret key shared with the base station, in the case of the BONAS system, each of the BONAS sensors would have a secret key shared with the command center. In this type of security, the only issue that remains outside the security protocol is how the shared key is indeed shared.
EXPERT SYSTEM
The BONAS expert system performs pattern recognition of the data collected by each network sensor, estimating the predicted probability of detection for each target. The system then integrates the responses from each sensor to provide the end-user with a global alarm level, summarizing information from the entire network into a unique evaluation of the possible criminal threat of an improvised explosives (IE) production site.
The expert system follows a three step workflow while providing a real time response for any new measurement:
The first step is estimating the predicted probability of the presence of each target for each sensor included in the BONAS network. The classification models adopted have been computed by means of a pattern recognition technique on a dataset of experimental data acquired from laboratory and field conditions. These classification models are then applied to any new data received to obtain the corresponding predicted probability value.
The expert system integrates the output given from multiple sensors measuring the same target to provide the user with a unique alarm value for each compound. In this context, sensor location is also considered and only output from sensors located within a user-defined distance range are integrated.
At the last step, the output from the previous steps is further integrated to provide the user with a defined global alarm. The initiated alarm considers the number of detected targets as well as the simultaneous detection of specific targets known to be used together to prepare a specific IE. The alarm value is thus increased when specific couples of targets defined by the user are simultaneously detected. Again, only output from sensors located in the user-defined distance range is considered.
A compact representation of the information provided by the expert system is shown in the dedicated user interface developed in collaboration with TEKEVER. A schematic representation of the three step approach developed for the BONAS expert system is shown in Figure 14.
Figure 20: Schematic representation of the three step approach developed for the BONAS expert system. The alarms due to individual targets are provided as output of Step 2 while the global alarm level is obtained as output of Step 3.
During both test campaigns, the command and control (C2) centre has shown its ability to communicate with all the sensors included in the network and to process, by means of the expert system, the data received. The live alarm monitor end‐user application developed by Tekever allowed a real-time monitoring of the information provided by the expert system. In fact, the user interface reports:
on the left, the alarm obtained for each target substance considering the information of all the sensors able to monitor it (step 2 output);
in the middle, the global alarm which summarizes the information from the entire network into a unique evaluation of the possible criminal threat of an IE production site (step 3 output).
on the right, a map showing the location of the different sensors is also reported. To indicate sensors’ position, markers colored according to the color code of Step 2 are used in order to show a possible positive detection of the sensor. Furthermore, a circle with a radius equal to value maximum distance threshold for data integration is also reported for each sensor. In this way, it is also possible to monitor the data of which sensors are being integrated.
Figure 21: Example of user interface obtained during the test campaign at FOI which shows the positive detections for three targets obtained by two sensors.
An example of user interface obtained during the test campaign at FOI is shown in Figure 15. It can be noticed that, as in the previous cases, three target substances are being detected by two sensors. However, since target B02 and target B15 are one of the couples of targets known to be used together in the preparation of a specific IE, a much higher global alarm level (equal to 60) is obtained in this case as output of Step 3.
The validation and performance assessment of the expert system developed for the project “BOmb factory detection by Networks of Advanced Sensors” (BONAS) has been successfully carried out, according to Task 8.5 and 8.6. Particular attention was paid to evaluate the classification models in terms of false positive and false negative results in order to optimize the performances of the expert system. Two thresholds were considered for each model on the basis of the ROC curve, one to maximize classification efficiency and one that allows to minimize false positive results.
The performance of the developed sensor network were evaluated under relevant environmental conditions of the scenarios from WP2, that is in the presence of interferents and pollutants in both the air and the water, in the Italian Air Force base in Pratica di Mare (Italy) and in Swedish Defence Research Agency (FOI) facility in Grindsjön (Sweden).
The BONAS expert system performed pattern recognition of the data collected by the network of sensors tested, estimating the predicted probability of detection for each target, and then integrating the responses from each sensor. The final aim was to provide the end-user with a global alarm level, summarizing information from the entire network into a unique evaluation of the possible criminal threat of an improvised explosives (IE) production site.
The QEPAS sensor was placed inside a dumpster used for camouflage and used to analyse vapours of IE precursors generated during an IE production (or the simulation of an IE production). The two target substances B02 and B10 included in the priority list for BONAS were tested, resulting in positive detections obtained in all the four days of tests in Pratica di Mare and positive detections obtained during all the tests carried out during the demo at FOI without any false positive detections.
The electrochemical (EC) sensor was successfully tested for main targets such as B08 and target B15, the latter both in pure form and in two commercial products containing target B15 and target B14. During all the experiments performed on field in Pratica di Mare, the EC sensor connected to the sink configuration was able to successfully acquire data and to communicate with both the network and the expert system and all the solutions obtained from the performed experiments were later analysed by ion chromatography in order to obtain the targets’ concentration.
The tests performed during the demo at FOI confirmed the capability of the SERS sensor, hidden in a toolbox, in sampling particles of target material from the atmosphere and successfully acquire spectra.
An extensive validation of the capability of the expert system to effectively integrate the information provided by the different sensors has been also carried out during the two test campaigns at the Italian Air Force base and at the Swedish Defence Research Agency. The end-users were effectively provided with a global alarm level, summarizing information from the entire network (both BONAS and EMPHASIS networks of sensors) into a unique evaluation of the possible criminal threat of an improvised explosives (IE) production site.
Investigation on new Nanostructured supports
The objective of the work was to design metallic nanostructures to provide a strong electromagnetic field enhancement in a spectral range required to address the wavelength of the Raman spectrum of the explosive precursors. This is due to the strong dependence of the Raman signal on the local electric field strength, leading to the significantly enhanced signal of surface enhanced Raman scattering (SERS) allowing to improve sensitivity of the molecular detection. While SERS signal can be obtained from a single molecule in a laboratory environment, this requires state of the art spectrometry with well controlled polarization, collimation and angle of incidence to make full use of resonant electromagnetic field enhancement on the plasmonic substrate, which is very sensitive to these conditions. The objective of this work was to design a robust and cost-effective SERS substrate which can be employed in the project scenarios with the determined sample collection approach. This involves 1) relaxed requirements on angle of incidence, so that there is no need to for collimated illumination but instead strongly focused illumination can be effectively used, 2) relaxed requirements on the polarisation of the excitation, so that there is no need to control polarization of the excitation light, 3) despite the above, to provide strong wavelength resonant enhancement at the required excitation and scattering wavelength, and 4) take into account the working conditions with respect to the decided sample collection scenario.
The objectives have been achieved using modified surface plasmon polaritonic crystals on specifically roughened Au films. The adopted approach provide a practical solution to the SERS detection in the scenarios of the project providing: 1) environmentally stable SERS substrates since Au films are used; 2) the substrates capable to withstand the laser powers needed for the operation of the Raman spectrometer without damage and deterioration; 3) the substrates compatible with the decided sample collection scenario; and 4) the substrates providing the sensitivity of SERS detection superior to the commercially available (e.g. Klarite © substarte) in the decided sample collection scenario.
The design was based on a controllably roughened Au films with the overlayered periodic patterns (the so-called plasmonic crystals) to provide “photonic-crystal”-like structure for surface plasmon waves. The roughness is used for generation of plasmonic hotspots in the nanoscale morphology of metallic surfaces. To further enhance the electric field in these hotspots, the plasmonic crystals can be made to provide additional pathway through which light can interact with the surface by resonantly coupling the incident light to the plasmonic excitations. These crystals have many geometrical parameters that can be tailored for the BONAS applications. The sample collection scenario constrains mean that the self-organisation process of the formation of nanocrystalites of target molecules after the drop casting of analyte solution from the sample collection apparatus will also benefit the overall enhancement. The resulting surface topologies and plasmonic crystals and their performances were presented in Deliverable D6.4.
Taking into account the specifications for the SERS substrates required in the framework of this project: robustness, weak dependence on the illumination conditions and compatibility with the selected sample collection scenario, it was shown that plasmonic crystal substrates based on roughened gold provide the required SERS detection parameters for RDX detection with a 100 fg limit. Moreover, they are thermally stable against extended exposure the high laser powers used and outperformed a commercial SERS substrate (Klarite) for the detection of RDX in the required scenarios of the sample collection.
SERS sensing technique is ubiquitous in many applications where chemical specificity and high sensitivity. Thus, the developed approach to SERS substrate design and tailoring can be used to address other sensing scenarios in environmental, chemical and biological sciences. While the design parameter can be readily tuned to address one or another substance in question, the specific attention in future applications should be paid to the sample collection approach which is different in different applications and can affect the overall performance of the nanostructure.
SERS activity: QUB substrates
Under this deliverable the QUB team investigated a number of substrates for surface enhanced Raman spectroscopy (SERS) with the purpose of deployment in the condenser-sampler system developed at EADS which incorporates the Serstech mini-Raman spectrometer. Three types of substrate were examined or used to a large extent during the course of the project (see Figure 22):-
KlariteTM (discontinued March 2014) – this was a commercial substrate from Renishaw which was based on inverted pyramidal structures etched in silicon and coated with a proprietary Au deposit (Figure 22 (a)).
Gold nanowires (NWs), based on the electrochemical deposition of Au in a porous alumina template where the host alumina matrix was generally subsequently stripped, leaving a free-standing array of Au NWs (Figure 22 (b) and (c)-(f)). A variation in the fabrication procedure utilising a sacrificial polypyrrole core in the alumina pores yielded Au nanotubes as shown in Figure 22 (g).
Gold coated arrays of nanodomes (NDs) as shown in Figure 1(h) and (i). The substrates were formed by a nano-imprint lithography process (R. Winfield, Tyndall National Lab., Ireland under NAP project 376) and subsequently Au-coated at QUB.
The polymer base-material in which the ND arrays were formed was found to be very prone to thermal damage on exposure to the type of high power laser (>150 mW) incorporated in the Serstech system. These substrates were therefore not pursued any further in the context of BONAS. The gold NWs (and nanotubes) served as a platform for rigorous analysis of the SERS performance in terms of 3 different plasmon modes supported in the system (Figure 22 (d)-(f)). This work was reported in ref [1] in which support from BONAS was acknowledged. The modes involved are a transverse mode (a surface plasmon resonance across the diameter of the free-standing NWs), a longitudinal mode (along the length of the NWs), and a cavity mode, supported only between two adjacent NWs that are in very close proximity (< 10 nm) along their length. The detail of the interplay between these modes and their influence on the Raman response is discussed in detail in ref [1] and need not be developed here. The key point is that the principle of operation depends on the extreme concentration (by the nanostructured surface) of electromagnetic energy into highly confined volumes to exploit the approximate fourth-power dependence on the magnitude of the incident electric field, |E|4. Thus a molecule placed in a region with a 10x field enhancement (relative to the free-space background for the same laser input) will yield a Raman signal that is amplified by 104 relative to that which it would yield in bulk solution. As it turned out the enhancement factor (EF) for the NW substrates was relatively modest, mostly in the 103 range, but they proved extremely useful for physical analysis and understanding. In addition, unlike the nanodome substrates, they proved reasonably robust under laser radiation.
Figure 22 Various SERS substrates trialled during BONAS (a) SEM image of KlariteTM substrate showing inverted square pyramid structures in silicon with magnified detail showing structured, wavy gold deposit within the pyramid structures (Image from H. Wackerbarth et al., Applied Optics, 49, 4362 (2010)). (b) Au NWs grown at QUB in porous alumina template (scale bar 100 nm) with (c) schematic illustration of structure showing (d) transverse, (e) longitudinal and (f) cavity plasmon modes supported in the system. (g) Au nanotube variant of NW substrate (scale bar 100 nm). (h) Top-down view and (i) 45o view of Au ND substrate (scale bars are 100 and 200 nm in (h) and (i) respectively).
For much of the experimental work we used Klarite on account of the good uniformity across a given substrate and reproducibility from substrate to substrate and batch to batch. We considered these features to be of primary importance in making comparative tests of a range of samples over longer periods of time. Also, while the EF was not outstanding (up to 104 at best), they proved extremely robust thermally. These substrates formed the basis for our work on the novel detection of very dilute acids by means of derivatization – this has recently been reported in Analytical Chemistry[2] where, again, BONAS was clearly acknowledged. Some of the common acids, such as nitric and sulphuric acid can be created as effluent of bomb making either directly or via the production and subsequent interaction of NO2 or SO2 gases with water vapour. The weak acid solutions in atmosphere cannot normally be detected by Raman spectroscopy or even SERS. In SERS the condensation of a weak acid on the substrate is, in itself, ineffective since only a tiny fraction of the target molecular species exists at surface sites of significant field intensity that drive the enhancement. Allowing a droplet of weak acid solution to dry on the SERS substrate is also ineffective since the target analyte evaporates contiguously with the water. The key to solving this detection problem is to form a derivative (salt in this case) of the analyte species (the relevant 〖"NO" 〗_3^- or 〖"SO" 〗_4^(2-)anion) through reaction with excess ammonium hydroxide. In this scenario the derivatized material, NH4NO3 or (NH4)2SO4, is deposited directly on the enhancing surface as the excess water evaporates, yielding significant signal enhancement; the reaction scheme for the case of nitric acid, for example, is:-
HNO3 (aq) + NH4OH(aq) → NH4NO3 (aq) + H2O → NH4NO3 (s) (1)
A depiction of the derivatization scheme, with relevant SERS spectra (comparing spectra from acid solution droplets with those from the derivative species) is shown in Figure 23, taken from ref. [2]. This figure illustrates is an improvement of the derivatization methodology over detection in solution by a factor of more than 1500, while the low-concentration detection threshold (~100 ppb) is ~ 4 orders of magnitude times lower compared with liquid phase detection. These findings represent very significant gains.
Figure 23. (a) Sketch illustrating the derivatization drop-casting technique where contaminant in solution forms salt and is then allowed to air dry; magnified 3D schematic of SERS surface illustrates higher concentration of derivative molecules very close to the surface after drying. (b) and (c) show SERS spectra from nitric acid and sulphuric acid solutions respectively:- 5% (black), 1% (blue) concentration liquid solutions on SERS surface before evaporation and 100 ppm (red) solution derivatized using ammonium hydroxide and allowed to dry as described in the text. Each set of spectra is plotted on same Raman intensity scale with individual spectra offset for clarity.
Prospects
While the derivatization technique successfully addressed deliverable 6.3 of BONAS, we consider that it could significantly extend the applicability of the SERS technique in general. The next step would be to demonstrate the technique’s efficacy in relation to the detection of other trace species in solution. The second advance that is required - perhaps surprisingly, after all the 40 years of the existence of SERS as a technique – is the fabrication of a SERS substrate that (i) displays a high EF (ideally >106) and is (ii) reproducible, (iii) uniform across its surface, (iv) thermally robust and (v) cheap. While Klarite scored highly on (ii) to (iv), the EF was relatively modest and it was always way too expensive (by at least a factor of 10) for routine application in chemical lab environments. As noted above, Renishaw withdrew this product earlier in the year. Replacement commercial substrates which are much cheaper are so because they are based on replication processes resulting in polymer or plastic substrates which makes them very prone to thermal damage from the input laser required from Raman spectroscopy. The production of a SERS substrate with all the properties (i) to (v) is a priority. The generally perceived challenge inherent in (i) to (iii) is the production of uniform nanoscale features on the surface. However, our efforts on the Au nanodome structures (Figure 1 (h), (i)) represent an attempt to depart from this philosophy by producing the necessary enhanced field regions through the excitation of a Fano resonance on a microstructured (rather than nanostructured) surface, thus considerably relaxing the spatial resolution requirements of the fabrication process. Physically, our initial results show that this process is at least partially successful and would be worth pursuing; the main drawback is that the current substrates are prone to thermal damage, but this is a materials problem that could be addressed.
In an additional, initial line of experiments we have recently been producing our own silicon pyramid structures through the well-establish KOH anisotropic etching route (Figure 24). These direct ‘Klarite-replacement’ style substrates could also be configured for exploiting Fano resonance in SERS and offer a line of investigation which we are keen to pursue in the future.
Figure 24 (a) Array of inverted pyramidal pits in Si(100) surface, produced by anisotropic KOH etch and subsequent coating with Au. The windows in the photoresist defining the etch regions were circular, but the etch pits are inherently square in (horizontal) section, hence the shadowing of the Au deposit at the corners. (b) ‘Template stripped’ single Au pyramid which has been extracted from the Si substrate by deposition of resin and subsequent lift-out.
Immunosensor
For the BONAS project, the UCBL team is developing an immunosensor which will be dedicated to the achievement of simultaneous monitoring of different explosives. With this new sensor, RDX and TNT could be detected. The test selectivity is proven by simultaneous detection of the two explosives with different possible cross-reactants.
The UCBL team has selected different targets and associated antibodies from commercial sources. Two explosive compounds were chosen according to their importance in bomb fabrication: TNT and RDX were purchased from LGC Standards, France. The associated antibodies, anti-TNT and anti-RDX were supplied by Strategic Diagnostics, USA. In order to test their selectivity (cross-reactivity), pollutants from different sources (3 pesticides, 1 toxin and 1 control molecule) which can be found in wastewater were also selected: 2-chloro-4-ethylamino-6-isopropylamino-1,3,5-triazine (atrazine), 2,4-dichlorophenoxyacetic acid (2,4-D), 2,4,5-trichlorophenoxyacetic acid (2,4,5-T), 4-benzoylbenzoic acid (4-BBA) and okadaic acid from Prorocentrumconcavum were obtained from Sigma-Aldrich, France
Figure 25 - The competitive multiplex immunoassay set-up.(a) Composition of the different matrices used. (b) Principle of the adhesive microarray assembly with the bottomless plate
Potential Impact:
BONAS project system has proposed a business model during the creation of the technology roadmap of the system, in which all the segments composing the exploitation strategy of a possible BONAS system have been provided (Figure 26).
Figure 26 – The BONAS technological roadmap
Four main exploitation strategies have been defined for the commercialization of the system, and subsequent service offering generated from acquisition of the system. These were:
• Sell developed sensor and sampling device individually
• Sell BONAS system as a whole
• Provide a customizable offer of the BONAS system
• Provide post-sale services
Given these four main possibilities of exploitation strategies, and given the relatively niche market in which the BONAS system aims to, one main business case has been identified, which could meet current market needs and provide a system capable to increase the capability in counter-terrorism actions, namely a Technology Manufacturer (TM).
A TM business model develops and integrates sensor devices, as well as sampling devices to be commercialized as both individual products and as a whole BONAS system. This dual commercialization vision provides to the user the possibility to acquire a customized system which meets their needs. The revenues in this case are obtained from the commercial sales from both individual sensors and system. In this case the capital costs (CAPEX) required to develop the technology is rather high while the OPEX will mainly depend on the involvement of the vendor in user-related services.
The TM business model is divided into the nine main building blocks defined which delineate each of the properties of the proposed business model (Figure 27).
• Key partners
• Key activities
• Key resources
• Value propositions
• Customer relationships
• Channels
• Customer segment
• Cost Structure
• Revenue Streams
The business model, in sum, is a model which aims at offering an innovative product(s) to requirements driven activities in a niche market. The schematic representing the business model described before is depicted in Figure 27.
Figure 27 - BONAS business model details
Strengths and weaknesses of BONAS
A strength and weakness study was performed towards the identified business model, in which a SWOT assessment was implemented. The SWOT analysis includes the identification of the internal status of the solution under analysis, through the analysis of the Strengths and Weaknesses (SW) of the solution at hand, as well as the set of opportunities which might be available for the future upbringing of BONAS (Opportunities - O). Finally the envisioned harmful elements towards this system application will be taken into account (Threats – T).
Strengths
• Varied sensor devices integration
The BONAS system integrates several different sensor devices which provide the required help to perceive the presence of bomb precursors in an urban environment. The BONAS system provides this capability with proven performance, even already with the sensor devices at a low TRL stage.
• Capability for mobile sensor devices
Most sensor devices have the capability to be mobile, i.e. they may be moved throughout the area to be monitored without any major difficulty. This capability enhances the monitoring capability that the BONAS system provides, giving a larger amount of freedom during he sensor placement to the end-user.
• Independent networking infrastructure
The network established in BONAS, used for the transmission of the sensorial data from the sensor devices to the command center is conceived and upheld through the use of an independent infrastructure, thus not relying on external infrastructures to transmit data within the established network.
• Customization and flexibility options
The BONAS system was designed to accommodate varied customization options for the end-user of the system, in which, among other, the number of sensors, network capability, inclusion of other sensor may be modified depending on the specific requirement needs of the end-user.
• Deployment
The BONAS system is very easy to deploy, and since no external infrastructure is required, there is no need for complicated network establishment.
• User friendly
The BONAS system presents a user friendly and easy-to-use GUI, which requires very little training time for a human operator to use.
• Scalability
The BONAS system was designed and developed to accommodate possible additions of further sensor technologies to the system. This way the system is not closed and compatible only with the sensors already integrated, but rather open so that any new innovative sensor technology may be added to the operation of the system.
• Camouflaged system
Most of the BONAS sensors have been designed and developed in order to be easily camouflaged in unsuspicious locales using common used objects found on vicinities of houses and neighbourhoods.
Weaknesses
• Low Maturity
Some of the sensor devices, developed for the BONAS system, are still in a very low level of maturity, requiring further development in order to meet the requirements for a commercial product.
• Physical ruggedness
Some sensor devices and the network connection devices are at this stage not applicable in very inhospitable environments, such as in very humid weather conditions, however some design choices have already been thought of and even implemented to try to circumvent this fact.
• Specialized Operators
Some of the sensor devices in the BONAS project must still be operated by a specialized human operator, given the high level of tuning and checks that must be performed throughout the use of the sensors. There is also the case of some sensors which don’t need a human operator by the sensor since they may be controlled through the HMI of the BONAS system.
• High power consumptions requirements
Some of the sensor devices present a very large power consumption, which requires the use of large power storage components nearby, rendering the camouflage of the sensors more difficult.
Opportunities
• Security necessity
Unfortunately, terrorist attacks where IED or home-made bombs are used to inflict fear and terror to people are still a reality in this age, and where safety seems to be one of the most valuable assets that a nation may have. The BONAS system is devoted to provide an instrument with its main objective in finding bomb factories, in order to dismantle any terrorist activities before the bombs are used. Therefore, this provides with an opportunity for the commercialization and use of the system in security-prone activities.
• Integration of other sensor devices or connection to external systems
The scalability of the BONAS system may be one of the most important features of the systems, since it provides the capability to integrate more sensor devices, or even another similar system which may complement the monitoring activities performed by BONAS. This provides an opportunity for the system towards the integration with other system.
• Little competition in the market
At the moment of writing there is no commercially available system as BONAS in the market, giving it the opportunity for BONAS to be the first of its kind on the market.
Threats
• Niche Market
The BONAS system is directed towards a niche market which presents little number of end-users which may integrate the system to their everyday activities. Therefore, any competition that may arise will be competing with the BONAS for an already very limited market.
• Regulation
Some of the sensor devices require to be regulated in order to be able to be used in urban environments. This fact increases the cost to further develop and finally deploy the system in the environment that it is supposed to be used.
• Competing products
Other systems may arise which may have the same characteristics of BONAS, which would compete with the system in a very limited available market. However, the independent network infrastructure and high performance shown by the sensor devices reduce the impact of this threat.
• New and innovative fear inducing techniques by terrorist groups
Terrorist groups are always trying to find new and innovative ways to use terror as their ally in order to inflict fear to people. The BONAS project is mostly devoted to the detection of bomb precursors during the fabrication of bombs in a bomb factory, however if terrorist groups find other ways to inflict fear into humans using other techniques other than bombs, this would render the BONAS system useless for this application. Nonetheless, given the historic terrorist events throughout time, it is foreseen that, unfortunately, homemade bombs will continue to be an issue that must be tackled.
BONAS business model evaluation
It is important for each organization to perform a regular assessment of the adopted business model and indicate what their objectives are for the results obtained from the project at hand and also to evaluate the health of the market to which it is being directed.
This evaluation consists mainly on answering a set of questions that help define the properties of the business building blocks.
Although a first assessment of the business model defined for BONAS is performed in this document, a deep assessment of the business model and eventual updates must be performed recursively taking into account the changes in the target market and the development phases of the system and as such is out of scope of this document.
Strengths and weaknesses
It is of extreme importance to identify the strengths of the business model defined for the system, in order to fully understand the opportunities that may emerge. In contrast the weaknesses must also be understood in order to identify the threats that are envisioned for the commercialization of the system at hand.
One of the main strengths of the business model proposed for the BONAS system is the fact that it is foreseen the development and commercialization of highly innovative technological products. This fact brings the advantage that typically high valued items contribute to an enrichment of the revenue streams. The value proposition of the business model is characterized for being very well aligned to the needs of the possible end-users to oppose the activities performed by terrorist groups, by detecting and provide aid for the dismantling of bob factories that may be present in a certain city. The business model also takes into account the scalability of the system which renders it prone for the inclusion of other sensorial technologies in the future, providing high customization possibilities to the end-user. The aim to which the BONAS system is directed to may even change somewhat for monitoring and detection of other elements, by adding other sensor devices of the system which may meet the requirements for other types of activities other than bomb factory detection. This enables the business model to be somewhat flexible, given the high customization opportunities provided by the system.
In contrast, the weaknesses of the system are mostly concerned with the niche market to which the system commercialization opportunities are directed. This fact will decrease the reach of the value proposition, as well as increase the risk and uncertainties of the revenue streams. However, given the high customization possibilities of the system, the customer’s requirements may be met through the inclusion of other features, sustained by the design choices implemented in the system. Finally, as the development of customized solutions based on BONAS products and services is attained by involving the clients in a co-creation process and by providing them personal assistance, it is expectable that strong and enduring relationships are established with the customer segment.
The following table presents a summary of the strengths and weaknesses towards each building block of the presented business model.
Building Block Strengths Weaknesses
Key partners Partnerships with further sensor and sampling devices manufacturer are encouraged.
Key activities Complete, ready to use system with high customization possibilities. Activities pertaining to security measures of the general population.
Key resources Highly specialized and difficult to replicate. High development requirements. Costly maintenance issues.
Value propositions Offer a novel system capable to help fighting terrorist and their activities, by increasing the security against such activities in a certain zone.
Customer relationships Co-creation and personal assistance
Channels Use of already established relationships, which proves to be efficient. Must reach all the potential clients.
Customer segment Continuous acquisition of clients Niche market difficult to permeate.
Cost structure Known costs for the development of sensors and system. Difficult to permeate niche market.
Revenue Streams The main objective of the system is directed to increasing the security of the person living in the area where the system is introduced, in order to hamper terrorist activities and their bomb making processes. This fact may encourage customers to acquire the system. The system will only be sold one time to the end customer, in which a repeat purchase is limited, unless new sensor devices and requirements arise from the customer’s point of view. This renders the foreground of revenues unpredictable and the corresponding risk relatively high.
Table 2 - Summary of strengths and weaknesses of the BONAS business model
Opportunities
The main activity in which the BONAS system excels in providing support in, is related to counter-terrorism activities, which, unfortunately, are a very real threat nowadays. Thus the BONAS system is devoted to make use of several sensor technologies which working together under the same command center may provide real time monitoring data from surrounding indicating the presence of possible terrorist bomb factory sites in the vicinity. This set of features of the system encourages possible customers to acquire this system and its abilities to fight this threat. The BONAS system was designed and developed by providing customization opportunities to the customer, thus allowing it to be very flexible in terms of what kind of sensorial technologies may be added to the network in order to meet the requirements from the customer. However from the business model point of view, the recurring revenues from this on-time transaction are very low, and unless maintenance services or training services are required, there is no recurring revenue from the sale of this system.
Threats
This section identifies the threats that may arise from the defined business model of the BONAS system, in general threats appear from the weaknesses identified in the business model once it is implemented. Thus, similarly to the opportunities, these are very difficult to perceive beforehand.
The main threat that this business model envisions is the application of the system in a very limited niche market, which only a very few possible customers may buy the system as a whole. This is why the possibility to customize and buy only some elements of the system is available, in order to broaden up the customer range to which the BONAS system is aiming at. Another threat is related to the actual development and maintenance of highly specialized sensor devices which is still very costly and increase significantly the cost of the system, thus rendering it a valuable asset for customers to acquire. A third threat may arise if terrorist activities change in terms of fear inducing solutions in the future, i.e. if terrorist groups start using other means for inflicting fear instead of the use homemade bombs, a system which may finality is to detect bomb factories is rendered useless for any customer and must be clearly revised in order to shift its main direction to another pending problem.
To prevent the possibility of a newcomer to penetrate the market segments to which the BONAS system is aiming at, and in order to convince the customers to use BONAS, first a high customization prospect is introduced to the system, giving it an edge towards the accomplishments of the requirements of the customer and it will be also important to establish strategic partnerships with business actors, which are already involved in the corresponding market segments.
Partner exploitation strategies
A brief definition of some partner’s exploitation strategy is presented in the following sections, namely towards the technology developed in the BONAS project. For the others not mentioned the future interest is in the use of the expertize achieved in the BONAS for future applications.
ENEA
ENEA participated in the realization of a lidar/DIAL remote sensor providing the vapour concentration of explosive precursors as a function of the range from the system. Public administrations or private bodies interested in homeland security (Army, Police, airports…) are very interested in the lidar/DIAL remote sensor. ENEA will exploit its knowhow and system by establishing collaborations with the abovementioned institution, serving at different levels: consulting, joint participation to Italian and/or European tenders, supply of complete systems. Success in this strategy will pave the way to the development of feasible, cost-effective and robust lidar/DIAL remote sensor for bomb factories, opening up employment opportunities in Italy/Europe since it addresses a global market need that is not supplied commercially at present. The prototype is reasonably cheap and can be produced for monitoring of airports, stations, squares.
CREO
This market can offer very interesting business opportunities for QEPAS sensors hyphenated to Gas Chromatographic (GC) columns. Currently, no commercial product exists. Miniaturized QCLs and silicon micro-GCs make it possible the development of highly integrated and miniaturized GC/QEPAS systems that, by exploiting the physical and chemical scaling laws of sensitivity and response speed, can compete with IMS and GC/Mass instruments in terms of costs, ruggedness, maintenance, portability, and sensing performance. QEPAS sensors have already proven high specificity against precursors, and ability to operate in a wide detection range, from real traces to ‘big’ traces.
CREO is currently searching for new research opportunities within the H2020 program, and has recently proposed a GC/QEPAS sensor as part of an integrated system for the analysis of cargo containers.
SAB
SAB has developed a very small and compact Raman spectrometer module during the BONAS project. As a matter of fact, some modules have already been commercialized and sold for evaluation to such customers. Furthermore, the spectrometer module has also been integrated independently by Serstech into a handheld Raman Spectrometer (Figure 28). This has been fully productized and is now commercially available and used by early customers for chemical identification. A network of sales partners in Europe and Asia has already been established and it will grow further.
Figure 28 - By being small, easy to use and affordable, the Serstech 100 Indicator helps customers within police, rescue services, customs and industry to save time, money and even lives.
TEK
All of the results obtained by TEK during its work in BONAS serve a specific purpose in the company’s research roadmap. TEK defines an internal R&D agenda based on existing or developing product lines. This agenda identifies capabilities and functionalities that we desire to include in each of our products based on:
• User needs and requirements passed on to TEK;
• Market opportunities;
• Lack of competition for a particular supported functionality;
• Internal technology watch activities;
• Evolution of existing functionalities.
After the end of the BONAS project, further work will be required by TEK in order to have the WAC ready as products for full commercialization. This includes:
• Further design efforts of the enclosure of the WAC radio, increasing the ruggedness and resistance to water for use in more demanding environments
• Replacement of antennas with higher gain directional antennas, enabling communications through longer distances
• Improvements on power management capabilities of the WAC device
LDI
The following work was performed by LDI for the BONAS project:
LIDAR mockup development that allows remote detection of substances (acetone, etc.) which are used in manufacturing of explosives by scanning in DIAL and the DOAS modes.
LDI presents the following exploitation steps for the future of the technology developed in BONAS:
• Design, assembly, installation, configuration and testing of LIDAR.
• Development and modification of flexible software.
• Company participation in planned meetings and testing.
CSEM
CSEM also contributed to the proposal for perspective lightweight airborne sensor for detection of unauthorized explosive precursors, based on DOAS method, exploiting the same wavelength range as the lidar/DIAL prototype. In future CSEM seeks participation with the same activity, where the acquired expertise may be applied in the realisation and the exploitation of lidar systems, ground based or airborne, in the interest of public administrations and/or private bodies active in homeland security. This includes consulting, developments and supply of lidar detection subsystems, as well as participation to tenders (national and/or European) with complete lidar systems. CSEM also exploits the possibility to propose to industry and environmental agencies, the realisation of DIAL/DOAS systems based on the same principles as the one realised in BONAS, for detection of industrial gases pollutions.
EADS
At the turn of the year 2014 the former EADS group was renamed into Airbus Group and a reorganization process across the group has taken place. Airbus group unites the capabilities of the three market leaders Airbus, Airbus Defence and Space and Airbus Helicopters. The group-wide research and development operates under the name Airbus Group Innovations (former EADS innovation works). Airbus Group Innovations was forced to change its orientation towards the civil aircraft manufacturer Airbus. Besides this restructuring process Airbus as a group is always interested in innovative sensor developments and functionalities for here products.
Obviously sensor systems for critical infrastructure and security application are showing some related functionalities and market opportunities were the company should find similarities and marked opportunities. Involved BONAS partners are already starting finding out the necessaries and opportunities. If opportunities are exist, involved component level production could be licensed / subcontracted by EADS business units from/to SME technology suppliers.
ONE
The work done in BONAS for onboard lidar system for IED detection , is a first step which can be regarded as a feasibility work. The laser sources are commercially available and implementation onboard a UAV has been studied in terms of size limit. Any work performed in Bonas will hopefully be used to build new projects dedicated to onboard detection of IED, or for other onboard UAV lidar applications.
The Dissemination process of the BONAS project was divided in two major groups, Internal and External Dissemination, in which, each one presents its own instruments for completing the objective at hand.
Internal Dissemination
The internal dissemination is a process in which is intended the distribution of external information and data throughout the consortium, in order to create a database of pertinent information collected during outside events including:
• Similar Projects
• Similar Technologies
• Articles
• Conferences or Events
o Information of possible End-Users or similar technologies presented
• Contacts
o Collection and distribution between the consortium of contacts of possible End-Users
External Dissemination
The External Dissemination’s main purpose focuses on promoting BONAS in environments external to the consortium. It includes:
• Public Presentation of BONAS to:
o End-Users
o Associations (e.g.: ASD, detection of explosives association, …)
o During events
• Participation in Conferences
Instruments for Dissemination
The aforementioned objectives were achieved using various dissemination instruments including:
• Creation of a website (Internal Dissemination)
o The website included a mailing list and a document repository for the members of the consortium
• Creation of an Advisory Board including end-users and technology experts in order to provide feedback and expert advice to the members of the consortium regarding the requirements throughout the project. (Internal and External Dissemination)
• Disclosure by the means of: (External Dissemination)
o Press releases
o Brochures
o Presentations (creation of a template presentation for all members of the consortium)
o Articles
The following chapters present the details on the dissemination activities and dissemination instruments used for the proliferation of the results obtained from the BONAS project.
BONAS participation in events and press releases
Press Release to the International Science Media
Presentation to possible End-Users in Italy
Video and pre-demonstration event in Pratica di Mare
The BONAS consortium has participated in a pre-demonstration event of the BONAS system at the Pratica di Mare Italian Air Force facilities between 9th and 13th of June 2014.
A video with some highlights of this event was created during the event and made publicly available (also available on the Cordis channel from Youtube). It may be found using the following link:
https://www.youtube.com/watch?v=kTHGB4xhSIc
Security Research Conference and CPExpo
The BONAS project was presented in the Security Research Conference and CPExpo (Community Protection Expo) by Antonio Palucci, Francesco Saverio Romolo, Christophe Marquette, and Peter Höjerback between the 9th and 11th of December 2014 in Genoa, Italy.
The BONAS consortium will have a devoted box for the presentation of the results of the project.
Figure 29 – Genoa Expo 2014
BONAS-EMPHASIS Joint Demonstration event
Both BONAS and the EMPHASIS projects had a joint demonstration event in Stockholm between the 24th and 25th of September, in which on the 24th was presented an experimental demonstration of both systems, where groups of persons (including possible end-users of the systems) have observed the several technologies being developed in the projects operating as a whole system. On the 25th a conference was held, in which were discussed the results from the 1st day of the demonstration event.
Several videos and photographs were made available showing some of the stations presented at the demonstration event.
Reports have been created indicating the results obtained from the demonstration event with some interviews from the persons working on both projects. The reports may be found by accessing the following links:
http://www.bbc.com/news/world-europe-29421952
http://www.bbc.com/news/science-environment-29416842
Recapping the BONAS’s partners have produced:
• 8 publications on peer review
• 28 communications to conferences
• 1 PhD
• 2 media dissemination
• 1 exhibition
• 1 video broadcasting
List of Websites:
The BONAS website is used as an instrument to disseminate the general information about the BONAS project to the public audience. It plays a crucial role in the dissemination of the activities of the project, including the news, the published documents, the public events, the incremental results and so on.
The website was implemented as a Google site (http://sites.google.com). It is available at the url:
https://www.bonas-fp7.eu/
and as mirror to the http://bonas.tekever.com/
The website is being regularly updated to inform about the project latest news.
Regarding the everyday working among the partners, the BONAS consortium is using a FTP based site as file sharing system. This site serves both as a collaborative tool for the partners and as a repository of all the documentation produced during the project history. The repository was only accessible by the members of the consortium through the use of the appropriate credentials.
The discovery of hidden bomb factories is of primary importance in the prevention of terrorist activities. In particular, the first stage of IED preparation will take considerably more time as compared to the successive phases of transport to the target and deployment. Therefore, in the early stages of IED preparation of a terrorist attack, investigations can be conducted with fewer time constraints and with greater accuracy than at later stages in support to intelligence personnel in order to monitor their activities to obtain clues, whether an object might actually be misused as an illicit “bomb factory”.
BONAS project (BOmb factory detection by Networks of Advanced Sensors, FP7-SEC-2010 N° 261685) was proposed to realize a substantial contribution to the “Early warning system concerning explosives”, which is “Priority 1” of the “Prevention measures” in the Action Plan on Enhancing the Security of Explosives.
Presently, there are no specific sensors available for precursor detection, so BONAS technological solutions have been created for this very purpose. BONAS have implemented five technologies to address precursor detection in different states: liquid, particle and vapour. Furthermore, these technologies have been linked in a devoted network for their remote control.
The wireless sensor network feature a variety of orthogonal sensing devices and systems (in‐situ and remote), that jointly provide broad chemical spread and low false alarm rates. In particular, BONAS has developed:
1. Innovative particle and gas samplers.
2. A compact lidar/DIAL (Differential Absorption Lidar) system.
3. A QEPAS (Quartz Enhanced Photo‐Acoustic Spectroscopy) sensor.
4. A SERS (Surface Enhanced Raman Spectroscopy) sensor.
5. An electrochemical sensor.
The network of sensors has already been tested in two field campaigns. The first in June 2014 at the Pratica di Mare (Rome - Italy) Military Airport and in September 2014 at the FOI (Swedish Defence Research Agency) facility near Stockholm, jointly with the EMPHASYS project. The field tests have provided the possibility to monitor different suspicious substances.
The project perfectly meets the request to develop new sensors for precursors not only in the device realization but also in their management with a Command a Control system (C2). The C2 centre includes also an expert system, which processes the data from the sensors in order to evaluate the global alarm level. The C2 system is equipped with a machine/user interface with a easy to use touch screen display. All the modules are managed by a master control board interfacing all the modules and managing power supply.
BONAS has reached its goals in realizing new sensors for a large number of compounds, being tested in two different campaigns. All the devices can be camouflaged while some are handheld. In particular, BOANS has contributes to the introduction to the market of a handheld miniRaman spectrometer.
Project Context and Objectives:
BONAS project, during 45 months of activity, has achieved the all the proposed objectives and Milestons in realizing different sensors including a sampling devices for the wireless sensor network on the detection of traces of precursors used in IEDs (Improvised Explosive Devices) production (particulates, gases and/or waterborne) present in the environment surrounding the vicinity of a “bomb factory”.
The wireless sensor network feature a variety of orthogonal sensing devices and systems (in-situ and remote), that will jointly provide broad chemical spread and low false alarm rates. In particular, BONAS has developed:
Innovative particle and gas samplers (WP3).
A compact lidar/DIAL (DIfferential Absorption Lidar) system (WP4).
A QEPAS (Quartz Enhanced Photo-Acoustic Spectroscopy) sensor (WP5).
A SERS (Surface Enhanced Raman Spectroscopy) sensor (WP6).
An electrochemical sensor (former immunosensor) (WP6).
A Command and Control System
An Expert System
A SAW (Surface Acoustic Waves) sensor (WP5).
The EADS sampling system consists of a sampling cone that can be cooled for condensing and freezing vapour from the ambient air and then heated to release the collected vapour onto the SERS surface positioned below. The SERS surfaces are mounted on a movable stage (Figure 3). The Raman spectrometer is mounted in such a way that the SERS surface with the target substance can be automatically moved to the correct measurement position after sampling.
The Raman spectrometer module starts the sampling sequence when instructed to do so by the higher level control system.
Figure 3 – The sampling unit matched with the miniRaman spectrometer; 1 SERS substrate loading; 2) Sampling and freezing; 3) Drop transfer to SERS surface; - 4) Liquid injection (Ammonia); 5) Heating/evaporation; 6) Raman signal collection
The sensor system consists of a miniaturized Raman spectrometer in combination with a sampling system for collecting particles and/or vapors from the surrounding air. The sampling system collects air through an inlet fan and directs this to a cooled SERS-surface were particles are being trapped onto the surface. The SERS-substrate is then dried by heating and placed in position for measurement by the Raman spectrometer. The resulting spectral information is transferred to the command center for analysis and comparison with a spectral library of known precursors.
An advantage of this sensor is its small size and low power requirements that makes it possible to hide it in various places relatively easily. Very low levels are potentially possible to detect depending on the enhancement factor of the surface and the capabilities of the sampling system. It is also possible to detect several different substances with the same sensor since many of the relevant precursors are Raman active (Figure 4).
The BONAS Surface-Enhanced Raman Spectrometry (SERS) based sensor is used for detecting particles and/or vapors in the surrounding air around a place where bombs are being prepared.
A special nanostructured surface coated with a metal (typically gold or silver) is able to enhance the normally weak Raman signal obtained in regular Raman spectroscopy. This makes it possible to detect very small amounts of a substance present on the surface.
The sensor system consists of a miniaturized Raman spectrometer in combination with a sampling system for collecting particles and/or vapors from the surrounding air. The sampling system collects air through an inlet fan and directs this to a cooled SERS-surface were particles are being trapped onto the surface. The SERS-substrate is then dried by heating and placed in position for measurement by the Raman spectrometer. The resulting spectral information is transferred to the command center for analysis and comparison with a spectral library of known precursors.
An advantage of this sensor is its small size and low power requirements that makes it possible to hide it in various places relatively easily. Very low levels are potentially possible to detect depending on the enhancement factor of the surface and the capabilities of the sampling system. It is also possible to detect several different substances with the same sensor since many of the relevant precursors are Raman active.
Figure 4 - The sampling device integrated with the Serstech sensor as deployed during the FOI campaign
The ENEA Lidar/Dial sensor was developed in collaboration with LDI and CSEM. It was successively tested July 2013 in Tallinn and successively at the ENEA Frascati Centre as described in D4.1 and D4.2. The apparatus installed inside a truck was located in the test area of the PDM airport (Figure 5).
Figure 5 – Lidar sensor installed in its truck inside the testing area.
A detailed view of the sensor installed inside the truck is shown in Figure 6 together with the telescope frame view from inside while pointing the caravan used as bomb factory.
Figure 6 – Inner view of the apparatus (right side) and line of sight (right side)
The optical beam of the lidar was pointed toward the open window of the caravan used as bomb factory, located at a distance of 108 m.
The sensor was operation in DOAS mode (DIfferential Optical Absorption Spectroscopy), with a resolution time (range): 8ns (1.2m) number of samples: 1024, number of laser pulses: 10, over an average of 30 measurements. The beam size at lidar was about 25 mm, while the beam size at front of caravan was about 300 mm, with a divergence: about 3 mrad (this value fits well with telescope field of view = 1/363.5 = 2.75 mrad).
While virtues of a lidar/DIAL for stand-off detection of IED precursors include its mobile use from a minivan and its range-resolved medium distance mapping capabilities in a few minutes without raising much suspicion, operating a lidar from an airborne platform offers wide areas coverage capability, increasing the BONAS network detection capability. Therefore, an airborne DIAL/lidar would work inconspicuously as an upper-layer tool in the sensor network, to find a large area that has to be more thoroughly looked at by mid-layer sensors such as ground lidar/DIAL. Data achieved with the lidar have been used for the design of an airborne version of the system (Figure 7).
Figure 7 – Design of the integrated optical device in the ONERA fling platform.
The realisation of an airborne DOAS, based on QCLs, is feasible in a long-path DOAS at nadir or closed to it, where the path is near the terrain. In such solution, a number of lasers may be used, simultaneously operating, each fixed on one wavelength «on» or «off» of either the acetone or some of the impurity gases (here H2O and CH4 have been considered).
A SAW (Surface Acoustic Waves) sensor was already developed by CEA and joined BONAS to exploit the possibility of field tests in a network of sensors. Successively, CEA has integrated this sensor in a portable device to detect and identify explosive vapors based on 14 sensors (2 Quartz Crystal Microbalance sensors, 8 surface acoustic waves sensors and 4 fluorescent sensors). The three different technologies were successfully integrated in a multi-sensor chamber. The sensor is autonomous, stand-alone operated and with Wireless connectivity. A specific data processing was developed in order to detect and identify the main explosive threats with a good probability. The new device, named T-REX , is in the process of industrialisation (Figure 8).
Figure 8 – The new device
A point sensor for the detection and identification of IED’s precursors in the vapour phase has been developed in the frame of the BONAS project. Most of the work was focused on sensors based on Quartz Enhanced Photo Acoustic Spectroscopy (QEPAS) that is one of the many forms of InfraRed Absorption Spectroscopy, particularly interesting for the extremely small size and reduced costs of its analysis cell.
BONAS has realized one of the first sensors that exploit the combination of external cavity quantum cascade lasers (EC-QCLs) and QEPAS for trace detection of IEDs, and to achieve both high sensitivity, and selectivity towards a wide spread of chemicals. Also, it is the first QEPAS sensor that integrates a Vapour Phase Concentration Unit (VPC), with advanced methods and devices specifically designed to sample and pre-concentrate IED substances and precursors.
A laser beam is focused between the two prongs of a quartz tuning fork (QTF), and pulsed at the resonant frequency of the QTF. When the small air volume between the prongs contains a gas that absorbs the laser radiation, acoustic waves are generated that induce the oscillation of the tuning fork. The QTF converts these mechanical oscillations into an electrical output signal that can be demodulated by a lock-in amplifier system. The demodulated signal is proportional to the concentration of the gas, its absorption coefficient, the optical power of the laser, the quality factor of the oscillator, and inversely proportional to the resonant frequency. Absorption spectra can be recorded by scanning the laser wavelength.
Main features,
Integrated sampling, pre-concentration and sensing.
Compactness and portability.
Fully battery operated.
Easily camouflaged.
Stand alone operation and remote control.
Detection and Identification capability against a wide family of VOCs, including: IED’s precursors, CWAs and TICs.
The system has been successfully tested in the lab with several target compounds and interferents, and also in two field campaigns, (Pratica di Mare, Grindsjön), showing its capability to detect vapours of suspicious substances escaping from simulated bomb factories.
Field tests have been carried out at the military airport of PDM to detect IED’s precursors in their vapour phase as they are produced by simulating the production or producing IE inside the ENEA caravan with at least one open window. In order to use realistic amounts of substances, and since the QEPAS sensor detects only gas/vapours, controlled amounts of liquids were previously poured into a glass pot inside the van (according to Deliverable 2.3) then the resulting vapours were sampled from outside in open air. The tests have been carried out with the QEPAS sensor hidden inside a dumpster for camouflage (Figure 9), autonomously operating with rechargeable batteries.
Figure 9 - The QEPAS sensor inside the dumpster used for camouflage
Before the trials, the sensor was set and prepared in the laboratory where refrigerating blocks have been introduced aside (Figure 10 right side) and the battery recharged (Figure 10 left side).
Figure 10 – The QEPAS sensor in the laboratory before the outside installation while battery are recharged (left side) and with the refrigerating devices (right side).
During the outside trials, the dumpster was positioned at defined distance from the van and properly set to collect, sample, analyse them and transfer data (Figure 11).
Figure 11 – The QEPAS sensor close to the caravan ready to operate.
Three compounds included in the priority list for BONAS have been tested with the QEPAS demonstrator, namely: B10, B02 and Int10. In Table 1 a summary list of the experimental tests carried out is reported. For each test: the name of the compound (Compound); The initial state of the compound to be sampled (Initial State); the amount of compound used for the test (Amount); the method used to sample the compound (Sampling) and the stand alone operating conditions of the sensor (Operating Conditions) are reported.
Table 1 - List of tested compounds
Compound Initial State Amount Sampling Operating conditions
B10 liquid 100-500 ml open air by means of a mini pump battery operated
remote data transfer
B02 liquid 100-500 ml open air by means of a mini pump battery operated
remote data transfer
Int10 liquid 200 ml open air by means of a mini pump battery operated
remote data transfer
The QEPAS sensor has demonstrated good detection and identification capability towards B10 and B02. But, as expected, the capability to detect precursors proved to be strongly dependent on the wind strength and direction. The sensor should be positioned downwind to maximize its detection capability. Some issues emerged during the tests, due to the harsh environmental conditions at PDM in June (temperature of about 30°C and strong solar irradiation). Efficient cooling of the sensor inside the dumpster was found necessary to avoid failure of the laser head.
Electrochemical Sensor
For the BONAS project, the UCBL team is developing an electrochemical sensor which will be dedicated to the achievement of simultaneous monitoring of different explosive precursors. With this new sensor, Hexamine, H2O2, NH4NO3 and NaOCl could be detected, by cyclic voltammetry measurement on a homemade chip.
The BONAS electrochemical (EC) sensor uses electrodes to oxidize or reduce molecules soluble in water. The BONAS EC sensor uses four types of electrodes to generate EC signatures, one carbon electrode and three electrodes covered by different noble metals. These signatures are then compared to an EC database generated during the project by analyzing target compounds in water, soapy water and artificial waste water in both laboratory and in-field conditions. As this system aims to contribute to the discovery of bomb factories by the detection of precursors in wastewater, we focused on soluble precursors that could exist in sewage water from improvised explosive (IE) preparation and were included in the BONAS Project Advisory Board’s list of the most interesting targets, based on the current trends of terrorism. The EC sensor was particularly effective in detecting three precursors. Advantages of the system include portability, easily hidden in the sewage system, battery stand-alone operability, and wireless operability. The system has been successfully tested in two field campaigns (Pratica di Mare and Grindsjön (Figure 12)), showing its capability to detect target precursors directly in sewage water drains from simulated bomb factories (Figure 13).
Figure 12 - BONAS EC sensor during the joint demo at FOI Grindsjön
Figure 13 - Operation principle of the BONAS EC sensor. In order to take full advantage of the information provided by the sensor, a unique signature is created for each sample by merging the signals given by the individual surface electrodes as a sequence. For any new acquired measurement, a predicted probability value of the presence of a target is obtained by applying classification models based on a pattern recognition technique.
System architecture - On-board control & processing system
BONAS has developed a novel wireless sensor network able to provide semi-permanent detection capabilities. The BONAS system architecture comprises various individual components that are present in every network node, such as sensors, sampling systems, processing units, communication devices, connectors, etc. Each network node will essentially contain three different subsystems: sensor, control and processing system and network:
Sensors – The BONAS system integrates a variety of different types of technologies and therefore there is a difference in performance achievement by each of the different component. Nonetheless, all components must guarantee the correct interoperability with each other and assure the overall quality of the functionalities of the system.
Control and Processing System – This subsystem is basically composed of a computer with input/output bus (to receive and send data from and to the sensors), local storage (in order to store relevant information in case of temporary miscommunication or to establish patterns avoiding false alarms), central processing unit (processing unit that controls and drives the communications and memory hardware), ad-hoc communication capabilities (antennas and SDR-based (Software Defined Radio) Front-End that allow each network node to forward and receive information from the BONAS central unit), human-machine interface – HMI (this component is only present in the central unit in order to provide the operator with some system interaction and relevant visual data), etc.
The Network represents the virtual environment that each BONAS node will use in order to communicate with each other (including the central unit).
The overall BONAS system architecture can be seen in Figure 14. This architecture structures and decomposes the BONAS problem into several aspects which must be properly addressed, mainly focusing on the On-Board Processing System (including Input/Output aspects) and Network. This architecture, however, requires that sensors and sampling systems provide digital Input/Output interfaces to the On-Board Processing System (specific connectors and communications protocols will be detailed during the requirements phase). Moreover, a mobile BONAS central unit will be developed too, which means that a slightly different on-board processing system must be designed.
Figure 14 – The BONAS system architecture
In detail, the on-board control & processing system basic functions required:
On-board processing
Communication
Human-machine interface
Power management
Data storage
Communication devices
Communication interfaces are provided by the Wireless Adaptive Communication (WAC) nodes (Figure 15). The summary description of each communication interface is listed next:
GPS – receive location information;
USB interface – connect to sensor control devices;
Ethernet interface – connect to sensor control devices or server computers or networks;
2.4GHz wireless interface – connect to wireless sensor network.
Figure 15 – Communication interfaces
For each particular sensor, a specific physical integration was considered. For some of the BONAS sensors, it was used USB interface and, for others, it was used Ethernet interface. The physical integration involved several network communication nodes that were connected to sensors controller devices, see Figure 16. Basically, the communication nodes should collect and forward sensor data wirelessly up to the command and control centre.
Figure 16 – Communication nodes and sensor controller physical interface
Human Machine Interface
The HMI of the system has been designed to be user-friendly and intuitive for any user who shall employ it. The intuitiveness of the system will decrease the amount of time required to train a person to use the system and therefore increase the performance of the system.
Physically, the C2 centre is connected to the BONAS sensor network through an Ethernet interface. A communication network node connected to the C2 centre server is used to forward data from sensors to the C2 centre and vice-versa.
The HIM allows to observe on Google Maps view, the sensor’s displacement on the area under investigation (Figure 17) and their status of connectivity.
Figure 17 – Panel of the sensor’s displacement on the area investigated.
Figure 18 - Sensor’s control panel.
Figure 19 – Status of the different sensors
In the successive figures are displayed the data transferred (Figure 18) and the row data collected (Figure 19). The final panel release the integrated information for the end-user with the help of the expert system (described below).
Cryptography is one of the utilized tools introduced in BONAS to keep information confidential and to ensure its integrity and authenticity. The approach implemented includes the symmetric key AES, Rijndael, RC5, Twofish, Triple DES, Serpent among others as well as the widely used RSA public key algorithm.
The cryptography algorithm must be applied to secure network traffic. One of the most important protocols for communication security is the IPsec (IP security). With this type of security it is possible to send the bits composing the payload secretly and without modification from source to destination keeping unwanted bits out of the message.
Whereas the BONAS system incorporates both wired and wireless sensor devices, wireless security measure has been taken into account. The 802.11 standard prescribes a data link-level security protocol called WEP (Wired Equivalent Privacy), which is designed for the security of wireless connections. When this type of security is enabled, each station possesses a secret key shared with the base station, in the case of the BONAS system, each of the BONAS sensors would have a secret key shared with the command center. In this type of security, the only issue that remains outside the security protocol is how the shared key is indeed shared.
EXPERT SYSTEM
The BONAS expert system performs pattern recognition of the data collected by each network sensor, estimating the predicted probability of detection for each target. The system then integrates the responses from each sensor to provide the end-user with a global alarm level, summarizing information from the entire network into a unique evaluation of the possible criminal threat of an improvised explosives (IE) production site.
The expert system follows a three step workflow while providing a real time response for any new measurement:
The first step is estimating the predicted probability of the presence of each target for each sensor included in the BONAS network. The classification models adopted have been computed by means of a pattern recognition technique on a dataset of experimental data acquired from laboratory and field conditions. These classification models are then applied to any new data received to obtain the corresponding predicted probability value.
The expert system integrates the output given from multiple sensors measuring the same target to provide the user with a unique alarm value for each compound. In this context, sensor location is also considered and only output from sensors located within a user-defined distance range are integrated.
At the last step, the output from the previous steps is further integrated to provide the user with a defined global alarm. The initiated alarm considers the number of detected targets as well as the simultaneous detection of specific targets known to be used together to prepare a specific IE. The alarm value is thus increased when specific couples of targets defined by the user are simultaneously detected. Again, only output from sensors located in the user-defined distance range is considered.
A compact representation of the information provided by the expert system is shown in the dedicated user interface developed in collaboration with TEKEVER. A schematic representation of the three step approach developed for the BONAS expert system is shown in Figure 20.
Figure 20: Schematic representation of the three step approach developed for the BONAS expert system. The alarms due to individual targets are provided as output of Step 2 while the global alarm level is obtained as output of Step 3.
During both test campaigns, the command and control (C2) centre has shown its ability to communicate with all the sensors included in the network and to process, by means of the expert system, the data received. The live alarm monitor end‐user application developed by Tekever allowed a real-time monitoring of the information provided by the expert system. In fact, the user interface reports:
on the left, the alarm obtained for each target substance considering the information of all the sensors able to monitor it (step 2 output);
in the middle, the global alarm which summarizes the information from the entire network into a unique evaluation of the possible criminal threat of an IE production site (step 3 output).
on the right, a map showing the location of the different sensors is also reported. To indicate sensors’ position, markers colored according to the color code of Step 2 are used in order to show a possible positive detection of the sensor. Furthermore, a circle with a radius equal to value maximum distance threshold for data integration is also reported for each sensor. In this way, it is also possible to monitor the data of which sensors are being integrated.
Figure 21: Example of user interface obtained during the test campaign at FOI which shows the positive detections for three targets obtained by two sensors.
An example of user interface obtained during the test campaign at FOI is shown in Figure 21. It can be noticed that, as in the previous cases, three target substances are being detected by two sensors. However, since target B02 and target B15 are one of the couples of targets known to be used together in the preparation of a specific IE, a much higher global alarm level (equal to 60) is obtained in this case as output of Step 3.
The validation and performance assessment of the expert system developed for the project “BOmb factory detection by Networks of Advanced Sensors” (BONAS) has been successfully carried out, according to Task 8.5 and 8.6. Particular attention was paid to evaluate the classification models in terms of false positive and false negative results in order to optimize the performances of the expert system. Two thresholds were considered for each model on the basis of the ROC curve, one to maximize classification efficiency and one that allows to minimize false positive results.
The performance of the developed sensor network were evaluated under relevant environmental conditions of the scenarios from WP2, that is in the presence of interferents and pollutants in both the air and the water, in the Italian Air Force base in Pratica di Mare (Italy) and in Swedish Defence Research Agency (FOI) facility in Grindsjön (Sweden).
The BONAS expert system performed pattern recognition of the data collected by the network of sensors tested, estimating the predicted probability of detection for each target, and then integrating the responses from each sensor. The final aim was to provide the end-user with a global alarm level, summarizing information from the entire network into a unique evaluation of the possible criminal threat of an improvised explosives (IE) production site.
The QEPAS sensor was placed inside a dumpster used for camouflage and used to analyse vapours of IE precursors generated during an IE production (or the simulation of an IE production). The two target substances B02 and B10 included in the priority list for BONAS were tested, resulting in positive detections obtained in all the four days of tests in Pratica di Mare and positive detections obtained during all the tests carried out during the demo at FOI without any false positive detections.
The electrochemical (EC) sensor was successfully tested for main targets such as B08 and target B15, the latter both in pure form and in two commercial products containing target B15 and target B14. During all the experiments performed on field in Pratica di Mare, the EC sensor connected to the sink configuration was able to successfully acquire data and to communicate with both the network and the expert system and all the solutions obtained from the performed experiments were later analysed by ion chromatography in order to obtain the targets’ concentration.
The tests performed during the demo at FOI confirmed the capability of the SERS sensor, hidden in a toolbox, in sampling particles of target material from the atmosphere and successfully acquire spectra.
An extensive validation of the capability of the expert system to effectively integrate the information provided by the different sensors has been also carried out during the two test campaigns at the Italian Air Force base and at the Swedish Defence Research Agency. The end-users were effectively provided with a global alarm level, summarizing information from the entire network (both BONAS and EMPHASIS networks of sensors) into a unique evaluation of the possible criminal threat of an improvised explosives (IE) production site.
Investigation on new Nanostructured supports
The objective of the work was to design metallic nanostructures to provide a strong electromagnetic field enhancement in a spectral range required to address the wavelength of the Raman spectrum of the explosive precursors. This is due to the strong dependence of the Raman signal on the local electric field strength, leading to the significantly enhanced signal of surface enhanced Raman scattering (SERS) allowing to improve sensitivity of the molecular detection. While SERS signal can be obtained from a single molecule in a laboratory environment, this requires state of the art spectrometry with well controlled polarization, collimation and angle of incidence to make full use of resonant electromagnetic field enhancement on the plasmonic substrate, which is very sensitive to these conditions. The objective of this work was to design a robust and cost-effective SERS substrate which can be employed in the project scenarios with the determined sample collection approach. This involves 1) relaxed requirements on angle of incidence, so that there is no need to for collimated illumination but instead strongly focused illumination can be effectively used, 2) relaxed requirements on the polarisation of the excitation, so that there is no need to control polarization of the excitation light, 3) despite the above, to provide strong wavelength resonant enhancement at the required excitation and scattering wavelength, and 4) take into account the working conditions with respect to the decided sample collection scenario.
The objectives have been achieved using modified surface plasmon polaritonic crystals on specifically roughened Au films. The adopted approach provide a practical solution to the SERS detection in the scenarios of the project providing: 1) environmentally stable SERS substrates since Au films are used; 2) the substrates capable to withstand the laser powers needed for the operation of the Raman spectrometer without damage and deterioration; 3) the substrates compatible with the decided sample collection scenario; and 4) the substrates providing the sensitivity of SERS detection superior to the commercially available (e.g. Klarite © substarte) in the decided sample collection scenario.
The design was based on a controllably roughened Au films with the overlayered periodic patterns (the so-called plasmonic crystals) to provide “photonic-crystal”-like structure for surface plasmon waves. The roughness is used for generation of plasmonic hotspots in the nanoscale morphology of metallic surfaces. To further enhance the electric field in these hotspots, the plasmonic crystals can be made to provide additional pathway through which light can interact with the surface by resonantly coupling the incident light to the plasmonic excitations. These crystals have many geometrical parameters that can be tailored for the BONAS applications. The sample collection scenario constrains mean that the self-organisation process of the formation of nanocrystalites of target molecules after the drop casting of analyte solution from the sample collection apparatus will also benefit the overall enhancement. The resulting surface topologies and plasmonic crystals and their performances were presented in Deliverable D6.4.
Taking into account the specifications for the SERS substrates required in the framework of this project: robustness, weak dependence on the illumination conditions and compatibility with the selected sample collection scenario, it was shown that plasmonic crystal substrates based on roughened gold provide the required SERS detection parameters for RDX detection with a 100 fg limit. Moreover, they are thermally stable against extended exposure the high laser powers used and outperformed a commercial SERS substrate (Klarite) for the detection of RDX in the required scenarios of the sample collection.
SERS sensing technique is ubiquitous in many applications where chemical specificity and high sensitivity. Thus, the developed approach to SERS substrate design and tailoring can be used to address other sensing scenarios in environmental, chemical and biological sciences. While the design parameter can be readily tuned to address one or another substance in question, the specific attention in future applications should be paid to the sample collection approach which is different in different applications and can affect the overall performance of the nanostructure.
SERS activity: QUB substrates
Under this deliverable the QUB team investigated a number of substrates for surface enhanced Raman spectroscopy (SERS) with the purpose of deployment in the condenser-sampler system developed at EADS which incorporates the Serstech mini-Raman spectrometer. Three types of substrate were examined or used to a large extent during the course of the project (see Figure 22):-
KlariteTM (discontinued March 2014) – this was a commercial substrate from Renishaw which was based on inverted pyramidal structures etched in silicon and coated with a proprietary Au deposit (Figure 22 (a)).
Gold nanowires (NWs), based on the electrochemical deposition of Au in a porous alumina template where the host alumina matrix was generally subsequently stripped, leaving a free-standing array of Au NWs (Figure 22 (b) and (c)-(f)). A variation in the fabrication procedure utilising a sacrificial polypyrrole core in the alumina pores yielded Au nanotubes as shown in Figure 22 (g).
Gold coated arrays of nanodomes (NDs) as shown in Figure 1(h) and (i). The substrates were formed by a nano-imprint lithography process (R. Winfield, Tyndall National Lab., Ireland under NAP project 376) and subsequently Au-coated at QUB.
The polymer base-material in which the ND arrays were formed was found to be very prone to thermal damage on exposure to the type of high power laser (>150 mW) incorporated in the Serstech system. These substrates were therefore not pursued any further in the context of BONAS. The gold NWs (and nanotubes) served as a platform for rigorous analysis of the SERS performance in terms of 3 different plasmon modes supported in the system (Figure 22 (d)-(f)). The modes involved are a transverse mode (a surface plasmon resonance across the diameter of the free-standing NWs), a longitudinal mode (along the length of the NWs), and a cavity mode, supported only between two adjacent NWs that are in very close proximity (< 10 nm) along their length. The detail of the interplay between these modes and their influence on the Raman response is discussed in detail in ref [1] and need not be developed here. The key point is that the principle of operation depends on the extreme concentration (by the nanostructured surface) of electromagnetic energy into highly confined volumes to exploit the approximate fourth-power dependence on the magnitude of the incident electric field, |E|4. Thus a molecule placed in a region with a 10x field enhancement (relative to the free-space background for the same laser input) will yield a Raman signal that is amplified by 104 relative to that which it
Figure 22 Various SERS substrates trialled during BONAS (a) SEM image of KlariteTM substrate showing inverted square pyramid structures in silicon with magnified detail showing structured, wavy gold deposit within the pyramid structures (Image from H. Wackerbarth et al., Applied Optics, 49, 4362 (2010)). (b) Au NWs grown at QUB in porous alumina template (scale bar 100 nm) with (c) schematic illustration of structure showing (d) transverse, (e) longitudinal and (f) cavity plasmon modes supported in the system. (g) Au nanotube variant of NW substrate (scale bar 100 nm). (h) Top-down view and (i) 45o view of Au ND substrate (scale bars are 100 and 200 nm in (h) and (i) respectively).
would yield in bulk solution. As it turned out the enhancement factor (EF) for the NW substrates was relatively modest, mostly in the 103 range, but they proved extremely useful for physical analysis and understanding. In addition, unlike the nanodome substrates, they proved reasonably robust under laser radiation.
For much of the experimental work we used Klarite on account of the good uniformity across a given substrate and reproducibility from substrate to substrate and batch to batch. We considered these features to be of primary importance in making comparative tests of a range of samples over longer periods of time. Also, while the EF was not outstanding (up to 104 at best), they proved extremely robust thermally. These substrates formed the basis for our work on the novel detection of very dilute acids by means of derivatization – this has recently been reported in Analytical Chemistry[2] where, again, BONAS was clearly acknowledged. Some of the common acids, such as nitric and sulphuric acid can be created as effluent of bomb making either directly or via the production and subsequent interaction of NO2 or SO2 gases with water vapour. The weak acid solutions in atmosphere cannot normally be detected by Raman spectroscopy or even SERS. In SERS the condensation of a weak acid on the substrate is, in itself, ineffective since only a tiny fraction of the target molecular species exists at surface sites of significant field intensity that drive the enhancement. Allowing a droplet of weak acid solution to dry on the SERS substrate is also ineffective since the target analyte evaporates contiguously with the water. The key to solving this detection problem is to form a derivative (salt in this case) of the analyte species (the relevant 〖"NO" 〗_3^- or 〖"SO" 〗_4^(2-)anion) through reaction with excess ammonium hydroxide. In this scenario the derivatized material, NH4NO3 or (NH4)2SO4, is deposited directly on the enhancing surface as the excess water evaporates, yielding significant signal enhancement; the reaction scheme for the case of nitric acid, for example, is:-
HNO3 (aq) + NH4OH(aq) → NH4NO3 (aq) + H2O → NH4NO3 (s) (1)
A depiction of the derivatization scheme, with relevant SERS spectra (comparing spectra from acid solution droplets with those from the derivative species) is shown in Figure 23. This figure illustrates is an improvement of the derivatization methodology over detection in solution by a factor of more than 1500, while the low-concentration detection threshold (~100 ppb) is ~ 4 orders of magnitude times lower compared with liquid phase detection. These findings represent very significant gains.
Figure 23. (a) Sketch illustrating the derivatization drop-casting technique where contaminant in solution forms salt and is then allowed to air dry; magnified 3D schematic of SERS surface illustrates higher concentration of derivative molecules very close to the surface after drying. (b) and (c) show SERS spectra from nitric acid and sulphuric acid solutions respectively:- 5% (black), 1% (blue) concentration liquid solutions on SERS surface before evaporation and 100 ppm (red) solution derivatized using ammonium hydroxide and allowed to dry as described in the text. Each set of spectra is plotted on same Raman intensity scale with individual spectra offset for clarity.
Prospects
While the derivatization technique successfully addressed deliverable 6.3 of BONAS, we consider that it could significantly extend the applicability of the SERS technique in general. The next step would be to demonstrate the technique’s efficacy in relation to the detection of other trace species in solution. The second advance that is required - perhaps surprisingly, after all the 40 years of the existence of SERS as a technique – is the fabrication of a SERS substrate that (i) displays a high EF (ideally >106) and is (ii) reproducible, (iii) uniform across its surface, (iv) thermally robust and (v) cheap. While Klarite scored highly on (ii) to (iv), the EF was relatively modest and it was always way too expensive (by at least a factor of 10) for routine application in chemical lab environments. As noted above, Renishaw withdrew this product earlier in the year. Replacement commercial substrates which are much cheaper are so because they are based on replication processes resulting in polymer or plastic substrates which makes them very prone to thermal damage from the input laser required from Raman spectroscopy. The production of a SERS substrate with all the properties (i) to (v) is a priority. The generally perceived challenge inherent in (i) to (iii) is the production of uniform nanoscale features on the surface. However, our efforts on the Au nanodome structures (Figure 22 (h), (i)) represent an attempt to depart from this philosophy by producing the necessary enhanced field regions through the excitation of a Fano resonance on a microstructured (rather than nanostructured) surface, thus considerably relaxing the spatial resolution requirements of the fabrication process. Physically, our initial results show that this process is at least partially successful and would be worth pursuing; the main drawback is that the current substrates are prone to thermal damage, but this is a materials problem that could be addressed.
In an additional, initial line of experiments we have recently been producing our own silicon pyramid structures through the well-establish KOH anisotropic etching route (Figure 24). These direct ‘Klarite-replacement’ style substrates could also be configured for exploiting Fano resonance in SERS and offer a line of investigation which we are keen to pursue in the future.
Figure 24 - (a) Array of inverted pyramidal pits in Si(100) surface, produced by anisotropic KOH etch and subsequent coating with Au. The windows in the photoresist defining the etch regions were circular, but the etch pits are inherently square in (horizontal) section, hence the shadowing of the Au deposit at the corners. (b) ‘Template stripped’ single Au pyramid which has been extracted from the Si substrate by deposition of resin and subsequent lift-out.
Immunosensor
For the BONAS project, the UCBL team is developing an immunosensor which will be dedicated to the achievement of simultaneous monitoring of different explosives. With this new sensor, RDX and TNT could be detected. The test selectivity is proven by simultaneous detection of the two explosives with different possible cross-reactants.
The UCBL team has selected different targets and associated antibodies from commercial sources. Two explosive compounds were chosen according to their importance in bomb fabrication: TNT and RDX were purchased from LGC Standards, France. The associated antibodies, anti-TNT and anti-RDX were supplied by Strategic Diagnostics, USA. In order to test their selectivity (cross-reactivity), pollutants from different sources (3 pesticides, 1 toxin and 1 control molecule) which can be found in wastewater were also selected: 2-chloro-4-ethylamino-6-isopropylamino-1,3,5-triazine (atrazine), 2,4-dichlorophenoxyacetic acid (2,4-D), 2,4,5-trichlorophenoxyacetic acid (2,4,5-T), 4-benzoylbenzoic acid (4-BBA) and okadaic acid from Prorocentrumconcavum were obtained from Sigma-Aldrich, France
Figure 25 - The competitive multiplex immunoassay set-up.(a) Composition of the different matrices used. (b) Principle of the adhesive microarray assembly with the bottomless plate
Project Results:
BONAS project, during 45 months of activity, has achieved the all the proposed objectives and Milestons in realizing different sensors including a sampling devices for the wireless sensor network on the detection of traces of precursors used in IEDs (Improvised Explosive Devices) production (particulates, gases and/or waterborne) present in the environment surrounding the vicinity of a “bomb factory”.
The wireless sensor network feature a variety of orthogonal sensing devices and systems (in-situ and remote), that will jointly provide broad chemical spread and low false alarm rates. In particular, BONAS has developed:
Innovative particle and gas samplers (WP3).
A compact lidar/DIAL (DIfferential Absorption Lidar) system (WP4).
A QEPAS (Quartz Enhanced Photo-Acoustic Spectroscopy) sensor (WP5).
A SERS (Surface Enhanced Raman Spectroscopy) sensor (WP6).
An electrochemical sensor (former immunosensor) (WP6).
A Command and Control System
An Expert System
A SAW (Surface Acoustic Waves) sensor (WP5).
The EADS sampling system consists of a sampling cone that can be cooled for condensing and freezing vapour from the ambient air and then heated to release the collected vapour onto the SERS surface positioned below. The SERS surfaces are mounted on a movable stage (Figure 3). The Raman spectrometer is mounted in such a way that the SERS surface with the target substance can be automatically moved to the correct measurement position after sampling.
The Raman spectrometer module starts the sampling sequence when instructed to do so by the higher level control system.
Figure 3 – The sampling unit matched with the miniRaman spectrometer; 1 SERS substrate loading; 2) Sampling and freezing; 3) Drop transfer to SERS surface; - 4) Liquid injection (Ammonia); 5) Heating/evaporation; 6) Raman signal collection
The sensor system consists of a miniaturized Raman spectrometer in combination with a sampling system for collecting particles and/or vapors from the surrounding air. The sampling system collects air through an inlet fan and directs this to a cooled SERS-surface were particles are being trapped onto the surface. The SERS-substrate is then dried by heating and placed in position for measurement by the Raman spectrometer. The resulting spectral information is transferred to the command center for analysis and comparison with a spectral library of known precursors.
An advantage of this sensor is its small size and low power requirements that makes it possible to hide it in various places relatively easily. Very low levels are potentially possible to detect depending on the enhancement factor of the surface and the capabilities of the sampling system. It is also possible to detect several different substances with the same sensor since many of the relevant precursors are Raman active.
The BONAS Surface-Enhanced Raman Spectrometry (SERS) based sensor is used for detecting particles and/or vapors in the surrounding air around a place where bombs are being prepared.
A special nanostructured surface coated with a metal (typically gold or silver) is able to enhance the normally weak Raman signal obtained in regular Raman spectroscopy. This makes it possible to detect very small amounts of a substance present on the surface.
The sensor system consists of a miniaturized Raman spectrometer in combination with a sampling system for collecting particles and/or vapors from the surrounding air. The sampling system collects air through an inlet fan and directs this to a cooled SERS-surface were particles are being trapped onto the surface. The SERS-substrate is then dried by heating and placed in position for measurement by the Raman spectrometer. The resulting spectral information is transferred to the command center for analysis and comparison with a spectral library of known precursors.
An advantage of this sensor is its small size and low power requirements that makes it possible to hide it in various places relatively easily. Very low levels are potentially possible to detect depending on the enhancement factor of the surface and the capabilities of the sampling system. It is also possible to detect several different substances with the same sensor since many of the relevant precursors are Raman active.
Figure 4 - The sampling device integrated with the Serstech sensor as deployed during the FOI campaign
The ENEA Lidar/Dial sensor was developed in collaboration with LDI and CSEM. It was successively tested July 2013 in Tallinn and successively at the ENEA Frascati Centre as described in D4.1 and D4.2. The apparatus installed inside a truck was located in the test area of the PDM airport (Figure 6).
Figure 5 – Lidar sensor installed in its truck inside the testing area.
A detailed view of the sensor installed inside the truck is shown in Figure 7 together with the telescope frame view from inside while pointing the caravan used as bomb factory.
Figure 6 – Inner view of the apparatus (right side) and line of sight (right side)
The optical beam of the lidar was pointed toward the open window of the caravan used as bomb factory, located at a distance of 108 m.
The sensor was operation in DOAS mode (DIfferential Optical Absorption Spectroscopy), with a resolution time (range): 8ns (1.2m) number of samples: 1024, number of laser pulses: 10, over an average of 30 measurements. The beam size at lidar was about 25 mm, while the beam size at front of caravan was about 300 mm, with a divergence: about 3 mrad (this value fits well with telescope field of view = 1/363.5 = 2.75 mrad).
While virtues of a lidar/DIAL for stand-off detection of IED precursors include its mobile use from a minivan and its range-resolved medium distance mapping capabilities in a few minutes without raising much suspicion, operating a lidar from an airborne platform offers wide areas coverage capability, increasing the BONAS network detection capability. Therefore, an airborne DIAL/lidar would work inconspicuously as an upper-layer tool in the sensor network, to find a large area that has to be more thoroughly looked at by mid-layer sensors such as ground lidar/DIAL. Data achieved with the lidar have been used for the design of an airborne version of the system.
Figure 7 – Design of the integrated optical device in the ONERA fling platform.
The realisation of an airborne DOAS, based on QCLs, is feasible in a long-path DOAS at nadir or closed to it, where the path is near the terrain. In such solution, a number of lasers may be used, simultaneously operating, each fixed on one wavelength «on» or «off» of either the acetone or some of the impurity gases (here H2O and CH4 have been considered).
A SAW (Surface Acoustic Waves) sensor was already developed by CEA and joined BONAS to exploit the possibility of field tests in a network of sensors. Successively, CEA has integrated this sensor in a portable device to detect and identify explosive vapors based on 14 sensors (2 Quartz Crystal Microbalance sensors, 8 surface acoustic waves sensors and 4 fluorescent sensors). The three different technologies were successfully integrated in a multi-sensor chamber. The sensor is autonomous, stand-alone operated and with Wireless connectivity. A specific data processing was developed in order to detect and identify the main explosive threats with a good probability. The new device, named T-REX , is in the process of industrialisation (Figure 9).
Figure 8 – The new device
A point sensor for the detection and identification of IED’s precursors in the vapour phase has been developed in the frame of the BONAS project. Most of the work was focused on sensors based on Quartz Enhanced Photo Acoustic Spectroscopy (QEPAS) that is one of the many forms of InfraRed Absorption Spectroscopy, particularly interesting for the extremely small size and reduced costs of its analysis cell.
BONAS has realized one of the first sensors that exploit the combination of external cavity quantum cascade lasers (EC-QCLs) and QEPAS for trace detection of IEDs, and to achieve both high sensitivity, and selectivity towards a wide spread of chemicals. Also, it is the first QEPAS sensor that integrates a Vapour Phase Concentration Unit (VPC), with advanced methods and devices specifically designed to sample and pre-concentrate IED substances and precursors.
A laser beam is focused between the two prongs of a quartz tuning fork (QTF), and pulsed at the resonant frequency of the QTF. When the small air volume between the prongs contains a gas that absorbs the laser radiation, acoustic waves are generated that induce the oscillation of the tuning fork. The QTF converts these mechanical oscillations into an electrical output signal that can be demodulated by a lock-in amplifier system. The demodulated signal is proportional to the concentration of the gas, its absorption coefficient, the optical power of the laser, the quality factor of the oscillator, and inversely proportional to the resonant frequency. Absorption spectra can be recorded by scanning the laser wavelength.
Main features,
Integrated sampling, pre-concentration and sensing.
Compactness and portability.
Fully battery operated.
Easily camouflaged.
Stand alone operation and remote control.
Detection and Identification capability against a wide family of VOCs, including: IED’s precursors, CWAs and TICs.
The system has been successfully tested in the lab with several target compounds and interferents, and also in two field campaigns, (Pratica di Mare, Grindsjön), showing its capability to detect vapours of suspicious substances escaping from simulated bomb factories.
Field tests have been carried out at the military airport of PDM to detect IED’s precursors in their vapour phase as they are produced by simulating the production or producing IE inside the ENEA caravan with at least one open window. In order to use realistic amounts of substances, and since the QEPAS sensor detects only gas/vapours, controlled amounts of liquids were previously poured into a glass pot inside the van (according to Deliverable 2.3) then the resulting vapours were sampled from outside in open air. The tests have been carried out with the QEPAS sensor hidden inside a dumpster for camouflage (Figure 3), autonomously operating with rechargeable batteries.
Figure 9 - The QEPAS sensor inside the dumpster used for camouflage
Before the trials, the sensor was set and prepared in the laboratory where refrigerating blocks have been introduced aside (Figure 4 right side) and the battery recharged (Figure 4 left side).
Figure 10 – The QEPAS sensor in the laboratory before the outside installation while battery are recharged (left side) and with the refrigerating devices (right side).
During the outside trials, the dumpster was positioned at defined distance from the van and properly set to collect, sample, analyse them and transfer data (Figure 5).
Figure 11 – The QEPAS sensor close to the caravan ready to operate.
Three compounds included in the priority list for BONAS have been tested with the QEPAS demonstrator, namely: B10, B02 and Int10. In Table 1 a summary list of the experimental tests carried out is reported. For each test: the name of the compound (Compound); The initial state of the compound to be sampled (Initial State); the amount of compound used for the test (Amount); the method used to sample the compound (Sampling) and the stand alone operating conditions of the sensor (Operating Conditions) are reported.
Table 1 - List of tested compounds
Compound Initial State Amount Sampling Operating conditions
B10 liquid 100-500 ml open air by means of a mini pump battery operated
remote data transfer
B02 liquid 100-500 ml open air by means of a mini pump battery operated
remote data transfer
Int10 liquid 200 ml open air by means of a mini pump battery operated
remote data transfer
The QEPAS sensor has demonstrated good detection and identification capability towards B10 and B02. But, as expected, the capability to detect precursors proved to be strongly dependent on the wind strength and direction. The sensor should be positioned downwind to maximize its detection capability. Some issues emerged during the tests, due to the harsh environmental conditions at PDM in June (temperature of about 30°C and strong solar irradiation). Efficient cooling of the sensor inside the dumpster was found necessary to avoid failure of the laser head.
Electrochemical Sensor
For the BONAS project, the UCBL team is developing an electrochemical sensor which will be dedicated to the achievement of simultaneous monitoring of different explosive precursors. With this new sensor, Hexamine, H2O2, NH4NO3 and NaOCl could be detected, by cyclic voltammetry measurement on a homemade chip.
The BONAS electrochemical (EC) sensor uses electrodes to oxidize or reduce molecules soluble in water. The BONAS EC sensor uses four types of electrodes to generate EC signatures, one carbon electrode and three electrodes covered by different noble metals. These signatures are then compared to an EC database generated during the project by analyzing target compounds in water, soapy water and artificial waste water in both laboratory and in-field conditions. As this system aims to contribute to the discovery of bomb factories by the detection of precursors in wastewater, we focused on soluble precursors that could exist in sewage water from improvised explosive (IE) preparation and were included in the BONAS Project Advisory Board’s list of the most interesting targets, based on the current trends of terrorism. The EC sensor was particularly effective in detecting three precursors. Advantages of the system include portability, easily hidden in the sewage system, battery stand-alone operability, and wireless operability. The system has been successfully tested in two field campaigns (Pratica di Mare and Grindsjön(Figure 12)), showing its capability to detect target precursors directly in sewage water drains from simulated bomb factories.
Figure 12 - BONAS EC sensor during the joint demo at FOI Grindsjön
Figure 13 - Operation principle of the BONAS EC sensor. In order to take full advantage of the information provided by the sensor, a unique signature is created for each sample by merging the signals given by the individual surface electrodes as a sequence. For any new acquired measurement, a predicted probability value of the presence of a target is obtained by applying classification models based on a pattern recognition technique.
System architecture - On-board control & processing system
BONAS has developed a novel wireless sensor network able to provide semi-permanent detection capabilities. The BONAS system architecture comprises various individual components that are present in every network node, such as sensors, sampling systems, processing units, communication devices, connectors, etc. Each network node will essentially contain three different subsystems: sensor, control and processing system and network:
Sensors – The BONAS system integrates a variety of different types of technologies and therefore there is a difference in performance achievement by each of the different component. Nonetheless, all components must guarantee the correct interoperability with each other and assure the overall quality of the functionalities of the system.
Control and Processing System – This subsystem is basically composed of a computer with input/output bus (to receive and send data from and to the sensors), local storage (in order to store relevant information in case of temporary miscommunication or to establish patterns avoiding false alarms), central processing unit (processing unit that controls and drives the communications and memory hardware), ad-hoc communication capabilities (antennas and SDR-based (Software Defined Radio) Front-End that allow each network node to forward and receive information from the BONAS central unit), human-machine interface – HMI (this component is only present in the central unit in order to provide the operator with some system interaction and relevant visual data), etc.
The Network represents the virtual environment that each BONAS node will use in order to communicate with each other (including the central unit).
The overall BONAS system architecture can be seen in Figure 8. This architecture structures and decomposes the BONAS problem into several aspects which must be properly addressed, mainly focusing on the On-Board Processing System (including Input/Output aspects) and Network. This architecture, however, requires that sensors and sampling systems provide digital Input/Output interfaces to the On-Board Processing System (specific connectors and communications protocols will be detailed during the requirements phase). Moreover, a mobile BONAS central unit will be developed too, which means that a slightly different on-board processing system must be designed.
Figure 14 – The BONAS system architecture
In detail, the on-board control & processing system basic functions required:
On-board processing
Communication
Human-machine interface
Power management
Data storage
Communication devices
Communication interfaces are provided by the Wireless Adaptive Communication (WAC) nodes (Figure 9). The summary description of each communication interface is listed next:
GPS – receive location information;
USB interface – connect to sensor control devices;
Ethernet interface – connect to sensor control devices or server computers or networks;
2.4GHz wireless interface – connect to wireless sensor network.
Figure 15 – Communication interfaces
For each particular sensor, a specific physical integration was considered. For some of the BONAS sensors, it was used USB interface and, for others, it was used Ethernet interface. The physical integration involved several network communication nodes that were connected to sensors controller devices, see Figure 10. Basically, the communication nodes should collect and forward sensor data wirelessly up to the command and control centre.
Figure 16 – Communication nodes and sensor controller physical interface
Human Machine Interface
The HMI of the system has been designed to be user-friendly and intuitive for any user who shall employ it. The intuitiveness of the system will decrease the amount of time required to train a person to use the system and therefore increase the performance of the system.
Physically, the C2 centre is connected to the BONAS sensor network through an Ethernet interface. A communication network node connected to the C2 centre server is used to forward data from sensors to the C2 centre and vice-versa.
The HIM allows to observe on Google Maps view, the sensor’s displacement on the area under investigation (Figure 11) and their status of connectivity.
Figure 17 – Panel of the sensor’s displacement on the area investigated.
Figure 18 - Sensor’s control panel.
Figure 19 – Status of the different sensors
In the successive figures are displayed the data transferred (Figure 12) and the row data collected (Figure 13). The final panel release the integrated information for the end-user with the help of the expert system (described below).
Cryptography is one of the utilized tools introduced in BONAS to keep information confidential and to ensure its integrity and authenticity. The approach implemented includes the symmetric key AES, Rijndael, RC5, Twofish, Triple DES, Serpent among others as well as the widely used RSA public key algorithm.
The cryptography algorithm must be applied to secure network traffic. One of the most important protocols for communication security is the IPsec (IP security). With this type of security it is possible to send the bits composing the payload secretly and without modification from source to destination keeping unwanted bits out of the message.
Whereas the BONAS system incorporates both wired and wireless sensor devices, wireless security measure has been taken into account. The 802.11 standard prescribes a data link-level security protocol called WEP (Wired Equivalent Privacy), which is designed for the security of wireless connections. When this type of security is enabled, each station possesses a secret key shared with the base station, in the case of the BONAS system, each of the BONAS sensors would have a secret key shared with the command center. In this type of security, the only issue that remains outside the security protocol is how the shared key is indeed shared.
EXPERT SYSTEM
The BONAS expert system performs pattern recognition of the data collected by each network sensor, estimating the predicted probability of detection for each target. The system then integrates the responses from each sensor to provide the end-user with a global alarm level, summarizing information from the entire network into a unique evaluation of the possible criminal threat of an improvised explosives (IE) production site.
The expert system follows a three step workflow while providing a real time response for any new measurement:
The first step is estimating the predicted probability of the presence of each target for each sensor included in the BONAS network. The classification models adopted have been computed by means of a pattern recognition technique on a dataset of experimental data acquired from laboratory and field conditions. These classification models are then applied to any new data received to obtain the corresponding predicted probability value.
The expert system integrates the output given from multiple sensors measuring the same target to provide the user with a unique alarm value for each compound. In this context, sensor location is also considered and only output from sensors located within a user-defined distance range are integrated.
At the last step, the output from the previous steps is further integrated to provide the user with a defined global alarm. The initiated alarm considers the number of detected targets as well as the simultaneous detection of specific targets known to be used together to prepare a specific IE. The alarm value is thus increased when specific couples of targets defined by the user are simultaneously detected. Again, only output from sensors located in the user-defined distance range is considered.
A compact representation of the information provided by the expert system is shown in the dedicated user interface developed in collaboration with TEKEVER. A schematic representation of the three step approach developed for the BONAS expert system is shown in Figure 14.
Figure 20: Schematic representation of the three step approach developed for the BONAS expert system. The alarms due to individual targets are provided as output of Step 2 while the global alarm level is obtained as output of Step 3.
During both test campaigns, the command and control (C2) centre has shown its ability to communicate with all the sensors included in the network and to process, by means of the expert system, the data received. The live alarm monitor end‐user application developed by Tekever allowed a real-time monitoring of the information provided by the expert system. In fact, the user interface reports:
on the left, the alarm obtained for each target substance considering the information of all the sensors able to monitor it (step 2 output);
in the middle, the global alarm which summarizes the information from the entire network into a unique evaluation of the possible criminal threat of an IE production site (step 3 output).
on the right, a map showing the location of the different sensors is also reported. To indicate sensors’ position, markers colored according to the color code of Step 2 are used in order to show a possible positive detection of the sensor. Furthermore, a circle with a radius equal to value maximum distance threshold for data integration is also reported for each sensor. In this way, it is also possible to monitor the data of which sensors are being integrated.
Figure 21: Example of user interface obtained during the test campaign at FOI which shows the positive detections for three targets obtained by two sensors.
An example of user interface obtained during the test campaign at FOI is shown in Figure 15. It can be noticed that, as in the previous cases, three target substances are being detected by two sensors. However, since target B02 and target B15 are one of the couples of targets known to be used together in the preparation of a specific IE, a much higher global alarm level (equal to 60) is obtained in this case as output of Step 3.
The validation and performance assessment of the expert system developed for the project “BOmb factory detection by Networks of Advanced Sensors” (BONAS) has been successfully carried out, according to Task 8.5 and 8.6. Particular attention was paid to evaluate the classification models in terms of false positive and false negative results in order to optimize the performances of the expert system. Two thresholds were considered for each model on the basis of the ROC curve, one to maximize classification efficiency and one that allows to minimize false positive results.
The performance of the developed sensor network were evaluated under relevant environmental conditions of the scenarios from WP2, that is in the presence of interferents and pollutants in both the air and the water, in the Italian Air Force base in Pratica di Mare (Italy) and in Swedish Defence Research Agency (FOI) facility in Grindsjön (Sweden).
The BONAS expert system performed pattern recognition of the data collected by the network of sensors tested, estimating the predicted probability of detection for each target, and then integrating the responses from each sensor. The final aim was to provide the end-user with a global alarm level, summarizing information from the entire network into a unique evaluation of the possible criminal threat of an improvised explosives (IE) production site.
The QEPAS sensor was placed inside a dumpster used for camouflage and used to analyse vapours of IE precursors generated during an IE production (or the simulation of an IE production). The two target substances B02 and B10 included in the priority list for BONAS were tested, resulting in positive detections obtained in all the four days of tests in Pratica di Mare and positive detections obtained during all the tests carried out during the demo at FOI without any false positive detections.
The electrochemical (EC) sensor was successfully tested for main targets such as B08 and target B15, the latter both in pure form and in two commercial products containing target B15 and target B14. During all the experiments performed on field in Pratica di Mare, the EC sensor connected to the sink configuration was able to successfully acquire data and to communicate with both the network and the expert system and all the solutions obtained from the performed experiments were later analysed by ion chromatography in order to obtain the targets’ concentration.
The tests performed during the demo at FOI confirmed the capability of the SERS sensor, hidden in a toolbox, in sampling particles of target material from the atmosphere and successfully acquire spectra.
An extensive validation of the capability of the expert system to effectively integrate the information provided by the different sensors has been also carried out during the two test campaigns at the Italian Air Force base and at the Swedish Defence Research Agency. The end-users were effectively provided with a global alarm level, summarizing information from the entire network (both BONAS and EMPHASIS networks of sensors) into a unique evaluation of the possible criminal threat of an improvised explosives (IE) production site.
Investigation on new Nanostructured supports
The objective of the work was to design metallic nanostructures to provide a strong electromagnetic field enhancement in a spectral range required to address the wavelength of the Raman spectrum of the explosive precursors. This is due to the strong dependence of the Raman signal on the local electric field strength, leading to the significantly enhanced signal of surface enhanced Raman scattering (SERS) allowing to improve sensitivity of the molecular detection. While SERS signal can be obtained from a single molecule in a laboratory environment, this requires state of the art spectrometry with well controlled polarization, collimation and angle of incidence to make full use of resonant electromagnetic field enhancement on the plasmonic substrate, which is very sensitive to these conditions. The objective of this work was to design a robust and cost-effective SERS substrate which can be employed in the project scenarios with the determined sample collection approach. This involves 1) relaxed requirements on angle of incidence, so that there is no need to for collimated illumination but instead strongly focused illumination can be effectively used, 2) relaxed requirements on the polarisation of the excitation, so that there is no need to control polarization of the excitation light, 3) despite the above, to provide strong wavelength resonant enhancement at the required excitation and scattering wavelength, and 4) take into account the working conditions with respect to the decided sample collection scenario.
The objectives have been achieved using modified surface plasmon polaritonic crystals on specifically roughened Au films. The adopted approach provide a practical solution to the SERS detection in the scenarios of the project providing: 1) environmentally stable SERS substrates since Au films are used; 2) the substrates capable to withstand the laser powers needed for the operation of the Raman spectrometer without damage and deterioration; 3) the substrates compatible with the decided sample collection scenario; and 4) the substrates providing the sensitivity of SERS detection superior to the commercially available (e.g. Klarite © substarte) in the decided sample collection scenario.
The design was based on a controllably roughened Au films with the overlayered periodic patterns (the so-called plasmonic crystals) to provide “photonic-crystal”-like structure for surface plasmon waves. The roughness is used for generation of plasmonic hotspots in the nanoscale morphology of metallic surfaces. To further enhance the electric field in these hotspots, the plasmonic crystals can be made to provide additional pathway through which light can interact with the surface by resonantly coupling the incident light to the plasmonic excitations. These crystals have many geometrical parameters that can be tailored for the BONAS applications. The sample collection scenario constrains mean that the self-organisation process of the formation of nanocrystalites of target molecules after the drop casting of analyte solution from the sample collection apparatus will also benefit the overall enhancement. The resulting surface topologies and plasmonic crystals and their performances were presented in Deliverable D6.4.
Taking into account the specifications for the SERS substrates required in the framework of this project: robustness, weak dependence on the illumination conditions and compatibility with the selected sample collection scenario, it was shown that plasmonic crystal substrates based on roughened gold provide the required SERS detection parameters for RDX detection with a 100 fg limit. Moreover, they are thermally stable against extended exposure the high laser powers used and outperformed a commercial SERS substrate (Klarite) for the detection of RDX in the required scenarios of the sample collection.
SERS sensing technique is ubiquitous in many applications where chemical specificity and high sensitivity. Thus, the developed approach to SERS substrate design and tailoring can be used to address other sensing scenarios in environmental, chemical and biological sciences. While the design parameter can be readily tuned to address one or another substance in question, the specific attention in future applications should be paid to the sample collection approach which is different in different applications and can affect the overall performance of the nanostructure.
SERS activity: QUB substrates
Under this deliverable the QUB team investigated a number of substrates for surface enhanced Raman spectroscopy (SERS) with the purpose of deployment in the condenser-sampler system developed at EADS which incorporates the Serstech mini-Raman spectrometer. Three types of substrate were examined or used to a large extent during the course of the project (see Figure 22):-
KlariteTM (discontinued March 2014) – this was a commercial substrate from Renishaw which was based on inverted pyramidal structures etched in silicon and coated with a proprietary Au deposit (Figure 22 (a)).
Gold nanowires (NWs), based on the electrochemical deposition of Au in a porous alumina template where the host alumina matrix was generally subsequently stripped, leaving a free-standing array of Au NWs (Figure 22 (b) and (c)-(f)). A variation in the fabrication procedure utilising a sacrificial polypyrrole core in the alumina pores yielded Au nanotubes as shown in Figure 22 (g).
Gold coated arrays of nanodomes (NDs) as shown in Figure 1(h) and (i). The substrates were formed by a nano-imprint lithography process (R. Winfield, Tyndall National Lab., Ireland under NAP project 376) and subsequently Au-coated at QUB.
The polymer base-material in which the ND arrays were formed was found to be very prone to thermal damage on exposure to the type of high power laser (>150 mW) incorporated in the Serstech system. These substrates were therefore not pursued any further in the context of BONAS. The gold NWs (and nanotubes) served as a platform for rigorous analysis of the SERS performance in terms of 3 different plasmon modes supported in the system (Figure 22 (d)-(f)). This work was reported in ref [1] in which support from BONAS was acknowledged. The modes involved are a transverse mode (a surface plasmon resonance across the diameter of the free-standing NWs), a longitudinal mode (along the length of the NWs), and a cavity mode, supported only between two adjacent NWs that are in very close proximity (< 10 nm) along their length. The detail of the interplay between these modes and their influence on the Raman response is discussed in detail in ref [1] and need not be developed here. The key point is that the principle of operation depends on the extreme concentration (by the nanostructured surface) of electromagnetic energy into highly confined volumes to exploit the approximate fourth-power dependence on the magnitude of the incident electric field, |E|4. Thus a molecule placed in a region with a 10x field enhancement (relative to the free-space background for the same laser input) will yield a Raman signal that is amplified by 104 relative to that which it would yield in bulk solution. As it turned out the enhancement factor (EF) for the NW substrates was relatively modest, mostly in the 103 range, but they proved extremely useful for physical analysis and understanding. In addition, unlike the nanodome substrates, they proved reasonably robust under laser radiation.
Figure 22 Various SERS substrates trialled during BONAS (a) SEM image of KlariteTM substrate showing inverted square pyramid structures in silicon with magnified detail showing structured, wavy gold deposit within the pyramid structures (Image from H. Wackerbarth et al., Applied Optics, 49, 4362 (2010)). (b) Au NWs grown at QUB in porous alumina template (scale bar 100 nm) with (c) schematic illustration of structure showing (d) transverse, (e) longitudinal and (f) cavity plasmon modes supported in the system. (g) Au nanotube variant of NW substrate (scale bar 100 nm). (h) Top-down view and (i) 45o view of Au ND substrate (scale bars are 100 and 200 nm in (h) and (i) respectively).
For much of the experimental work we used Klarite on account of the good uniformity across a given substrate and reproducibility from substrate to substrate and batch to batch. We considered these features to be of primary importance in making comparative tests of a range of samples over longer periods of time. Also, while the EF was not outstanding (up to 104 at best), they proved extremely robust thermally. These substrates formed the basis for our work on the novel detection of very dilute acids by means of derivatization – this has recently been reported in Analytical Chemistry[2] where, again, BONAS was clearly acknowledged. Some of the common acids, such as nitric and sulphuric acid can be created as effluent of bomb making either directly or via the production and subsequent interaction of NO2 or SO2 gases with water vapour. The weak acid solutions in atmosphere cannot normally be detected by Raman spectroscopy or even SERS. In SERS the condensation of a weak acid on the substrate is, in itself, ineffective since only a tiny fraction of the target molecular species exists at surface sites of significant field intensity that drive the enhancement. Allowing a droplet of weak acid solution to dry on the SERS substrate is also ineffective since the target analyte evaporates contiguously with the water. The key to solving this detection problem is to form a derivative (salt in this case) of the analyte species (the relevant 〖"NO" 〗_3^- or 〖"SO" 〗_4^(2-)anion) through reaction with excess ammonium hydroxide. In this scenario the derivatized material, NH4NO3 or (NH4)2SO4, is deposited directly on the enhancing surface as the excess water evaporates, yielding significant signal enhancement; the reaction scheme for the case of nitric acid, for example, is:-
HNO3 (aq) + NH4OH(aq) → NH4NO3 (aq) + H2O → NH4NO3 (s) (1)
A depiction of the derivatization scheme, with relevant SERS spectra (comparing spectra from acid solution droplets with those from the derivative species) is shown in Figure 23, taken from ref. [2]. This figure illustrates is an improvement of the derivatization methodology over detection in solution by a factor of more than 1500, while the low-concentration detection threshold (~100 ppb) is ~ 4 orders of magnitude times lower compared with liquid phase detection. These findings represent very significant gains.
Figure 23. (a) Sketch illustrating the derivatization drop-casting technique where contaminant in solution forms salt and is then allowed to air dry; magnified 3D schematic of SERS surface illustrates higher concentration of derivative molecules very close to the surface after drying. (b) and (c) show SERS spectra from nitric acid and sulphuric acid solutions respectively:- 5% (black), 1% (blue) concentration liquid solutions on SERS surface before evaporation and 100 ppm (red) solution derivatized using ammonium hydroxide and allowed to dry as described in the text. Each set of spectra is plotted on same Raman intensity scale with individual spectra offset for clarity.
Prospects
While the derivatization technique successfully addressed deliverable 6.3 of BONAS, we consider that it could significantly extend the applicability of the SERS technique in general. The next step would be to demonstrate the technique’s efficacy in relation to the detection of other trace species in solution. The second advance that is required - perhaps surprisingly, after all the 40 years of the existence of SERS as a technique – is the fabrication of a SERS substrate that (i) displays a high EF (ideally >106) and is (ii) reproducible, (iii) uniform across its surface, (iv) thermally robust and (v) cheap. While Klarite scored highly on (ii) to (iv), the EF was relatively modest and it was always way too expensive (by at least a factor of 10) for routine application in chemical lab environments. As noted above, Renishaw withdrew this product earlier in the year. Replacement commercial substrates which are much cheaper are so because they are based on replication processes resulting in polymer or plastic substrates which makes them very prone to thermal damage from the input laser required from Raman spectroscopy. The production of a SERS substrate with all the properties (i) to (v) is a priority. The generally perceived challenge inherent in (i) to (iii) is the production of uniform nanoscale features on the surface. However, our efforts on the Au nanodome structures (Figure 1 (h), (i)) represent an attempt to depart from this philosophy by producing the necessary enhanced field regions through the excitation of a Fano resonance on a microstructured (rather than nanostructured) surface, thus considerably relaxing the spatial resolution requirements of the fabrication process. Physically, our initial results show that this process is at least partially successful and would be worth pursuing; the main drawback is that the current substrates are prone to thermal damage, but this is a materials problem that could be addressed.
In an additional, initial line of experiments we have recently been producing our own silicon pyramid structures through the well-establish KOH anisotropic etching route (Figure 24). These direct ‘Klarite-replacement’ style substrates could also be configured for exploiting Fano resonance in SERS and offer a line of investigation which we are keen to pursue in the future.
Figure 24 (a) Array of inverted pyramidal pits in Si(100) surface, produced by anisotropic KOH etch and subsequent coating with Au. The windows in the photoresist defining the etch regions were circular, but the etch pits are inherently square in (horizontal) section, hence the shadowing of the Au deposit at the corners. (b) ‘Template stripped’ single Au pyramid which has been extracted from the Si substrate by deposition of resin and subsequent lift-out.
Immunosensor
For the BONAS project, the UCBL team is developing an immunosensor which will be dedicated to the achievement of simultaneous monitoring of different explosives. With this new sensor, RDX and TNT could be detected. The test selectivity is proven by simultaneous detection of the two explosives with different possible cross-reactants.
The UCBL team has selected different targets and associated antibodies from commercial sources. Two explosive compounds were chosen according to their importance in bomb fabrication: TNT and RDX were purchased from LGC Standards, France. The associated antibodies, anti-TNT and anti-RDX were supplied by Strategic Diagnostics, USA. In order to test their selectivity (cross-reactivity), pollutants from different sources (3 pesticides, 1 toxin and 1 control molecule) which can be found in wastewater were also selected: 2-chloro-4-ethylamino-6-isopropylamino-1,3,5-triazine (atrazine), 2,4-dichlorophenoxyacetic acid (2,4-D), 2,4,5-trichlorophenoxyacetic acid (2,4,5-T), 4-benzoylbenzoic acid (4-BBA) and okadaic acid from Prorocentrumconcavum were obtained from Sigma-Aldrich, France
Figure 25 - The competitive multiplex immunoassay set-up.(a) Composition of the different matrices used. (b) Principle of the adhesive microarray assembly with the bottomless plate
Potential Impact:
BONAS project system has proposed a business model during the creation of the technology roadmap of the system, in which all the segments composing the exploitation strategy of a possible BONAS system have been provided (Figure 26).
Figure 26 – The BONAS technological roadmap
Four main exploitation strategies have been defined for the commercialization of the system, and subsequent service offering generated from acquisition of the system. These were:
• Sell developed sensor and sampling device individually
• Sell BONAS system as a whole
• Provide a customizable offer of the BONAS system
• Provide post-sale services
Given these four main possibilities of exploitation strategies, and given the relatively niche market in which the BONAS system aims to, one main business case has been identified, which could meet current market needs and provide a system capable to increase the capability in counter-terrorism actions, namely a Technology Manufacturer (TM).
A TM business model develops and integrates sensor devices, as well as sampling devices to be commercialized as both individual products and as a whole BONAS system. This dual commercialization vision provides to the user the possibility to acquire a customized system which meets their needs. The revenues in this case are obtained from the commercial sales from both individual sensors and system. In this case the capital costs (CAPEX) required to develop the technology is rather high while the OPEX will mainly depend on the involvement of the vendor in user-related services.
The TM business model is divided into the nine main building blocks defined which delineate each of the properties of the proposed business model (Figure 27).
• Key partners
• Key activities
• Key resources
• Value propositions
• Customer relationships
• Channels
• Customer segment
• Cost Structure
• Revenue Streams
The business model, in sum, is a model which aims at offering an innovative product(s) to requirements driven activities in a niche market. The schematic representing the business model described before is depicted in Figure 27.
Figure 27 - BONAS business model details
Strengths and weaknesses of BONAS
A strength and weakness study was performed towards the identified business model, in which a SWOT assessment was implemented. The SWOT analysis includes the identification of the internal status of the solution under analysis, through the analysis of the Strengths and Weaknesses (SW) of the solution at hand, as well as the set of opportunities which might be available for the future upbringing of BONAS (Opportunities - O). Finally the envisioned harmful elements towards this system application will be taken into account (Threats – T).
Strengths
• Varied sensor devices integration
The BONAS system integrates several different sensor devices which provide the required help to perceive the presence of bomb precursors in an urban environment. The BONAS system provides this capability with proven performance, even already with the sensor devices at a low TRL stage.
• Capability for mobile sensor devices
Most sensor devices have the capability to be mobile, i.e. they may be moved throughout the area to be monitored without any major difficulty. This capability enhances the monitoring capability that the BONAS system provides, giving a larger amount of freedom during he sensor placement to the end-user.
• Independent networking infrastructure
The network established in BONAS, used for the transmission of the sensorial data from the sensor devices to the command center is conceived and upheld through the use of an independent infrastructure, thus not relying on external infrastructures to transmit data within the established network.
• Customization and flexibility options
The BONAS system was designed to accommodate varied customization options for the end-user of the system, in which, among other, the number of sensors, network capability, inclusion of other sensor may be modified depending on the specific requirement needs of the end-user.
• Deployment
The BONAS system is very easy to deploy, and since no external infrastructure is required, there is no need for complicated network establishment.
• User friendly
The BONAS system presents a user friendly and easy-to-use GUI, which requires very little training time for a human operator to use.
• Scalability
The BONAS system was designed and developed to accommodate possible additions of further sensor technologies to the system. This way the system is not closed and compatible only with the sensors already integrated, but rather open so that any new innovative sensor technology may be added to the operation of the system.
• Camouflaged system
Most of the BONAS sensors have been designed and developed in order to be easily camouflaged in unsuspicious locales using common used objects found on vicinities of houses and neighbourhoods.
Weaknesses
• Low Maturity
Some of the sensor devices, developed for the BONAS system, are still in a very low level of maturity, requiring further development in order to meet the requirements for a commercial product.
• Physical ruggedness
Some sensor devices and the network connection devices are at this stage not applicable in very inhospitable environments, such as in very humid weather conditions, however some design choices have already been thought of and even implemented to try to circumvent this fact.
• Specialized Operators
Some of the sensor devices in the BONAS project must still be operated by a specialized human operator, given the high level of tuning and checks that must be performed throughout the use of the sensors. There is also the case of some sensors which don’t need a human operator by the sensor since they may be controlled through the HMI of the BONAS system.
• High power consumptions requirements
Some of the sensor devices present a very large power consumption, which requires the use of large power storage components nearby, rendering the camouflage of the sensors more difficult.
Opportunities
• Security necessity
Unfortunately, terrorist attacks where IED or home-made bombs are used to inflict fear and terror to people are still a reality in this age, and where safety seems to be one of the most valuable assets that a nation may have. The BONAS system is devoted to provide an instrument with its main objective in finding bomb factories, in order to dismantle any terrorist activities before the bombs are used. Therefore, this provides with an opportunity for the commercialization and use of the system in security-prone activities.
• Integration of other sensor devices or connection to external systems
The scalability of the BONAS system may be one of the most important features of the systems, since it provides the capability to integrate more sensor devices, or even another similar system which may complement the monitoring activities performed by BONAS. This provides an opportunity for the system towards the integration with other system.
• Little competition in the market
At the moment of writing there is no commercially available system as BONAS in the market, giving it the opportunity for BONAS to be the first of its kind on the market.
Threats
• Niche Market
The BONAS system is directed towards a niche market which presents little number of end-users which may integrate the system to their everyday activities. Therefore, any competition that may arise will be competing with the BONAS for an already very limited market.
• Regulation
Some of the sensor devices require to be regulated in order to be able to be used in urban environments. This fact increases the cost to further develop and finally deploy the system in the environment that it is supposed to be used.
• Competing products
Other systems may arise which may have the same characteristics of BONAS, which would compete with the system in a very limited available market. However, the independent network infrastructure and high performance shown by the sensor devices reduce the impact of this threat.
• New and innovative fear inducing techniques by terrorist groups
Terrorist groups are always trying to find new and innovative ways to use terror as their ally in order to inflict fear to people. The BONAS project is mostly devoted to the detection of bomb precursors during the fabrication of bombs in a bomb factory, however if terrorist groups find other ways to inflict fear into humans using other techniques other than bombs, this would render the BONAS system useless for this application. Nonetheless, given the historic terrorist events throughout time, it is foreseen that, unfortunately, homemade bombs will continue to be an issue that must be tackled.
BONAS business model evaluation
It is important for each organization to perform a regular assessment of the adopted business model and indicate what their objectives are for the results obtained from the project at hand and also to evaluate the health of the market to which it is being directed.
This evaluation consists mainly on answering a set of questions that help define the properties of the business building blocks.
Although a first assessment of the business model defined for BONAS is performed in this document, a deep assessment of the business model and eventual updates must be performed recursively taking into account the changes in the target market and the development phases of the system and as such is out of scope of this document.
Strengths and weaknesses
It is of extreme importance to identify the strengths of the business model defined for the system, in order to fully understand the opportunities that may emerge. In contrast the weaknesses must also be understood in order to identify the threats that are envisioned for the commercialization of the system at hand.
One of the main strengths of the business model proposed for the BONAS system is the fact that it is foreseen the development and commercialization of highly innovative technological products. This fact brings the advantage that typically high valued items contribute to an enrichment of the revenue streams. The value proposition of the business model is characterized for being very well aligned to the needs of the possible end-users to oppose the activities performed by terrorist groups, by detecting and provide aid for the dismantling of bob factories that may be present in a certain city. The business model also takes into account the scalability of the system which renders it prone for the inclusion of other sensorial technologies in the future, providing high customization possibilities to the end-user. The aim to which the BONAS system is directed to may even change somewhat for monitoring and detection of other elements, by adding other sensor devices of the system which may meet the requirements for other types of activities other than bomb factory detection. This enables the business model to be somewhat flexible, given the high customization opportunities provided by the system.
In contrast, the weaknesses of the system are mostly concerned with the niche market to which the system commercialization opportunities are directed. This fact will decrease the reach of the value proposition, as well as increase the risk and uncertainties of the revenue streams. However, given the high customization possibilities of the system, the customer’s requirements may be met through the inclusion of other features, sustained by the design choices implemented in the system. Finally, as the development of customized solutions based on BONAS products and services is attained by involving the clients in a co-creation process and by providing them personal assistance, it is expectable that strong and enduring relationships are established with the customer segment.
The following table presents a summary of the strengths and weaknesses towards each building block of the presented business model.
Building Block Strengths Weaknesses
Key partners Partnerships with further sensor and sampling devices manufacturer are encouraged.
Key activities Complete, ready to use system with high customization possibilities. Activities pertaining to security measures of the general population.
Key resources Highly specialized and difficult to replicate. High development requirements. Costly maintenance issues.
Value propositions Offer a novel system capable to help fighting terrorist and their activities, by increasing the security against such activities in a certain zone.
Customer relationships Co-creation and personal assistance
Channels Use of already established relationships, which proves to be efficient. Must reach all the potential clients.
Customer segment Continuous acquisition of clients Niche market difficult to permeate.
Cost structure Known costs for the development of sensors and system. Difficult to permeate niche market.
Revenue Streams The main objective of the system is directed to increasing the security of the person living in the area where the system is introduced, in order to hamper terrorist activities and their bomb making processes. This fact may encourage customers to acquire the system. The system will only be sold one time to the end customer, in which a repeat purchase is limited, unless new sensor devices and requirements arise from the customer’s point of view. This renders the foreground of revenues unpredictable and the corresponding risk relatively high.
Table 2 - Summary of strengths and weaknesses of the BONAS business model
Opportunities
The main activity in which the BONAS system excels in providing support in, is related to counter-terrorism activities, which, unfortunately, are a very real threat nowadays. Thus the BONAS system is devoted to make use of several sensor technologies which working together under the same command center may provide real time monitoring data from surrounding indicating the presence of possible terrorist bomb factory sites in the vicinity. This set of features of the system encourages possible customers to acquire this system and its abilities to fight this threat. The BONAS system was designed and developed by providing customization opportunities to the customer, thus allowing it to be very flexible in terms of what kind of sensorial technologies may be added to the network in order to meet the requirements from the customer. However from the business model point of view, the recurring revenues from this on-time transaction are very low, and unless maintenance services or training services are required, there is no recurring revenue from the sale of this system.
Threats
This section identifies the threats that may arise from the defined business model of the BONAS system, in general threats appear from the weaknesses identified in the business model once it is implemented. Thus, similarly to the opportunities, these are very difficult to perceive beforehand.
The main threat that this business model envisions is the application of the system in a very limited niche market, which only a very few possible customers may buy the system as a whole. This is why the possibility to customize and buy only some elements of the system is available, in order to broaden up the customer range to which the BONAS system is aiming at. Another threat is related to the actual development and maintenance of highly specialized sensor devices which is still very costly and increase significantly the cost of the system, thus rendering it a valuable asset for customers to acquire. A third threat may arise if terrorist activities change in terms of fear inducing solutions in the future, i.e. if terrorist groups start using other means for inflicting fear instead of the use homemade bombs, a system which may finality is to detect bomb factories is rendered useless for any customer and must be clearly revised in order to shift its main direction to another pending problem.
To prevent the possibility of a newcomer to penetrate the market segments to which the BONAS system is aiming at, and in order to convince the customers to use BONAS, first a high customization prospect is introduced to the system, giving it an edge towards the accomplishments of the requirements of the customer and it will be also important to establish strategic partnerships with business actors, which are already involved in the corresponding market segments.
Partner exploitation strategies
A brief definition of some partner’s exploitation strategy is presented in the following sections, namely towards the technology developed in the BONAS project. For the others not mentioned the future interest is in the use of the expertize achieved in the BONAS for future applications.
ENEA
ENEA participated in the realization of a lidar/DIAL remote sensor providing the vapour concentration of explosive precursors as a function of the range from the system. Public administrations or private bodies interested in homeland security (Army, Police, airports…) are very interested in the lidar/DIAL remote sensor. ENEA will exploit its knowhow and system by establishing collaborations with the abovementioned institution, serving at different levels: consulting, joint participation to Italian and/or European tenders, supply of complete systems. Success in this strategy will pave the way to the development of feasible, cost-effective and robust lidar/DIAL remote sensor for bomb factories, opening up employment opportunities in Italy/Europe since it addresses a global market need that is not supplied commercially at present. The prototype is reasonably cheap and can be produced for monitoring of airports, stations, squares.
CREO
This market can offer very interesting business opportunities for QEPAS sensors hyphenated to Gas Chromatographic (GC) columns. Currently, no commercial product exists. Miniaturized QCLs and silicon micro-GCs make it possible the development of highly integrated and miniaturized GC/QEPAS systems that, by exploiting the physical and chemical scaling laws of sensitivity and response speed, can compete with IMS and GC/Mass instruments in terms of costs, ruggedness, maintenance, portability, and sensing performance. QEPAS sensors have already proven high specificity against precursors, and ability to operate in a wide detection range, from real traces to ‘big’ traces.
CREO is currently searching for new research opportunities within the H2020 program, and has recently proposed a GC/QEPAS sensor as part of an integrated system for the analysis of cargo containers.
SAB
SAB has developed a very small and compact Raman spectrometer module during the BONAS project. As a matter of fact, some modules have already been commercialized and sold for evaluation to such customers. Furthermore, the spectrometer module has also been integrated independently by Serstech into a handheld Raman Spectrometer (Figure 28). This has been fully productized and is now commercially available and used by early customers for chemical identification. A network of sales partners in Europe and Asia has already been established and it will grow further.
Figure 28 - By being small, easy to use and affordable, the Serstech 100 Indicator helps customers within police, rescue services, customs and industry to save time, money and even lives.
TEK
All of the results obtained by TEK during its work in BONAS serve a specific purpose in the company’s research roadmap. TEK defines an internal R&D agenda based on existing or developing product lines. This agenda identifies capabilities and functionalities that we desire to include in each of our products based on:
• User needs and requirements passed on to TEK;
• Market opportunities;
• Lack of competition for a particular supported functionality;
• Internal technology watch activities;
• Evolution of existing functionalities.
After the end of the BONAS project, further work will be required by TEK in order to have the WAC ready as products for full commercialization. This includes:
• Further design efforts of the enclosure of the WAC radio, increasing the ruggedness and resistance to water for use in more demanding environments
• Replacement of antennas with higher gain directional antennas, enabling communications through longer distances
• Improvements on power management capabilities of the WAC device
LDI
The following work was performed by LDI for the BONAS project:
LIDAR mockup development that allows remote detection of substances (acetone, etc.) which are used in manufacturing of explosives by scanning in DIAL and the DOAS modes.
LDI presents the following exploitation steps for the future of the technology developed in BONAS:
• Design, assembly, installation, configuration and testing of LIDAR.
• Development and modification of flexible software.
• Company participation in planned meetings and testing.
CSEM
CSEM also contributed to the proposal for perspective lightweight airborne sensor for detection of unauthorized explosive precursors, based on DOAS method, exploiting the same wavelength range as the lidar/DIAL prototype. In future CSEM seeks participation with the same activity, where the acquired expertise may be applied in the realisation and the exploitation of lidar systems, ground based or airborne, in the interest of public administrations and/or private bodies active in homeland security. This includes consulting, developments and supply of lidar detection subsystems, as well as participation to tenders (national and/or European) with complete lidar systems. CSEM also exploits the possibility to propose to industry and environmental agencies, the realisation of DIAL/DOAS systems based on the same principles as the one realised in BONAS, for detection of industrial gases pollutions.
EADS
At the turn of the year 2014 the former EADS group was renamed into Airbus Group and a reorganization process across the group has taken place. Airbus group unites the capabilities of the three market leaders Airbus, Airbus Defence and Space and Airbus Helicopters. The group-wide research and development operates under the name Airbus Group Innovations (former EADS innovation works). Airbus Group Innovations was forced to change its orientation towards the civil aircraft manufacturer Airbus. Besides this restructuring process Airbus as a group is always interested in innovative sensor developments and functionalities for here products.
Obviously sensor systems for critical infrastructure and security application are showing some related functionalities and market opportunities were the company should find similarities and marked opportunities. Involved BONAS partners are already starting finding out the necessaries and opportunities. If opportunities are exist, involved component level production could be licensed / subcontracted by EADS business units from/to SME technology suppliers.
ONE
The work done in BONAS for onboard lidar system for IED detection , is a first step which can be regarded as a feasibility work. The laser sources are commercially available and implementation onboard a UAV has been studied in terms of size limit. Any work performed in Bonas will hopefully be used to build new projects dedicated to onboard detection of IED, or for other onboard UAV lidar applications.
The Dissemination process of the BONAS project was divided in two major groups, Internal and External Dissemination, in which, each one presents its own instruments for completing the objective at hand.
Internal Dissemination
The internal dissemination is a process in which is intended the distribution of external information and data throughout the consortium, in order to create a database of pertinent information collected during outside events including:
• Similar Projects
• Similar Technologies
• Articles
• Conferences or Events
o Information of possible End-Users or similar technologies presented
• Contacts
o Collection and distribution between the consortium of contacts of possible End-Users
External Dissemination
The External Dissemination’s main purpose focuses on promoting BONAS in environments external to the consortium. It includes:
• Public Presentation of BONAS to:
o End-Users
o Associations (e.g.: ASD, detection of explosives association, …)
o During events
• Participation in Conferences
Instruments for Dissemination
The aforementioned objectives were achieved using various dissemination instruments including:
• Creation of a website (Internal Dissemination)
o The website included a mailing list and a document repository for the members of the consortium
• Creation of an Advisory Board including end-users and technology experts in order to provide feedback and expert advice to the members of the consortium regarding the requirements throughout the project. (Internal and External Dissemination)
• Disclosure by the means of: (External Dissemination)
o Press releases
o Brochures
o Presentations (creation of a template presentation for all members of the consortium)
o Articles
The following chapters present the details on the dissemination activities and dissemination instruments used for the proliferation of the results obtained from the BONAS project.
BONAS participation in events and press releases
Press Release to the International Science Media
Presentation to possible End-Users in Italy
Video and pre-demonstration event in Pratica di Mare
The BONAS consortium has participated in a pre-demonstration event of the BONAS system at the Pratica di Mare Italian Air Force facilities between 9th and 13th of June 2014.
A video with some highlights of this event was created during the event and made publicly available (also available on the Cordis channel from Youtube). It may be found using the following link:
https://www.youtube.com/watch?v=kTHGB4xhSIc
Security Research Conference and CPExpo
The BONAS project was presented in the Security Research Conference and CPExpo (Community Protection Expo) by Antonio Palucci, Francesco Saverio Romolo, Christophe Marquette, and Peter Höjerback between the 9th and 11th of December 2014 in Genoa, Italy.
The BONAS consortium will have a devoted box for the presentation of the results of the project.
Figure 29 – Genoa Expo 2014
BONAS-EMPHASIS Joint Demonstration event
Both BONAS and the EMPHASIS projects had a joint demonstration event in Stockholm between the 24th and 25th of September, in which on the 24th was presented an experimental demonstration of both systems, where groups of persons (including possible end-users of the systems) have observed the several technologies being developed in the projects operating as a whole system. On the 25th a conference was held, in which were discussed the results from the 1st day of the demonstration event.
Several videos and photographs were made available showing some of the stations presented at the demonstration event.
Reports have been created indicating the results obtained from the demonstration event with some interviews from the persons working on both projects. The reports may be found by accessing the following links:
http://www.bbc.com/news/world-europe-29421952
http://www.bbc.com/news/science-environment-29416842
Recapping the BONAS’s partners have produced:
• 8 publications on peer review
• 28 communications to conferences
• 1 PhD
• 2 media dissemination
• 1 exhibition
• 1 video broadcasting
List of Websites:
The BONAS website is used as an instrument to disseminate the general information about the BONAS project to the public audience. It plays a crucial role in the dissemination of the activities of the project, including the news, the published documents, the public events, the incremental results and so on.
The website was implemented as a Google site (http://sites.google.com). It is available at the url:
https://www.bonas-fp7.eu/
and as mirror to the http://bonas.tekever.com/
The website is being regularly updated to inform about the project latest news.
Regarding the everyday working among the partners, the BONAS consortium is using a FTP based site as file sharing system. This site serves both as a collaborative tool for the partners and as a repository of all the documentation produced during the project history. The repository was only accessible by the members of the consortium through the use of the appropriate credentials.