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Development of Fjord-Edged Graphene Nanoribbons by Rh-Catalyzed Stitching Polymerization

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Graphene nanoribbons (GNRs) are the class of next-generation carbon materials that have emerged as promising candidates in future nanoelectronic devices. Their unique electronic, thermal, and magnetic properties heavily depend on their widths and edge topologies. Thus, the precise design and synthesis of GNRs is highly desirable with control over the structural factors with atomic precision. In this regard, the bottom-up synthesis approach plays a significant effort in constructing a wide variety of well-defined widths and edge structures. Moreover, the planer GNRs (zigzag, armchair, and gulf edges) were extensively explored, whereas curved GNRs (cGNRs) (cove and fjord edges) were scarcely reported. Although cGNRs exhibit novel edge topologies, narrow band gap, and high carrier transport mobility provide further opportunities in nonlinear optics and asymmetric catalysis. Thus, efficient synthetic strategies and precise molecular designs are highly desirable for the synthesis of curved GNRs. To address this gap, we propose the Rh-catalyzed stitching polymerization strategy for the construction of fjord-edged graphene nanoribbons (FGNRs). The proposal will systematically investigate the stitching efficiency through NMR analysis and examine the influence of alkyl chains on the synthesis and solubility of cGNRs. Additionally, the methodology will be extended to the synthesis of heteroarenes containing cGNRs, with a focus on their solubility in organic solvents for further optical and electronic property studies. Furthermore, a comparison will be made with the living chain growth polymerization strategy applied to the same substrate. Overall, this proposal will systematically address the challenges associated with the synthesis, characterization, and structure-property relationship of fjord-edged graphene nanoribbons to pave the way for advancements in the field of nanoelectronics.

Wissenschaftliches Gebiet (EuroSciVoc)

CORDIS klassifiziert Projekte mit EuroSciVoc, einer mehrsprachigen Taxonomie der Wissenschaftsbereiche, durch einen halbautomatischen Prozess, der auf Verfahren der Verarbeitung natürlicher Sprache beruht.

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