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The role of precursor coverage in the synthesis and substrate transfer of graphene nanoribbons

Published 17 Jul 2024 in cond-mat.mtrl-sci | (2407.12646v1)

Abstract: Graphene nanoribbons (GNRs) with atomically precise widths and edge topologies have well-defined band gaps that depend on ribbon dimensions, making them ideal for room-temperature switching applications like field-effect transistors (FETs). For efficient device integration, it is crucial to optimize growth conditions to maximize GNR length and, consequently, device yield. Here, we investigate the growth and alignment of 9-atom-wide armchair graphene nanoribbons (9-AGNRs) on a vicinal gold substrate, Au(788), with varying molecular precursor doses (PD) and, therefore, different resulting GNR coverages. Our investigation reveals that GNR growth location on the Au(788) substrate is coverage-dependent. Furthermore, scanning tunneling microscopy shows a strong correlation between the GNR length evolution and both the PD and the GNR growth location on the substrate. Employing Raman spectroscopy, we analyze samples with eight different PDs on Au(788). We find that GNR alignment improves with length, achieving near-perfect alignment with an average GNR length of ~40 nm for GNRs growing solely at Au(788) step edges. To fully exploit GNR properties in device architectures, GNRs need to be transferred from the gold to semiconducting or insulating substrates. Upon substrate transfer, samples with higher PD present systematically better alignment preservation and less surface disorder, which we attribute to reduced GNR mobility during the transfer process. PD also affects the substrate transfer success rate, with higher success rates observed for samples with higher GNR coverages (77%) compared to those with lower GNR coverages (53%). Our findings characterize the important relationship between precursor dose, GNR length, alignment quality, and surface disorder during GNR growth and upon substrate transfer, offering crucial insights for the further development of GNR-based nanoelectronic devices.

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