Abstract

The transfer of aligned graphene nanoribbons from metallic growth surfaces to device-compatible platforms remains a central bottleneck for nanoribbon electronics. Here, we investigate step-edge passivation of vicinal Au(788) by chevron-GNRs as a strategy to improve the transfer of aligned 9-armchair graphene nanoribbons. Scanning tunneling microscopy reveals that chevron-GNRs preferentially occupy step-edges, effectively acting as passivators that displace 9-AGNRs toward terrace centers, thereby altering their local growth configuration. To quantify transfer performance, we establish an automated large-area Raman analysis framework that enables pixel-wise classification based on the G mode and the radial breathing–like mode (RBLM). This approach provides a robust and scalable metric for assessing both transfer coverage and local ribbon integrity across macroscopic areas. Raman mapping uncovers strongly inhomogeneous transfer, characterized by extended regions with no detectable GNR signal and pronounced spatial variability in the RBLM-to-G intensity ratio. Transfer quality varies substantially across the sample series, with only a single high-yield outlier and most samples remaining well below 100% transfer yield. These results demonstrate that while chevron passivation locally modifies the growth configuration of 9-AGNRs on Au(788), it does not yet yield reproducible, high-quality transfer of intact aligned ribbons. The presented Raman-based analysis framework establishes a quantitative benchmark for the systematic optimization of GNR transfer strategies.

Abstract

The transfer of aligned graphene nanoribbons from metallic growth surfaces to device-compatible platforms remains a central bottleneck for nanoribbon electronics. Here, we investigate step-edge passivation of vicinal Au(788) by chevron-GNRs as a strategy to improve the transfer of aligned 9-armchair graphene nanoribbons. Scanning tunneling microscopy reveals that chevron-GNRs preferentially occupy step-edges, effectively acting as passivators that displace 9-AGNRs toward terrace centers, thereby altering their local growth configuration. To quantify transfer performance, we establish an automated large-area Raman analysis framework that enables pixel-wise classification based on the G mode and the radial breathing–like mode (RBLM). This approach provides a robust and scalable metric for assessing both transfer coverage and local ribbon integrity across macroscopic areas. Raman mapping uncovers strongly inhomogeneous transfer, characterized by extended regions with no detectable GNR signal and pronounced spatial variability in the RBLM-to-G intensity ratio. Transfer quality varies substantially across the sample series, with only a single high-yield outlier and most samples remaining well below 100% transfer yield. These results demonstrate that while chevron passivation locally modifies the growth configuration of 9-AGNRs on Au(788), it does not yet yield reproducible, high-quality transfer of intact aligned ribbons. The presented Raman-based analysis framework establishes a quantitative benchmark for the systematic optimization of GNR transfer strategies.

Abstract

The transfer of aligned graphene nanoribbons from metallic growth surfaces to device-compatible platforms remains a central bottleneck for nanoribbon electronics. Here, we investigate step-edge passivation of vicinal Au(788) by chevron-GNRs as a strategy to improve the transfer of aligned 9-armchair graphene nanoribbons. Scanning tunneling microscopy reveals that chevron-GNRs preferentially occupy step-edges, effectively acting as passivators that displace 9-AGNRs toward terrace centers, thereby altering their local growth configuration. To quantify transfer performance, we establish an automated large-area Raman analysis framework that enables pixel-wise classification based on the G mode and the radial breathing–like mode (RBLM). This approach provides a robust and scalable metric for assessing both transfer coverage and local ribbon integrity across macroscopic areas. Raman mapping uncovers strongly inhomogeneous transfer, characterized by extended regions with no detectable GNR signal and pronounced spatial variability in the RBLM-to-G intensity ratio. Transfer quality varies substantially across the sample series, with only a single high-yield outlier and most samples remaining well below 100% transfer yield. These results demonstrate that while chevron passivation locally modifies the growth configuration of 9-AGNRs on Au(788), it does not yet yield reproducible, high-quality transfer of intact aligned ribbons. The presented Raman-based analysis framework establishes a quantitative benchmark for the systematic optimization of GNR transfer strategies.

Abstract

The transfer of aligned graphene nanoribbons from metallic growth surfaces to device-compatible platforms remains a central bottleneck for nanoribbon electronics. Here, we investigate step-edge passivation of vicinal Au(788) by chevron-GNRs as a strategy to improve the transfer of aligned 9-armchair graphene nanoribbons. Scanning tunneling microscopy reveals that chevron-GNRs preferentially occupy step-edges, effectively acting as passivators that displace 9-AGNRs toward terrace centers, thereby altering their local growth configuration. To quantify transfer performance, we establish an automated large-area Raman analysis framework that enables pixel-wise classification based on the G mode and the radial breathing–like mode (RBLM). This approach provides a robust and scalable metric for assessing both transfer coverage and local ribbon integrity across macroscopic areas. Raman mapping uncovers strongly inhomogeneous transfer, characterized by extended regions with no detectable GNR signal and pronounced spatial variability in the RBLM-to-G intensity ratio. Transfer quality varies substantially across the sample series, with only a single high-yield outlier and most samples remaining well below 100% transfer yield. These results demonstrate that while chevron passivation locally modifies the growth configuration of 9-AGNRs on Au(788), it does not yet yield reproducible, high-quality transfer of intact aligned ribbons. The presented Raman-based analysis framework establishes a quantitative benchmark for the systematic optimization of GNR transfer strategies.