Bottom-up Synthesis of Sub-10 Nm Semiconducting Graphene Nanoribbons with Smooth Armchair Edges on Ge(001)
Bibliographic record
Abstract
The rational synthesis of graphene nanoribbons that are semiconducting with sub-10 nm width, controlled crystallographic orientation, and well-defined edges on non-metallic substrates has been a significant challenge. The growth of nanoribbons on metal substrates precludes their direct use in semiconducting electronics due to the conducting substrate, and the direct synthesis of nanoribbons in solution is complicated by challenges of their post-synthetic assembly. In this talk, we demonstrate the scalable synthesis of graphene nanoribbons from the bottom-up via chemical vapor deposition (CVD) on Ge(001). Low energy electron diffraction (LEED) and scanning tunneling microscopy (STM) show that the ribbons are self-orienting ±2.9° from the Ge[110] directions and are self-defining. The nanoribbons have predominately smooth armchair edges that give rise to electron interference patterns that are indicative of the high quality of the edges. By tuning the precursor flux, growth time, and growth temperature, the ribbon anisotropy and growth kinetics can be tailored to yield ribbons with controlled width < 10 nm and aspect ratio > 60. Compared to previous reports of the growth of low aspect ratio crystals of graphene on Ge, we find that in order to realize high aspect ratio nanoribbons, it is critical to operate in a regime in which the growth rate is especially slow, on the order of 5 nm/h for growth in the width direction. Scanning tunneling spectroscopy shows that the ribbons have electronic structures that are consistent with semiconductors with bandgaps that are > 500 meV and that vary inversely with width. This work is important because unlike continuous two-dimensional graphene, which is semimetallic, one-dimensional graphene nanoribbons can be semiconducting, allowing for the substantial modulation of their conductance and enabling their application in semiconductor logic, optoelectronics, photonics, and sensors. Moreover, the direct synthesis of ultranarrow and smooth graphene nanoribbons on Ge demonstrated here provides a scalable, high throughput pathway for integrating semiconducting graphene directly on conventional large-area semiconductor wafer platforms that are compatible with planar processing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".