DFT‐Assisted Approach to Low‐Temperature Graphene Growth on Sapphire
Bibliographic record
Abstract
Abstract Controlling the direct growth of 2D materials onto dielectric substrates is considered as a key requirement for integrating these ultrathin functional materials into existing technology platforms. Here, a combined experimental and theoretical approach is presented to unravel the mechanism of low‐temperature graphene growth on sapphire, a dielectric substrate widely used in the semiconductor industry. A clear dependence of the graphene growth rate on the crystal facet is found, with the highest growth rate for a ‐plane and ca ‐plane, and the lowest for r ‐plane sapphire. Density functional theory calculations reveal that the coordination environment of surface oxygen ions governs carbon adsorption energetics: lower coordinated oxygen sites on the a ‐plane markedly enhance carbon atom binding, driving nucleation and growth, while higher coordinated oxygen sites on the r‐ plane hinder adsorption and growth. Guided by these insights, it is demonstrated that tailoring substrate termination yields controllable graphene formation at temperatures as low as 670 °C and sheet resistances down to 1.65 kΩ □ −1 . This approach may establish a universal design principle to guide low‐temperature growth of 2D materials on non‐catalytic dielectrics.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".