Conjugated <i>π</i> electron engineering of generalized stacking fault in graphene and <i>h</i> -BN
Bibliographic record
Abstract
Generalized-stacking-fault energy (GSFE) serves as an important metric that prescribes dislocation behaviors in materials. In this paper, utilizing first-principle calculations and chemical bonding analysis, we studied the behaviors of generalized stacking fault in graphene and h-BN. It has been shown that the π bond formation plays a critical role in the existence of metastable stacking fault (MSF) in graphene and h-BN lattice along certain slip directions. Chemical functionalization was then proposed as an effective means to engineer the π bond, and subsequently MSF along dislocation slips within graphene and h-BN. Taking hydrogenation as a representative functionalization method, we demonstrated that, with the preferential adsorption of hydrogen along the slip line, π electrons along the slip would be saturated by adsorbed hydrogen atoms, leading to the moderation or elimination of MSF. Our study elucidates the atomic mechanism of MSF formation in graphene-like materials, and more generally, provides important insights towards predictive tuning of mechanic properties in two-dimensional nanomaterials.
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 | 0.000 |
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 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".