Effects of Different Hydrogenation Regimes on Mechanical Properties of h-BN: A Reactive Force Field Study
Bibliographic record
Abstract
This article describes molecular dynamics based simulations, which were performed to investigate the effects of different hydrogenation regimes on the mechanical properties of boron nitride nanosheets (h-BN). The reaction force field (ReaxFF) was used as the interatomic potential to capture atomistic interactions. Separate atomistic models were developed for pristine, semihydrogenated (hydrogen is attached either to boron or nitrogen) and fully hydrogenated h-BN (hydrogen is attached to both boron and nitrogen). The radial distribution function was used to study the structural integrity and stability of both pristine and hydrogenated structures. The simulations predicted an improvement in stability and integrity of the atomistic structures under the influence of hydrogenation compared to pristine h-BN. The semihydrogenated structure in which hydrogen was attached only to nitrogen was found to be the least stable configuration, while the fully hydrogenated structure was the most stable. Furthermore, the selective hydrogenation of h-BN nanosheets was studied with respect to tailoring the mechanical behavior of h-BN nanosheets. With applied strain the hydrogen atom shifts its role from hydrogen bond acceptor to donor, which increases the toughness of semihydrogenated h-BN nanosheets.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".