First-principles calculation of the stopping power of protons in hexagonal boron nitride with different stacking sequences
Bibliographic record
Abstract
This study employs the real-time time-dependent density functional theory method to conduct an in-depth investigation of the energy dissipation mechanism of proton in hexagonal boron nitride (h-BN) materials. By calculating the stopping power to quantify the energy dissipation of proton, we find that the energy dissipation in h-BN is primarily dependent on electron excitation, with relatively minor nuclear energy loss. Furthermore, we analyze the micro-dynamic behavior of radiation particles in detail by tracking the forces exerted on proton and the charge transfer process. Finally, by simulating the energy transfer of proton through bilayer h-BN under different stacking sequences, we explore the influence of stacking structures on stopping power. The results indicate that asymmetric stacking structures exhibit slightly higher stopping power, which may become a potential stacking configuration for radiation protection. The findings of this study not only provide a new perspective for understanding the radiation response of h-BN materials but also lay an important theoretical foundation for the long-term radiation protection design of h-BN materials.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".