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Record W4407125388 · doi:10.1088/1361-648x/adb233

First-principles calculation of the stopping power of protons in hexagonal boron nitride with different stacking sequences

2025· article· en· W4407125388 on OpenAlexaff
Bin Zhang, Tao Ying, Weiqi Li, Chaoyang Xing, Jianqun Yang, Xingji Li

Bibliographic record

VenueJournal of Physics Condensed Matter · 2025
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsAdvanced Micro Devices (Canada)
FundersNatural Science Foundation of Heilongjiang ProvinceNational Natural Science Foundation of China
KeywordsStackingStopping powerDensity functional theoryElectronMolecular physicsProtonAtomic physicsMaterials scienceCondensed matter physicsChemistryIonComputational chemistryPhysicsNuclear physics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.266
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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