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Record W2520995750 · doi:10.1021/acs.jpcc.6b05812

Effects of Different Hydrogenation Regimes on Mechanical Properties of h-BN: A Reactive Force Field Study

2016· article· en· W2520995750 on OpenAlexaff
Rajesh Kumar, Pierre Mertiny, Avinash Parashar

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2016
Typearticle
Languageen
FieldMaterials Science
TopicBoron and Carbon Nanomaterials Research
Canadian institutionsUniversity of Alberta
FundersIndian Institute of Technology RoorkeeDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsReaxFFMaterials scienceMolecular dynamicsBoron nitrideHydrogenForce field (fiction)NitrogenBoronChemical physicsHydrogen storageChemical engineeringNanotechnologyComputational chemistryInteratomic potentialComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

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.

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 score0.001
Threshold uncertainty score0.156

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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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

Citations46
Published2016
Admission routes1
Has abstractyes

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