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Record W4382653009 · doi:10.1139/cjp-2023-0072

Tailoring hydrogen adsorption and desorption properties of Li-doped SV (single vacancy) monolayer <i>h</i>-BN systems using ab initio calculations

2023· article· en· W4382653009 on OpenAlexvenueno aff
Kaneez Fatima, Muhammad Rafique, Amir Mahmood Soomro, Mahesh Kumar

Bibliographic record

VenueCanadian Journal of Physics · 2023
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsnot available
Fundersnot available
KeywordsHydrogen storageMonolayerAb initioDesorptionDensity functional theoryHydrogenAdsorptionDopingVacancy defectPhysicsAtomic physicsPhysical chemistryAnalytical Chemistry (journal)Materials scienceNanotechnologyChemistryCondensed matter physicsOrganic chemistry

Abstract

fetched live from OpenAlex

This study uses density functional theory (DFT) technique to examine the hydrogen molecules (H 2 ) storage on Li-decorated h-BN monolayer. The results of DFT have proven that Li-doped h-BN system can hold up to 9H 2 with the adsorption energy lying in between −0.31 eV and −0.24 eV/H 2 at ambient condition. However, the calculated average adsorption energy for 9H 2 is −0.240 eV/H 2 with hydrogen storage capacity of 5.96 wt.%, which is according to the United States Department of Energy. Partial density of state was computed for each configuration to provide additional justifications for the H 2 storage on Li-doped h-BN monolayer. The hybridization shows a significant interaction between H 2 and Li atom, and most of their hybrid peaks were observed in the energy range from −7.5 to −1 eV. Moreover, the H 2 desorption simulations achieved via the ab initio molecular dynamics. The computed desorption temperature T D is 306 °K, which is a suitable operating temperature. Hence, our research demonstrates that Li-doped h-BN is a thermally stable and viable hydrogen storage material for hydrogen storage systems.

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 score0.009
Threshold uncertainty score0.715

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 communication0.0000.001
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 head0.083
GPT teacher head0.246
Teacher spread0.163 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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