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Record W3200184329 · doi:10.1063/5.0062323

A novel two-dimensional boron–carbon–nitride (BCN) monolayer: A first-principles insight

2021· article· en· W3200184329 on OpenAlexaff
A. Bafekry, Mosayeb Naseri, Mohamed M. Fadlallah, I. Abdolhosseini Sarsari, Mehrdad Faraji, Abbas Bagheri Khatibani, Mitra Ghergherehchi, D. Gogova

Bibliographic record

VenueJournal of Applied Physics · 2021
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsUniversity of Calgary
FundersNational Research Foundation of Korea
KeywordsMonolayerPhononMaterials scienceDensity functional theoryBoron nitrideBand gapSemiconductorCondensed matter physicsWide-bandgap semiconductorHybrid functionalMolecular physicsChemical physicsOptoelectronicsComputational chemistryNanotechnologyChemistryPhysics

Abstract

fetched live from OpenAlex

The optical, electronic, and structural properties of a theoretically predicted new boron–carbon–nitride (BCN) two-dimensional monolayer have been explored using density functional theory calculations. The phonon dispersion, molecular dynamics simulation, the cohesive energy, and the Born criteria of elastic constant calculations of the BCN monolayer confirm its stability. The phonon spectrum illustrates an out-of-plane flexure mode with quadratic dispersion in the long-wavelength limit. The BCN monolayer is a semiconductor with a direct bandgap of 0.9 (1.63) eV determined via the Perdew–Burke–Ernzerhof (Heyd–Scuseria–Ernzerhof) functional. The same electron and hole effective masses and mobility values indicate the high recombination rate of electrons and holes. Meanwhile, the BCN monolayer can absorb ultraviolet radiation more effectively than visible light. Due to its interesting physical properties, the novel BCN monolayer could be a rather good candidate material for electro-optical applications.

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 score0.057
Threshold uncertainty score0.713

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 head0.027
GPT teacher head0.249
Teacher spread0.222 · 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

Citations52
Published2021
Admission routes1
Has abstractyes

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