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Record W4367601130 · doi:10.1063/5.0142242

Epitaxial hexagonal boron nitride with high quantum efficiency

2023· article· en· W4367601130 on OpenAlexafffund
David Laleyan, Woncheol Lee, Ying Zhao, Yuanpeng Wu, Ping Wang, Jun Song, Emmanouil Kioupakis, Zetian Mi

Bibliographic record

VenueAPL Materials · 2023
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsMcGill University
FundersLawrence Berkeley National LaboratoryArmy Research OfficeBasic Energy SciencesNatural Sciences and Engineering Research Council of CanadaKwanjeong Educational FoundationNational Energy Research Scientific Computing CenterU.S. Department of EnergyMcGill UniversityW. M. Keck FoundationDivision of Materials ResearchOffice of ScienceUniversity of MichiganNational Science Foundation
KeywordsMaterials scienceOptoelectronicsBand gapWide-bandgap semiconductorExcitonQuantum efficiencyEpitaxyHexagonal boron nitrideSemiconductorUltravioletPhotonic crystalPhononPhotonicsNanotechnologyCondensed matter physics

Abstract

fetched live from OpenAlex

Two-dimensional (2D) hexagonal boron nitride (h-BN) is one of the few materials showing great promise for light emission in the far ultraviolet (UV)-C wavelength, which is more effective and safer in containing the transmission of microbial diseases than traditional UV light. In this report, we observed that h-BN, despite having an indirect energy bandgap, exhibits a remarkably high room-temperature quantum efficiency (∼60%), which is orders of magnitude higher than that of other indirect bandgap material, and is enabled by strong excitonic effects and efficient exciton-phonon interactions. This study offers a new approach for the design and development of far UV-C optoelectronic devices as well as quantum photonic devices employing 2D semiconductor active regions.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.013
GPT teacher head0.240
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2023
Admission routes2
Has abstractyes

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