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Record W2916601208 · doi:10.1109/mnano.2019.2891370

Molecular Beam Epitaxy of III-Nitride Nanowires: Emerging Applications From Deep-Ultraviolet Light Emitters and Micro-LEDs to Artificial Photosynthesis

2019· article· en· W2916601208 on OpenAlexafffund
Songrui Zhao, Renjie Wang, Sheng Chu, Zetian Mi

Bibliographic record

VenueIEEE Nanotechnology Magazine · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsMolecular beam epitaxyMaterials scienceLight-emitting diodePhotonicsNanowireUltravioletOptoelectronicsNitrideArtificial photosynthesisNanotechnologyUltraviolet lightEpitaxyChemistryLayer (electronics)Photocatalysis

Abstract

fetched live from OpenAlex

Low-dimensional nanowires have received increased interest as building blocks for electronic and photonic devices. In this article, we review the recent progress made on III -nitridenanowire photonic devices by molecular beam epitaxy (MBE), with a focus on micro-LED s, deep-ultraviolet (UV)-light emitters, and solar fuel and artificial photosynthesis devices. The challenges and prospects of III -nitride nanowires for these applications are also discussed.

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: none
Teacher disagreement score0.000
Threshold uncertainty score0.002

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)0.0000.000

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 head0.005
GPT teacher head0.220
Teacher spread0.214 · 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

Citations18
Published2019
Admission routes2
Has abstractyes

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