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Band engineering of GaSbN alloy for solar fuel applications

2017· article· en· W2742810954 on OpenAlex
Qing Shi, Ying‐Chih Chen, Faqrul A. Chowdhury, Zetian Mi, Vincent Michaud-Rioux, Hong Guo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePhysical Review Materials · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceDopingSemiconductorImpurityBand gapSolar fuelOptoelectronicsChemical physicsNanotechnologyCondensed matter physicsEngineering physicsPhysicsChemistryPhotocatalysis

Abstract

fetched live from OpenAlex

III-nitride nanostructures possess ideal attributes for harvesting solar energy and generating solar fuel through natural water splitting. The most basic requirement of the latter is to engineer the band gap of the semiconductor to straddle the redox potential of water molecules. To this end, using first principles method we predict that GaN engineered with Sb doping at the dilute limit of 0.3% and/or slightly less is suitable for photochemical water splitting applications. The valence band edge is very significantly enhanced by dilute Sb doping while the conduction band edge is not. The microscopic physics behind the strong band bowing by such a small impurity concentration, not seen in other III-V semiconductors, is revealed by investigating the quantum interaction between Sb impurity states and the host GaN states. The dilute doping limit dictates very large systems to be calculated at the hybrid exchange-correlation level which is made possible by our newly developed first principles approach.

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.

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 score0.056
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.022
GPT teacher head0.308
Teacher spread0.287 · 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