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Record W2488258811 · doi:10.1063/1.4958964

An In0.5Ga0.5N nanowire photoanode for harvesting deep visible light photons

2016· article· en· W2488258811 on OpenAlexafffund
Shizhao Fan, Steffi Y. Woo, Srinivas Vanka, Gianluigi A. Botton, Zetian Mi

Bibliographic record

VenueAPL Materials · 2016
Typearticle
Languageen
FieldMaterials Science
TopicGa2O3 and related materials
Canadian institutionsMcMaster UniversityMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaClimate Change and Emissions Management CorporationMcMaster University
KeywordsPhotocurrentMaterials scienceNanowireOptoelectronicsIndiumMolecular beam epitaxySemiconductorPhotonOxideNanotechnologyEpitaxyOpticsLayer (electronics)

Abstract

fetched live from OpenAlex

III-nitride semiconductors hold tremendous promise for realizing high efficiency photoelectrodes. However, previously reported InGaN photoelectrodes generally exhibit very low photocurrent densities, due to the presence of extensive defects, dislocations, and indium phase separation. Here, we show that In0.5Ga0.5N nanowires with nearly homogeneous indium distribution can be achieved by plasma-assisted molecular beam epitaxy. Under AM1.5G one sun illumination, the InGaN nanowire photoanode exhibits a photocurrent density of 7.3 mA/cm2 at 1.2 V (vs. NHE) in 1M HBr. The incident-photon-to-current efficiency is above 10% at 650 nm, which is significantly higher than previously reported values of metal oxide photoelectrodes.

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 score0.001
Threshold uncertainty score0.003

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 head0.256
Teacher spread0.243 · 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
Published2016
Admission routes2
Has abstractyes

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