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Record W3186308335 · doi:10.1149/ma2021-0115707mtgabs

(Invited) Surface Photovoltage Spectroscopy Observes Junctions and Carrier Separation in Gallium Nitride Nanowire Arrays for Overall Water-Splitting

2021· article· en· W3186308335 on OpenAlexaff
Frank E. Osterloh, Rachel M. Doughty, Faqrul A. Chowdhury, Zetian Mi

Bibliographic record

VenueECS Meeting Abstracts · 2021
Typearticle
Languageen
FieldMaterials Science
TopicGa2O3 and related materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsSurface photovoltageNanowireMaterials scienceWater splittingGallium nitrideOptoelectronicsSemiconductorDepletion regionWaferBand gapCarrier lifetimeSpectroscopyNitrideCharge carrierNanotechnologySiliconLayer (electronics)ChemistryPhotocatalysisCatalysis

Abstract

fetched live from OpenAlex

Gallium nitride (GaN) nanowire arrays on silicon are able to drive the overall water-splitting reaction with up to 3.3% solar-to-hydrogen efficiency. Photochemical charge separation is key to the operation of these devices, but details are difficult to observe experimentally because of the number of components and interfaces. Here we use surface photovoltage spectroscopy (SPS) to study charge transfer in i- and n- and p-GaN nanowire arrays on n + -Si wafers in the presence and absence of Rh/Cr 2 O 3 co-catalysts. The effect of the space charge layer and sub-bandgap defects on majority and minority carrier transport can be clearly observed and estimates of the built-in potential of the junctions can be made. Transient illumination of the p-GaN/n + -Si junction generates up to -1.4 V surface photovoltage by carrier separation along the GaN nanowire axis. This process is central to the overall water-splitting function of n + -Si/p-GaN/Rh/Cr 2 O 3 nanowire array. These results improve our understanding of photochemical charge transfer and separation in group III-V semiconductor nanostructures for the conversion of solar energy into fuels. Figure 1

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)0.001
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 score0.015
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.019
GPT teacher head0.266
Teacher spread0.247 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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