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Record W1967510505 · doi:10.1117/12.912714

Binding sites of water molecules on GaN (100) surface: DFT calculations

2011· article· en· W1967510505 on OpenAlexafffund
Dongping Liu, Yu Zhu, Hong Guo, Hakima Abou‐Rachid, Mounir Jaidann, Zetian Mi

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsDefence Research and Development CanadaNanoacademic TechnologiesMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaDefence Research and Development Canada
KeywordsWurtzite crystal structureDensity functional theoryDissociation (chemistry)MoleculeHydrogenAdsorptionHydrogen atomChemistryMaterials scienceGalliumChemical physicsCrystallographyPhysical chemistryComputational chemistryGroup (periodic table)

Abstract

fetched live from OpenAlex

Recently, preliminary experimental results of solar-to-hydrogen generation by wafer level InGaN nanowires were reported [Z. Mi et al. Nano Lett., 2011, 11 (6), pp 2353-2357]. In the present paper we report a theoretical investigation on the dissociation process of water molecules on wurtzite GaN (100) surface (M-Plane) using the density functional theory (DFT). We calculated the structure and energetic of the water adsorption, reaction barrier energies and pathway for water dissociation. The results suggest that the absorption of H2O is more favorable near Gallium atoms than near Nitrogen atoms and we determined the likely binding sites of water molecules on GaN (100) surface. We also analyzed a model for hydrogen evolution reaction on GaN (100) that involves three steps, where a water molecule first dissociates into a hydrogen atom plus the OH group, followed by the dissociation of the hydroxyl group, and finally the two hydrogen atoms recombine to form molecular hydrogen. For these reactions, the atomic positions and the reaction barriers were determined.

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)0.000
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 score0.299
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.020
GPT teacher head0.235
Teacher spread0.215 · 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

Citations1
Published2011
Admission routes2
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicGaN-based semiconductor devices and materialsFrench-language works237,207