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Record W2302970633 · doi:10.1126/science.aaf1525

Homogeneously dispersed multimetal oxygen-evolving catalysts

2016· article· en· W2302970633 on OpenAlexafffund
Bo Zhang, X. R. Zheng, Oleksandr Voznyy, Riccardo Comin, Michal Bajdich, Max García‐Melchor, Lili Han, Jixian Xu, Min Liu, Lirong Zheng, F. Pelayo Garcı́a de Arquer, Cao‐Thang Dinh, Fengjia Fan, Mingjian Yuan, Emre Yassitepe, Ning Chen, Tom Regier, Peng Fei Liu, Yuhang Li, Phil De Luna, Alyf Janmohamed, Huolin L. Xin, Hua Gui Yang, Aleksandra Vojvodić, Edward H. Sargent

Bibliographic record

VenueScience · 2016
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsCanadian Light Source (Canada)University of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaNatural Science Foundation of ShanghaiChina Scholarship CouncilU.S. Department of Energy
KeywordsOverpotentialCatalysisOxygen evolutionTungstenTransition metalCobaltDensity functional theoryValencyMetalChemistryInorganic chemistryChemical engineeringAdsorptionElectrolyteOxygenMaterials sciencePhysical chemistryMetallurgyComputational chemistryElectrochemistryOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

Earth-abundant first-row (3d) transition metal-based catalysts have been developed for the oxygen-evolution reaction (OER); however, they operate at overpotentials substantially above thermodynamic requirements. Density functional theory suggested that non-3d high-valency metals such as tungsten can modulate 3d metal oxides, providing near-optimal adsorption energies for OER intermediates. We developed a room-temperature synthesis to produce gelled oxyhydroxides materials with an atomically homogeneous metal distribution. These gelled FeCoW oxyhydroxides exhibit the lowest overpotential (191 millivolts) reported at 10 milliamperes per square centimeter in alkaline electrolyte. The catalyst shows no evidence of degradation after more than 500 hours of operation. X-ray absorption and computational studies reveal a synergistic interplay between tungsten, iron, and cobalt in producing a favorable local coordination environment and electronic structure that enhance the energetics for OER.

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 score0.004

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 science0.0010.000
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 head0.008
GPT teacher head0.225
Teacher spread0.217 · 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

Citations2,422
Published2016
Admission routes2
Has abstractyes

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