MétaCan
Menu
Back to cohort
Record W2770867421 · doi:10.1038/nchem.2886

Theory-driven design of high-valence metal sites for water oxidation confirmed using in situ soft X-ray absorption

2017· article· en· W2770867421 on OpenAlexafffund
X. R. Zheng, Phil De Luna, Yufeng Liang, Riccardo Comin, Oleksandr Voznyy, Lili Han, F. Pelayo Garcı́a de Arquer, Min Liu, Cao‐Thang Dinh, Tom Regier, James J. Dynes, Sisi He, Huolin L. Xin, Huisheng Peng, David Prendergast, Xi‐Wen Du, Edward H. Sargent

Bibliographic record

VenueNature Chemistry · 2017
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsCanadian Light Source (Canada)University of Toronto
FundersBeijing Synchrotron Radiation FacilityWestern Economic Diversification CanadaBasic Energy SciencesCanadian Light SourceNatural Sciences and Engineering Research Council of CanadaOffice of ScienceCanadian Institutes of Health ResearchChina Scholarship CouncilNational Key Research and Development Program of ChinaNational Research Council CanadaUniversity of TorontoNational Energy Research Scientific Computing CenterNational Natural Science Foundation of ChinaCanadian Institute for Advanced ResearchLawrence Berkeley National LaboratoryScience and Technology Commission of Shanghai MunicipalityBrookhaven National LaboratoryU.S. Department of Energy
KeywordsChemistryOverpotentialValence (chemistry)Oxidizing agentCatalysisOxygen evolutionDensity functional theoryX-ray absorption spectroscopyIn situTransition metalChemical engineeringWater splittingX-ray photoelectron spectroscopyMetalAbsorption spectroscopyInorganic chemistryElectrochemistryElectrodePhysical chemistryPhotocatalysisComputational chemistryOrganic chemistry

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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 score0.003
Threshold uncertainty score0.008

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.255
Teacher spread0.241 · 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

Citations653
Published2017
Admission routes2
Has abstractno

Explore more

Same venueNature ChemistrySame topicElectrocatalysts for Energy ConversionFrench-language works237,207