MétaCan
Menu
Back to cohort
Record W3205935355 · doi:10.1038/s41467-021-26307-7

Boride-derived oxygen-evolution catalysts

2021· article· en· W3205935355 on OpenAlexafffund
Ning Wang, Aoni Xu, Pengfei Ou, Sung‐Fu Hung, Adnan Ozden, Ying‐Rui Lu, Jehad Abed, Ziyun Wang, Yushan Yan, Meng‐Jia Sun, Yujian Xia, Mei Han, Jingrui Han, Kaili Yao, Fengyi Wu, Pei‐Hsuan Chen, Alberto Vomiero, Ali Seifitokaldani, Xuhui Sun, David Sinton, Yongchang Liu, Edward H. Sargent, Hongyan Liang

Bibliographic record

VenueNature Communications · 2021
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsMcGill UniversityUniversity of Toronto
FundersNatural Resources CanadaUniversity of TorontoNatural Sciences and Engineering Research Council of CanadaNational Synchrotron Radiation Research CenterNational Natural Science Foundation of ChinaGovernment of Ontario
KeywordsOverpotentialBorideBoronCatalysisMaterials scienceElectrolyteOxygen evolutionOxideInorganic chemistryChemical engineeringMetalCurrent densityElectrodeElectrochemistryMetallurgyChemistryPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Metal borides/borates have been considered promising as oxygen evolution reaction catalysts; however, to date, there is a dearth of evidence of long-term stability at practical current densities. Here we report a phase composition modulation approach to fabricate effective borides/borates-based catalysts. We find that metal borides in-situ formed metal borates are responsible for their high activity. This knowledge prompts us to synthesize NiFe-Boride, and to use it as a templating precursor to form an active NiFe-Borate catalyst. This boride-derived oxide catalyzes oxygen evolution with an overpotential of 167 mV at 10 mA/cm 2 in 1 M KOH electrolyte and requires a record-low overpotential of 460 mV to maintain water splitting performance for over 400 h at current density of 1 A/cm 2 . We couple the catalyst with CO reduction in an alkaline membrane electrode assembly electrolyser, reporting stable C 2 H 4 electrosynthesis at current density 200 mA/cm 2 for over 80 h.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.724
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.011
GPT teacher head0.258
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 designNot applicable
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

Citations154
Published2021
Admission routes2
Has abstractyes

Explore more

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