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Record W4385381082 · doi:10.1007/s12274-023-5956-8

Enriched electrophilic oxygen species facilitate acidic oxygen evolution on Ru-Mo binary oxide catalysts

2023· article· en· W4385381082 on OpenAlexaff
Yaya Wang, Yunzhou Wen, Yumeng Cheng, Xinhong Chen, Mengjiao Zhuansun, Tongbao Wang, Jun Li, Debora Meira, Huarui Sun, Jun Wei, Jia Zhou, Yuhang Wang, Sisi He

Bibliographic record

VenueNano Research · 2023
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsCanadian Light Source (Canada)
Fundersnot available
KeywordsOxygen evolutionOverpotentialTafel equationCatalysisChemistryOxideWater splittingInorganic chemistryHydrogen productionElectrolyteOxygenElectrocatalystChemical engineeringElectrochemistryPhysical chemistryElectrodeOrganic chemistryPhotocatalysis

Abstract

fetched live from OpenAlex

The polymer electrolyte membrane (PEM) electrolyzers are burdened with costly iridium (Ir)-based catalysts and high operation overpotentials for the oxygen evolution reaction (OER). The development of earth-abundant, highly active, and durable electrocatalysts to replace Ir is a critical step in reducing the cost of green hydrogen production. Here we develop a Ru 5 Mo 4 O x binary oxide catalyst that exhibits high activity and stability in acidic OER. The electron-withdrawing property of Mo enriches the electrophilic surface oxygen species, which promotes acidic OER to proceed via the adsorbate evolution pathway. As a result, we achieve a 189 mV overpotential at 10 mA·cm −2 and a Tafel slope of 48.8 mV·dec −1 . Our catalyst demonstrates a substantial 18-fold increase in intrinsic activity, as evaluated by turnover frequency, compared to commercially available RuO 2 and IrO 2 catalysts. Moreover, we report a stable OER operation at 10 mA·cm −2 for 100 h with a low degradation rate of 2.05 mV·h −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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.191
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.012

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 head0.059
GPT teacher head0.306
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.

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

Citations20
Published2023
Admission routes1
Has abstractyes

Explore more

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