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Record W4392588509 · doi:10.1002/aenm.202303635

Modulating Pt‐N/O Bonds on Co‐doped WO<sub>3</sub> for Acid Electrocatalytic Hydrogen Evolution with Over 2000 h Operation

2024· article· en· W4392588509 on OpenAlexaff
Hengyi Chen, Jidong Yu, Lijia Liu, Rui‐Ting Gao, Zehua Gao, Yang Yang, Zhiqiang Chen, Sibo Zhan, Xianhu Liu, Xueyuan Zhang, Hongliang Dong, Limin Wu, Lei Wang

Bibliographic record

VenueAdvanced Energy Materials · 2024
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsWestern University
FundersNational Science and Technology Major Project
KeywordsMaterials scienceDopingElectrocatalystHydrogen bondHydrogenInorganic chemistryPhysical chemistryElectrochemistryElectrodeOrganic chemistryOptoelectronicsMoleculeChemistry

Abstract

fetched live from OpenAlex

Abstract Developing durable electrocatalysts with high performance for hydrogen evolution reaction (HER) in acid conditions is of prime challenge for hydrogen production. Durability is of important prerequisite for catalyst application. Herein, this work constructs the Co‐doped WO 3 loaded with Pt nanoparticles under ammonia treatment (Pt/N‐CoWO 3 ) with the Pt‐N/O‐W interaction, which shows excellent activity and stability for acidic hydrogen production at industrial current density. The electronic structure of Pt species is modulated with enhanced Pt‐N/O bonding by Co doping, hence reinforcing the metal‐support interaction, and greatly enhancing the stability of the catalyst under high current density in acidic media. The resultant Pt/N‐CoWO 3 catalyst exhibits the overpotentials of only 94 and 108 mV at high current densities of 1 and 2 A cm −2 , respectively. More impressively, Pt/N‐CoWO 3 delivers a record operation for acid electrocatalytic hydrogen evolution over 2000 h at 1 A cm −2 , denoting its potential for catalyst applications at the industrial current density. This work opens a new avenue for developing Pt‐loading acidic HER catalysts for long‐term operation at ampere‐level current densities in acid conditions.

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 categoriesMeta-epidemiology (narrow)
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 score0.119
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.006
GPT teacher head0.228
Teacher spread0.222 · 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

Citations44
Published2024
Admission routes1
Has abstractyes

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