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Record W4410700599 · doi:10.1002/ange.202508239

Anomalous pH‐Dependence of Ru for Hydrogen Electrochemistry

2025· article· en· W4410700599 on OpenAlexaff
Yanyan Fang, Cong Wei, Tianshu Liu, Chongyang Tang, Zhaohui Liu, Xuanwei Yin, Bo Liu, Zhangyan Mu, Zenan Bian, Junxin Xiao, Peng Chi, Mengning Ding, Changzheng Wu, Gongming Wang

Bibliographic record

VenueAngewandte Chemie · 2025
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsMinistry of Education and Child Care
FundersFundamental Research Funds for the Central UniversitiesNational Key Research and Development Program of ChinaNatural Science Foundation of Anhui ProvinceAnhui Provincial Development and Reform Commission
KeywordsElectrochemistryChemistryHydrogenInorganic chemistryPhysical chemistryElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The essence of the classic pH‐dependent catalytic behavior of Pt toward hydrogen electrochemistry has been debated for decades. In stark contrast, Ru represents a unique exception by exhibiting intrinsically anomalous pH dependence among Pt‐group metals, significantly challenging the universal validity of the existing mechanistic system derived from classic pH effects. To decode the chemical essence beneath the unique anomalous pH dependence, refined multidimensional structural characterizations and electrical transport spectroscopic studies reveal that the electron delocalization on Ru surface enables strong oxophilicity and unique pH‐dependent coverage of OH*, essentially different from the H*‐dominated Pt surface. The enriched OH* breaks the classic pH‐dependent rule by forming hydrogen bond‐regulated interfacial water structures, thus creating a unique chemical environment for alkaline hydrogen catalysis. By clearly decoding the anomalous pH‐dependent behavior of Ru, our work provides new chances to improve the physicochemical model for re‐understanding hydrogen electrochemistry.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.249
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 teacher head, 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

Citations3
Published2025
Admission routes1
Has abstractyes

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