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Record W1994141605 · doi:10.1149/1.3076188

Carbon-Supported IrM (M=V, Mn, Fe, Co, and Ni) Binary Alloys as Anode Catalysts for Polymer Electrolyte Fuel Cells

2009· article· en· W1994141605 on OpenAlexfundno aff
Jinli Qiao, Bing Li, Jianxin Ma

Bibliographic record

VenueJournal of The Electrochemical Society · 2009
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsnot available
FundersNational Research Council CanadaTongji UniversityMinistry of Science and Technology of the People's Republic of China
KeywordsChronoamperometryCatalysisCyclic voltammetryElectrolyteAnodeMaterials scienceLinear sweep voltammetryCarbon fibersElectrochemistryPlatinumInorganic chemistryHydrogenProton exchange membrane fuel cellChemical engineeringElectrodeChemistryComposite materialOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Carbon-supported binary IrM (where , Mn, Fe, Co, and Ni as the second active component) alloys as a substitute for platinum were viewed as the anode catalysts in polymer electrolyte membrane fuel cells. The resulting and of 50:50 ratio, heat-treated at in argon atmosphere, exhibited promising catalytic performance in terms of the hydrogen oxidation reaction and results in mass activity and specific activity at almost the same level and even a little higher as compared with 20% at . These carbon-supported catalysts showed particle sizes ranging from . Results of electrochemical characterizations, performed using a rotating disk electrode by cyclic voltammetry, linear sweep voltammetry, and chronoamperometry with emphasis on the activity of the catalyst, are presented in .

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 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.221
Teacher spread0.216 · 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

Citations14
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJournal of The Electrochemical SocietySame topicElectrocatalysts for Energy ConversionFrench-language works237,207