MétaCan
Menu
Back to cohort
Record W2557137774 · doi:10.1039/c6ra19924k

Stabilization of platinum–nickel alloy nanoparticles with a sulfur-doped graphene support in polymer electrolyte membrane fuel cells

2016· article· en· W2557137774 on OpenAlexafffund
Calvin Xu, Md Ariful Hoque, Gordon Chiu, Teresa Sung, Zhongwei Chen

Bibliographic record

VenueRSC Advances · 2016
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsNational Institute for NanotechnologyUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGrapheneElectrolyteMaterials scienceSulfurNanoparticleNickelAlloyMembranePolymerPlatinumDopingPlatinum nanoparticlesFuel cellsChemical engineeringInorganic chemistryNanotechnologyChemistryElectrodeMetallurgyCatalysisOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Post-heat treatment of dealloyed Pt–Ni nanoparticles on sulfur-doped graphene for PEM fuel cell cathode catalysis exhibit greatly improved activity and electrochemically active surface area retention over Pt/C in half-cell 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 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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.211
Teacher spread0.205 · 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

Citations7
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueRSC AdvancesSame topicElectrocatalysts for Energy ConversionFrench-language works237,207