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Record W2334263495 · doi:10.1149/1.3701967

Recent Advances in Non-Precious Metal Electrocatalysts for Oxygen Reduction in PEM Fuel Cells

2012· article· en· W2334263495 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueECS Transactions · 2012
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersMinistère du Développement Économique, de l’Innovation et de l’Exportation
KeywordsProton exchange membrane fuel cellCathodeElectrolyteMaterials sciencePower densityCatalysisDurabilityChemical engineeringNanotechnologyFuel cellsElectrodePower (physics)Electrical engineeringChemistryEngineeringComposite material

Abstract

fetched live from OpenAlex

Polymer electrolyte membrane fuel cells (PEMFC) are electrical power generators for a wide range of possible applications, but are still considered too expensive for many. To reduce their cost, much research has focused on replacing the expensive Pt-based electrocatalysts in PEMFCs with a lower-cost alternative. Fe-based cathode catalysts are a promising alternative. To compete with Pt-based cathode catalysts, non-precious metal catalysts must meet three key criteria: have high catalytic activity, allow for high power density at meaningful cell voltages and have adequate operational stability/durability. Over the last three years our research group at INRS-EMT has made significant progress in achieving these criteria including a cathode catalyst with a power density of 0.75W cm−2 at 0.6V under H2/O2, a meaningful voltage for PEMFC operation, comparable with that of a commercial Pt-based cathode tested under identical 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.

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 score0.331
Threshold uncertainty score0.505

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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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