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Record W3124880858 · doi:10.1039/d0ee03431b

Reply to the ‘Comment on “Non-PGM electrocatalysts for PEM fuel cells: effect of fluorination on the activity and stability of a highly active NC_Ar + NH<sub>3</sub> catalyst”’ by Xi Yin, Edward F. Holby and Piotr Zelenay, <i>Energy Environ. Sci.</i>, 10.1039/D0EE02069A

2021· article· en· W3124880858 on OpenAlexafffund
Jean‐Pol Dodelet, Vassili P. Glibin, Gaixia Zhang, Ulrike I. Kramm, Régis Chenitz, François Vidal, Shuhui Sun, Marc Dubois

Bibliographic record

VenueEnergy & Environmental Science · 2021
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsWestern UniversityInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaInstitut national de la recherche scientifique
KeywordsCatalysisProton exchange membrane fuel cellElectrochemistryFuel cellsChemistryChemical engineeringActive siteMaterials scienceInorganic chemistryElectrodePhysical chemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

The fast decay in PEM fuel cells of a highly active, high performance, but unstable Fe/N/C catalyst like our NC_Ar + NH 3 follows a chemical, not an electrochemical, demetallation mechanism for its ORR active FeN 4 sites in the catalyst micropores.

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)0.006
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.051
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.027
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0030.005
Open science0.0040.002
Research integrity0.0510.049
Insufficient payload (model declined to judge)0.0090.011

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 head0.004
GPT teacher head0.190
Teacher spread0.186 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations10
Published2021
Admission routes2
Has abstractyes

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