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Record W2898864509 · doi:10.1149/200231.0089pv

Non-Noble Metal Catalysts for PEM Oxygen Reduction based on Sol Gel Derived Cobalt Nigrogen Compounds

2002· article· en· W2898864509 on OpenAlexaff
V. I. Birss

Bibliographic record

VenueECS Proceedings Volumes · 2002
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCatalysisCobaltCarbon fibersNoble metalInorganic chemistryMaterials scienceAnodeProton exchange membrane fuel cellAdsorptionChemical engineeringCathodeOxideMetalChemistryComposite numberElectrodeOrganic chemistryComposite materialMetallurgy

Abstract

fetched live from OpenAlex

The majority of power loss in PEM fuel cells occurs at the cathode, due to the sluggish kinetics of the oxygen reduction reaction (ORR). Much higher loadings of Pt at the cathode, as compared to the anode, must be used to compensate for this, resulting in high costs. There have been numerous efforts to develop non-noble metal catalysts for the ORR, including Co-N4 chelates, normally arising from porphyrin precursors and which have been shown to improve in activity with heat treatment. In this work, Co oxide sol-gel syntheses, known to yield nanoparticulate composite materials, have been modified by the incorporation of carbon and nitrogen in the form of ethylene diamine. These new catalysts have demonstrated very good ORR activity in acidic solutions after adsorption on carbon and subsequent heat treatment, with a maximum in performance and minimum in H2O2 generation at 700 °C. Catalyst activity was also found to increase with an increase in the concentration of the catalyst on the carbon powder and with increased loadings of the catalyst.

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 categoriesMeta-epidemiology (narrow)
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 score0.140
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.014
GPT teacher head0.217
Teacher spread0.203 · 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.

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

Citations0
Published2002
Admission routes1
Has abstractyes

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