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Record W2333779117 · doi:10.1149/1.3502342

Nanoporous Carbon-Supported Fe/Co-N Electrocatalyst for Oxygen Reduction Reaction in PEM Fuel Cells

2010· article· en· W2333779117 on OpenAlexafffund
Ja‐Yeon Choi, Ryan Hsu, Zhongwei Chen

Bibliographic record

VenueECS Transactions · 2010
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrocatalystProton exchange membrane fuel cellNanoporousCatalysisCarbon blackCarbon fibersOxygenChemical engineeringSelectivityInorganic chemistryMaterials scienceChemistryElectrodeComposite numberElectrochemistryNanotechnologyOrganic chemistryComposite materialPhysical chemistry

Abstract

fetched live from OpenAlex

As a substitute for high-cost platinum based catalysts, nonprecious catalysts for the oxygen reduction reaction were synthesized by deposition of Fe/Co-N composite onto two different nanoporous carbon supports, Ketjen Black EC300J and EC600JD, using ethylenediamine as a nitrogen precursor. Rotating ring disk electrode measurements were used to investigate the ORR activity, and the results obtained from EC600JD based catalyst, showed improved onset and half-wave potentials and superior selectivity than that of the EC300J. Similarly, the catalyst showed good performance in the hydrogen-oxygen PEMFC, being able to produce 0.37 A/cm2 with a maximum power density of 0.44 W/cm2 at a cell voltage of 0.6 V. A fuel cell life test at a voltage of 0.40 V demonstrated promising stability up to 100 h. These results suggest that a higher pore volume and surface area of the carbon support could lead to higher nitrogen content, providing more active sites for ORR.

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 score0.042
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.225
Teacher spread0.218 · 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

Citations5
Published2010
Admission routes2
Has abstractyes

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