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Record W2149523850 · doi:10.1149/1.3054278

Anode Poisoning Study in Direct Formic Acid Fuel Cells

2009· article· en· W2149523850 on OpenAlexaff
Yinghui Pan, Ruiming Zhang, Sharon L. Blair

Bibliographic record

VenueElectrochemical and Solid-State Letters · 2009
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsTekion (Canada)
Fundersnot available
KeywordsAnodeFormic acidCatalysisMaterials scienceHydrogenFuel cellsPalladiumInorganic chemistryElectrodeConstant currentChemical engineeringCurrent (fluid)ChemistryElectrical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Palladium is an active catalyst for the electro-oxidation of formic acid, making it attractive as an anode catalyst for direct formic acid fuel cells. However, it exhibits significant decay under constant current operation, making it unsuitable for use in any fuel cell application unless the catalyst can be regenerated. The buildup of anode poisons, including a means of regenerating the catalyst, is investigated using chronopotentiometry and is reported here. After catalyst poisoning has occurred, operation at a constant voltage of 0.7 V vs dynamic hydrogen electrode for 10 min recovers the anode performance equivalent to that of the fresh anode.

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)0.001
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.003
GPT teacher head0.194
Teacher spread0.191 · 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

Citations50
Published2009
Admission routes1
Has abstractyes

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