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Record W4405202190 · doi:10.1002/celc.202400563

A Comparative Study of the Oxygen Reduction Reaction on Pt and Ag in Alkaline Media

2024· article· en· W4405202190 on OpenAlexaff
Alexander Rampf, Michael Braig, Stefano Passerini, Roswitha Zeis

Bibliographic record

VenueChemElectroChem · 2024
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of TorontoUniversity of New Brunswick
Fundersnot available
KeywordsDielectric spectroscopyChemistryCatalysisPolarization (electrochemistry)ElectrochemistryDesorptionGas diffusion electrodeAnalytical Chemistry (journal)Fuel cellsElectrodeChemical engineeringInorganic chemistryPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Investigating the ORR under practical conditions is vital for optimizing metal–air batteries and alkaline fuel cells. Herein, we characterized Pt and Ag gas diffusion electrodes (GDE) in a GDE half‐cell in high alkaline concentrations at elevated temperatures by polarization curves and electrochemical impedance spectroscopy (EIS) combined with the distribution of relaxation times (DRT) analysis. The Pt catalyst's polarization curve displays substantial losses below 0.82 V vs. RHE. The DRT analysis reveals significantly increased charge transfer resistance and a decelerated ORR at that potential. RRDE measurements attributed the polarization loss observed for Pt catalysts to increased peroxide formation in this potential region triggered by the desorption of oxygenated species. Therefore, the ORR activity of Ag exceeds some of the here‐used Pt catalysts at high current densities. This work combines the benefits of the RRDE and the GDE half‐cell to study catalysts and identify the reaction mechanisms under conditions relevant to practical fuel cells and batteries. Moreover, the DRT analysis is introduced as an analytical tool to determine the charge transfer resistance contribution and the corresponding frequency of the 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 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 score0.004
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.257
Teacher spread0.241 · 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.

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

Citations10
Published2024
Admission routes1
Has abstractyes

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