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Record W4308458320 · doi:10.1016/j.jcat.2022.10.025

Improving the ORR performance by enhancing the Pt oxidation resistance

2022· article· en· W4308458320 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Catalysis · 2022
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsDalhousie University
FundersEngineering and Physical Sciences Research CouncilChina Scholarship CouncilRoyal Society
KeywordsChemistryCatalysisDissolutionProton exchange membrane fuel cellAlloyX-ray absorption fine structureChemical engineeringOstwald ripeningAdsorptionElectrochemistryPlatinumPhysical chemistryElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

Proton exchange membrane fuel cells require oxygen reduction catalysts with high activity and stability. Pt based alloy materials are most widely applied ORR catalyst due to its high intrinsic activity, but usually suffer from rapid deactivation as a result of particle agglomeration, detachment, Ostwald ripening and/or Pt dissolution. Here we investigate the degradation of the PdPt alloys via in situ X-ray absorption fine structure, Δ μ analysis, identical location-electron microscopy and DFT calculations. We conclude that the origin of high activity and stability of the PdPt catalyst stems from the oxidation resistance of metallic Pt, forming mainly surface adsorbed O species at high potentials. Two stage degradation process are observed, showing an evolution of dynamic surface dependent ORR performance along with the deactivation process. The careful design of Pt alloy structure leads to controlled surface oxygen behaviours. This opens a new way to increase the lifespan of fuel cells and improve the Pt utilization efficiency.

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.

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)0.002
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 score0.180
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.004
GPT teacher head0.183
Teacher spread0.179 · 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