MétaCan
Menu
Back to cohort
Record W2314116502 · doi:10.1149/1.3635631

Optimization of Ternary Alloy Catalysts for PEMFC Cathodes

2011· article· en· W2314116502 on OpenAlexaff
Prasanna Mani, Doris Tang, Wendy Lee

Bibliographic record

VenueECS Transactions · 2011
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsAutomotive Fuel Cell Cooperation (Canada)
Fundersnot available
KeywordsDurabilityCatalysisTernary operationMaterials scienceElectrochemistryAlloyChemical engineeringTernary alloyCathodeProton exchange membrane fuel cellMetallurgyNuclear chemistryComposite materialElectrodeChemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Performance and durability of cathodes have been one of the main factors impeding commercialization of fuel cells for automotive applications. This work reports an optimized Rotating Disk Electrode (RDE) technique and a mass activity and durability for a baseline Pt/Graphitized Carbon (Pt/GC). An accelerated stress test (AST) is performed to determine the durability of the catalysts by cycling 30,000 times between 0.6 and 1.0V with a 1 sec hold at each potential. Mass activity and electrochemical surface area are measured at the beginning of life (BOL) and at the end of life (EOL) after 30,000 cycles. The BOL mass activity of the Pt/GC baseline is 218 A/g-Pt. In-house prepared Pt-Ni-Co ternary alloys show a BOL mass activity of 600-900 A/g-Pt for the best performing catalysts not shown in this report. However optimization of post heat and acid treatment of catalysts is necessary to improve the durability. The optimum temperature for the initial and final post heat treatment is found to be 400 oC for the ternary alloy catalysts. The heat-acid-heat treated catalyst is more durable showing a 52% degradation rate (EOL: 292 A/g-Pt) compared to 75% (EOL: 196 A/g-Pt) for the heat-acid treated catalysts.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.936
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.199
Teacher spread0.180 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueECS TransactionsSame topicFuel Cells and Related MaterialsFrench-language works237,207