MétaCan
Menu
Back to cohort
Record W2791211698 · doi:10.1149/08301.0071ecst

Influence of Ionomer Structures and Ratios on Performance and Degradation of PEM Fuel Cells

2018· article· en· W2791211698 on OpenAlexaff
Samaneh Shahgaldi, Ibrahim Alaefour, Xianguo Li

Bibliographic record

VenueECS Transactions · 2018
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsIonomerDurabilityNafionProton exchange membrane fuel cellMaterials scienceCoatingComposite materialDegradation (telecommunications)Fuel cellsLayer (electronics)ConductivityChemical engineeringPolymerChemistryEngineeringElectrochemistryCopolymerElectrical engineering

Abstract

fetched live from OpenAlex

In recent years, short side chain (SSC) ionomers with lower equivalent weights have been demonstrated to increase cell performance via more-homogenous coating and higher proton conductivity than the conventional ionomer, Nafion, used in the catalyst layer (CL). In spite of some studies on cell performance, the impact of an ionomer's structure and ratio on cell durability has hardly been reported. In this study, a systematic experimental investigation is performed to study the impact of SSC ionomer structures and ratios on fuel cell durability using a scaled up cell (45 cm2). A CL fabricated with an SSC ionomer is shown to perform better and be more durable than a CL prepared by Nafion in the same ratio. Moreover, increasing the ionomer ratio in the catalyst layer leads to lower cell performance at high current densities, but higher cell durability. These results highlight the importance of the structure and ratio of the ionomers in cell performance and durability.

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 score0.287
Threshold uncertainty score0.180

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 head0.005
GPT teacher head0.184
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

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

Citations7
Published2018
Admission routes1
Has abstractyes

Explore more

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