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Record W2800601344 · doi:10.1149/2.0351806jes

Modeling the Effect of Chemical Membrane Degradation on PEMFC Performance

2018· article· en· W2800601344 on OpenAlexaff
Ramesh K. Singh, Pang‐Chieh Sui, Ka Hung Wong, Erik Kjeang, Shanna Knights, Ned Djilali

Bibliographic record

VenueJournal of The Electrochemical Society · 2018
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsBallard Power Systems (Canada)Simon Fraser UniversityUniversity of Victoria
FundersNational Natural Science Foundation of China
KeywordsMembraneDegradation (telecommunications)ChemistryRadicalChemical decompositionElectrolyteIsothermal processChemical engineeringHydrogen peroxideDecompositionOrganic chemistryThermodynamicsPhysical chemistry

Abstract

fetched live from OpenAlex

A transient, isothermal, two-dimensional model coupling cell performance and chemical membrane degradation in a polymer electrolyte membrane fuel cell (PEMFC) is developed. The model is based on the conservation of and thermodynamic equilibrium between charged and neutral species, including radicals. The model is validated against experimental polarization behavior and chemical degradation under an open circuit voltage (OCV) hold test at 368.15 K. The four-step chemical degradation of a PFSA-based membrane is assumed to start by an attack by hydroxyl radical at the terminal ether bond in the side chain. The source of the attacking hydroxyl radical is an indirect hydrogen peroxide formation and the subsequent decomposition at Fenton's reagent in the membrane. Simulation of degradation rate (defined as the loss of cell voltage with time at a fixed cell operating condition and at a point of time with a known degradation history) under an OCV-hold test agree qualitatively with the degradation rates reported in the literature.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.192
Teacher spread0.188 · 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 designSimulation or modeling
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

Citations102
Published2018
Admission routes1
Has abstractyes

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