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Record W2508553101 · doi:10.1149/07514.0289ecst

Accelerated Degradation of Polymer Electrolyte Membrane Fuel Cell Gas Diffusion Layers: Performance Degradation and Steady State Liquid Water Distributions with in Operando Synchrotron X-ray Radiography

2016· article· en· W2508553101 on OpenAlexaff
Hang Liu, Michael G. George, Nan Ge, Rupak Banerjee, Stéphane Chevalier, Jong‐Min Lee, Pranay Shrestha, Daniel Muirhead, James Hinebaugh, Roswitha Zeis, Matthias Messerschmidt, Joachim Scholta, Aimy Bazylak

Bibliographic record

VenueECS Transactions · 2016
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicroporous materialElectrolytePower densityMaterials scienceProton exchange membrane fuel cellPorosityWater transportAnalytical Chemistry (journal)Polarization (electrochemistry)Current densityDiffusionChemical engineeringComposite materialChemistryMembraneWater flowChromatographyElectrodeEnvironmental engineering

Abstract

fetched live from OpenAlex

As-received pristine gas diffusion layers (GDLs) were degraded through an accelerated artificial aging process by immersion into a 35% solution of H 2 O 2 at 90°C for 12 hours. Polarization curves were obtained while synchrotron X-ray radiography was performed to investigate the effect of ageing on liquid water transport behavior. Peak output power density of the fuel cell composed of the aged GDL reached only 76% of that of the fuel cell composed of the pristine GDL. This performance degradation was attributed to an increase in mass transport resistance associated with liquid water accumulation at the aged GDL and flow field channel interface. The aged GDLs showed more liquid water accumulation at the microporous layer (MPL)/carbon substrate interface and carbon substrate regions than pristine GDLs at low current density operation. A fully developed water profile was established at lower current density for the aged GDLs compared to pristine GDLs.

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 score0.012
Threshold uncertainty score0.508

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 head0.173
Teacher spread0.168 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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