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Record W4245522080 · doi:10.1149/ma2016-02/38/2536

Model-Based Analysis of Carbon Corrosion in Start-up/Shutdown, Fuel Starvation, and Voltage Reversal of a Polymer Electrolyte Fuel Cell

2016· article· en· W4245522080 on OpenAlexaff
Jixin Chen, Jingwei Hu, James Waldecker

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsAutomotive Fuel Cell Cooperation (Canada)
Fundersnot available
KeywordsAnodeCorrosionCathodeElectrolyteCarbon fibersMaterials scienceProton exchange membrane fuel cellPolarity reversalChemistryNuclear engineeringChemical engineeringVoltageComposite materialElectrodeElectrical engineeringCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Support carbon corrosion has been identified as a major degradation source in a polymer electrolyte fuel cell. Start-up/Shutdown (SUSD) [1], fuel starvation [1], and voltage reversal [2] are widely-known conditions that can trigger non-negligible carbon corrosion in the anode or cathode. SUSD carbon corrosion is associated with current reversal while there is no external load. As a comparison, fuel starvation due to nitrogen blanketing and/or water droplets in an operating cell also can result in similar current reversal. The region with current reversal, i.e., a proton flux from the cathode to the anode, features extremely severe hydrogen depletion in the anode and occurrence of carbon corrosion in the opposite cathode. Differently, the voltage reversal occurs in one or a few cells (not all) in a stack. Those cells show negative cell voltages under extended reversal, but the overall current is flowing as usual thanks to the other normal cells in the stack. The voltage reversal leads to carbon corrosion in the anode, as compared to carbon corrosion in the cathode for current reversal. The local potential for carbon corrosion could be much more severe in an extended voltage reversal than that in SUSD. This paper/presentation will elucidate the fundamental differences of these three carbon corrosion scenarios with model simulation and analysis. The models [3, 4] will also be used to perform comprehensive parametric studies, aiming to understand the impacts from key design and operating parameters including pseudo-capacitance, selective oxygen evolution reaction (OER) catalysis, reactant gas flow rate, temperature, membrane hydration state, and current load drawn from the stack. It is anticipated to shed light on the otherwise confusing carbon corrosion scenarios in a polymer electrolyte fuel cell and the fundamental ideas for alleviation strategies. For example, in the fuel starvation induced carbon corrosion, the simulation indicates that the decreasing cell voltage due to diluted hydrogen concentration appears to limit the local potential in the cathode and associated carbon corrosion. In the SUSD carbon corrosion, pseudo-capacitance from Pt oxidation may raise another concern in parallel to carbon corrosion. In general, a reduced temperature or enhanced OER activity is helpful in reducing carbon corrosion. References: [1] W. Gu, P. T. Yu, R. N. Carter, R. Makharia, and H. A. Gasteiger, “Modeling and Diagnostics of Polymer Electrolyte Fuel Cells— Local H2Starvation and Start–Stop Induced Carbon-Support Corrosion”, Modern Aspects of Electrochemistry 49, Springer Science + Business Media, LLC, New York (2010). [2] S. Knights, D. Wilkinson, S. Campbell, J. Taylor, J. Gascoyne, and T. Ralph, “Solid Polymer Fuel Cell with Improved Voltage Reversal Tolerance”, U.S. Patent, 6,936,370 B1 (2005). [3] J. Chen, J. Siegel, T. Matsuura, and A. Stefanopoulou, “Carbon Corrosion in PEM Fuel Cell Dead-Ended Anode Operations”, Journal of the Electrochemical Society, 158, B1164-B1174 (2011). [4] J. Chen, J. Hu, and J. Waldecker, “A Comprehensive Model for Carbon Corrosion during Fuel Cell Start-Up”, Journal of the Electrochemical Society, 162, F878-F889 (2015). Figure 1. The schematic of carbon corrosion mechanisms from current reversal during start-up (upper subfigure) and voltage reversal (lower subfigure) in a polymer electrolyte fuel cell. Figure 1

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 score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.194
Teacher spread0.186 · 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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