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Record W4240544083 · doi:10.1149/ma2019-02/34/1518

Cyclic Hygral Swelling and Shrinkage Behavior of Fuel Cell Membranes

2019· article· en· W4240544083 on OpenAlexaff
Alireza Sadeghi Alavijeh, Sandeep Bhattacharya, Erik Kjeang

Bibliographic record

VenueECS Meeting Abstracts · 2019
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSwellingShrinkageMembraneIonomerMaterials scienceComposite materialUltimate tensile strengthProton exchange membrane fuel cellChemistryPolymerCopolymer

Abstract

fetched live from OpenAlex

Mechanical membrane durability is a key factor for the overall lifetime of PEM fuel cells. It is therefore important to understand the impact of the dynamic membrane hygral swelling and shrinkage characteristics on the in-situ mechanical stresses and the associated mechanical durability. In this work, an in-depth experimental investigation was carried out to determine the cyclic hygral swelling and shrinkage properties of four commonly used types of fuel cell membranes: Nafion® NRE211, two different expanded polytetrafluoroethylene (ePTFE) reinforced perfluorosulfonic acid ionomer membranes, and one hydrocarbon membrane. In addition to the bare membranes, the effect of coating the membranes with catalyst layers on the hygral swelling/shrinkage behavior was also investigated. Tensile specimens were subjected to ex-situ hydration and dehydration cycles using a dynamic mechanical analyzer (TA Instruments Q800 DMA) equipped with an environmental chamber, wherein the dynamic swelling and shrinkage characteristics as well as the residual stresses were measured for each membrane by displacement sensor and load cell, respectively. Hydrocarbon and reinforced PFSA ionomer membranes revealed higher shrinkage than swelling at each RH cycle, resulting in an overall shrinkage. Inside a fuel cell stack where membrane swelling and shrinkage is constrained, the observed shrinkage would result in internal tensile stress in the membrane and adjacent components, and hence reduce the overall membrane mechanical durability. The accumulation of residual stress during successive RH cycles was therefore measured explicitly using a custom-designed constrained shrinkage test and compared to the tensile properties of each individual membrane. For example, the hydrocarbon membranes analyzed in this work showed large residual stresses (severe contraction) during confined shrinkage at 80oC that exceeded their yield stress, and are therefore deemed prone to failure after repeated RH cycles. In addition, the hygral swelling and shrinkage behavior of each membrane was compared to the corresponding catalyst-coated membranes and the reinforcement offered by the catalyst layers was determined. The identified swelling and shrinkage properties, generalized into three different categories of overall cyclic behavior, have vital implications for the development of durable fuel cell membranes and membrane electrode assemblies. In addition to providing good mechanical strength, the membranes are also expected to accommodate or preferably eliminate progressive shrinkage stresses during dynamic fuel cell operation.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

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