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Record W2336328736 · doi:10.1149/ma2014-02/21/1087

Modulated Ionomer Distribution for High Performance PEMFC

2014· article· en· W2336328736 on OpenAlexaff
Hee-tak Kim, Jung-Ki Park, Min‐Ju Choo, Keun‐Hwan Oh

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsIonomerProton exchange membrane fuel cellMaterials scienceCatalysisChemical engineeringElectrochemistryMembrane electrode assemblyElectrical conductorCathodePlatinumNanotechnologyComposite materialChemistryElectrolyteElectrodePolymerElectrical engineeringEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

PEMFC technology is almost ready for large-scale commercialization, yet the technology sector perceives that the current devices are not sufficiently durable, have limited operational flexibility, and are not cost competitive. In particular, the automotive industry is demanding a membrane electrode assembly (MEA) that endows fuel cells with a cost-to-power ratio of $30/kW and high operation temperatures above 90 °C. From the catalyst layer design perspective, ionomer has a vital function in meeting these needs. The ionomers in the catalyst layers function as proton conductors to expand the electrochemically active region, binding materials to impart mechanical stability, and hydrophilic agents to retain moisture and prevent membrane dehydration. In all optimization processes for fuel cell development, the ionomer content is a vital design parameter. If the amount of ionomer is insufficient to form a three-dimensional network, the protons cannot access every part of the catalyst layer. Therefore, only parts of the catalyst can be utilized as active sites for electrochemical reactions. In contrast, if a MEA contains too much ionomer, the electronic conduction paths and gas transport channels (pores) in the catalyst layers will be blocked by either ionomer material or flooded water inside the more hydrophilic pores, particularly at a high current density. In the same way that a platinum loading and its dispersion are key factors in the design of catalysts, the ionomer content and its distribution are essential for providing a good proton transport rate in the catalyst layer without increasing the mass transport resistance but still obtaining high efficiency in the electrochemical conversion. From this perspective, the ionomer distribution of the catalytic layer must be controlled in order to achieve high performance. However, the method of controlling the distributions of the ionomer, catalyst, and water is evolving slowly. The researches on the ionomer distribution have raised questions regarding how to expand the ionomer coverage and reduce the proton transport resistance, while not increasing the oxygen transport resistance, and how to modulate the ionomer distribution from molecular-scale to macro-scale throughout the catalyst layer for better performance. In this work, we propose a new approach to modulate the ionomer distribution through the introduction of poly(ethylene glycol) (PEG) to the cathode catalyst layer and leaching of the PEG phase using the water generated during the fuel cell operation. The PEG, which induces nano-phase separation with ionomer, expands the ionomer phase in the catalyst layer through dilution, which results in a higher ionomer surface coverage on a Pt/C matrix. Furthermore, the ionomer phase in the catalyst layers becomes more connected due to the expansion. Then, the water soluble PEG is removed during the break-in, which retrieves the reaction sites and mass transport channels that are blocked by the PEG. Overall, the key concept in the strategy is to increase the ionomer coverage at fixed ionomer content. We prepared a series of catalyst layers with varying PEG to ionomer ratio from 0 to 0.4, and investigated morphology, electrochemical properties, and power performances of the catalyst layers. In the PEG/ionomer range of 0 to 0.3, both electrochemical active surface area and double layer capacitance increased with the PEG/ionomer ratio and they decreased with a further increase of the ratio up to 0.4. The proton transport resistance thorugh the catalyst layers was decreased with the PEG addition; these were 0.37, 0.29, 0.16, and 0.18 Ohmcm2for PEG/ionomer ratios of 0, 0.2, 0.3, and 0.4, respectively. These results clearly deomstrate the modulation of ionomer distribution is realized with this approach. At an intermediate PEG content of 30%, a maximum power performance was observed; the power performance at 0.6V was increased by 1.7-fold as a result of 1.3-fold increase in the electrochemial active area and two-fold increase in the proton transport rate in the catalyst layer.

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 score0.002
Threshold uncertainty score0.008

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.185
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 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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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