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Record W2338618494 · doi:10.1149/ma2014-02/21/1083

The Impacts of Cathode Catalyst Layer Morphology on PEMFC Design

2014· article· en· W2338618494 on OpenAlexaboutno aff
A. P. Young, Vesna Colbow, Derek Cheng, Siyu Ye, Silvia Wessel

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsnot available
Fundersnot available
KeywordsDurabilityProton exchange membrane fuel cellOperating temperatureMaterials scienceElectrolyteIonomerCathodePorosityChemical engineeringProcess engineeringComposite materialEngineeringChemistryPolymerElectrical engineeringFuel cells

Abstract

fetched live from OpenAlex

Introduction Commercializing polymer electrolyte membrane fuel cells (PEMFC) is ultimately a matter of achieving the necessary cost for market penetration. Cost is a function of materials, manufacturability, performance, and durability. Performance and durability targets have been set by the DOE [1] to provide guidance and focus to the PEMFC industry for specific applications such as the automotive sector. Since both performance and durability are heavily dependent on operating conditions, PEMFC design will be strongly dependent upon the specific requirements of any given commercial application. Designing PEMFCs for applications with narrow operating windows (eg. low or high RH) is a relatively simple task; however, most commercial applications have performance/durability requirements that must be met across a wide range of operating conditions. Cathode catalyst layer morphology is one of the dominating factors for achieving the performance and durability required for many applications; therefore, understanding how manufacturing impacts the catalyst layer morphology and the resulting performance under a wide range of operating conditions is important for designing commercial PEMFCs. Results & Discussion Aside from catalyst activity the cathode catalyst layer performance is dictated completely by the mass transport of protons (proton conductivity), oxygen (gas diffusivity), and water (gas & liquid permeability) [2-4]. Figure 1 shows the balance between proton conductivity and catalyst layer porosity as a function of ionomer loading. Figure 1 – Maximum performance obtained at 30wt% ionomer content with a balance between proton conductivity and gas porosity. Understanding the processes and variables required to control these mass transport parameters is necessary for proper PEMFC design [5-6]. Table 1 outlines several processes and design variables that help control mass transport through the cathode catalyst layer and have therefore had an impact on both performance and durability. Table 1 – Design variables that affect key functions for PEMFC performance. A study was conducted investigating several of these variables, resulting in relationships with mass transport and ultimately performance. These relationships have been used to design a robust catalyst layer that is capable of performing under a wider range of operating conditions compared to previous designs. The goal of this specific design case was to achieve the best attributes of both the top performing MEA over a range of operating conditions and the most durable MEA, in a single design. Starting with a more corrosion resistant catalyst, the performance was increased under a wider range of operating conditions, by improving proton, oxygen, and water mass transport. This was successfully done by utilizing the relationships described above, specifically regarding the types of catalyst and ionomer, the catalyst-ionomer ratio, mixing and coating methodologies and layer contiguity. Acknowledgement The authors would like to acknowledge National Resources Canada (NRCan), National Research Council of Canada (NRC-IRAP), and the US Department of Energy (DOE) for funding various aspects of this work. Reference 1.http://www1.eere.energy.gov/hydrogenandfuelcells/mypp/pdfs/fuel_cells.pdf 2. Egushi, M., Baba, K., Onuma, T., Yoshida, K., Iwasawa, K., Kobayashi, Y., Uno, K., Komatsu, K., Kobori, M., Nishitani-Gamo, M., Ando, T., Polymers, 4, p. 1645 (2012) 3. Xie, J., Xu, F., Wood, D.L., More, K.L., Zawodzinski, T.A., Smith, W.H., Electrochimica Acta 55 p. 7404 (2010) 4. Gode, P., Jaouen, F., Lindbergh, G., Lundblad, A., Sundholm, G., Electrochimica Acta 48 p. 4175 (2003) 5. Mehta, V., Cooper, J.S., J. Power Sources 114, p. 32

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.015
GPT teacher head0.224
Teacher spread0.209 · 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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