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Record W2510963458 · doi:10.1149/ma2016-02/38/2595

Establishing Targets for the Cathode Catalyst Layer Kinetic & Transport Parameters in PEMFC Designs

2016· article· en· W2510963458 on OpenAlexaboutno aff
A. P. Young, Siyu Ye, Kyoung Bai, Drew Stolar

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsnot available
Fundersnot available
KeywordsProton exchange membrane fuel cellMaterials scienceMembrane electrode assemblyDurabilityThermal diffusivityElectrolyteCatalysisConductivityProton transportChemical engineeringProcess engineeringMembraneComposite materialChemistryEngineeringElectrodeThermodynamics

Abstract

fetched live from OpenAlex

Introduction Commercializing polymer electrolyte membrane fuel cells (PEMFC) is ultimately a matter of achieving the necessary cost for broad 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. To cascade these challenging targets down to material and transport requirements we employ a fully integrated performance model to conduct parameter optimization studies in tandem with experimental validation. Design curves are utilized as inputs to the model to provide the current state-of-the-art capability regarding catalyst activity and ionomer proton conductivity. High performing commercial membrane and GDL components were selected with a standard catalyst (Figure 1) to provide a baseline for this evaluation. The gap between modelled MEA performance and DOE targets will be highlighted and a recommended path forward will be provided. Results & Discussion Aside from catalyst activity and available surface area, the cathode catalyst layer performance is dictated by the mass transport of protons (proton conductivity), oxygen (gas diffusivity), and water (gas & liquid permeability) [2-4]. Several material sets have been evaluated experimentally in-situ to provide a range in catalyst activity, proton conductivity, and effective layer diffusivity as shown in figures 1 and 2 (diffusivity not shown). During operation the distribution of current through the porous three dimensional catalyst layer structure [6] is dictated by the catalyst activity and layer transport properties. We will describe in this work the relationship between proton conductivity and voltage performance. Based on these results we can set relevant conductivity targets, which can be used for both ionomer development and material down selection. These parameters will be utilized in the Ballard/DOE funded FC-Apollo performance model to provide the current status toward achieving the DOE’s 2020 automotive targets listed in Table 1. Further, technology gaps will be identified in conjunction with a strategy toward meeting these objectives. Acknowledgement The authors would like to acknowledge Joey Jickain for his testing support and 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://energy.gov/eere/fuelcells/doe-technical-targets-polymer-electrolyte-membrane-fuel-cell-components 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. Young, AP., Gyenge, E., Stumper, J., J. Electrochem. Soc., 156, B913 (2009) 6. Young, AP., Knights, S., Gyenge, E., Stumper, J., J. Electrochem. Soc., 157, B425 (2010) 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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
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 head0.033
GPT teacher head0.237
Teacher spread0.204 · 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
Published2016
Admission routes1
Has abstractyes

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