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Record W2263758335 · doi:10.1149/ma2014-02/21/1206

Doing More with Less: Challenges for PEMFC Catalyst Layer Design

2014· article· en· W2263758335 on OpenAlexaff
Jürgen Stumper

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsAutomotive Fuel Cell Cooperation (Canada)
Fundersnot available
KeywordsProton exchange membrane fuel cellTafel equationCatalysisCathodeCurrent densityMaterials scienceMembrane electrode assemblyExchange current densityOxideNanotechnologyElectrodeChemical engineeringElectrochemistryChemistryElectrical engineeringElectrolyteEngineeringMetallurgy

Abstract

fetched live from OpenAlex

Proton exchange membrane (PEM) fuel cells are being developed as alternative energy sources for both residential and automotive application. In order for this technology to become fully commercial, the reduction of cost and improvements in performance and durability of PEM fuel cells membrane electrode assemblies (MEAs) are still required [1,2]. To address the requirement for further cost reduction the Pt loading of the cathode catalyst layer (CCL) needs to be reduced to 0.2- 0.1 mg/cm2 while maintaining high currents and efficiency. Consequently, it becomes increasingly important to not only develop new catalyst materials, but also optimize the 3D structural arrangement of the CCL components such as catalyst, ionomer and void space so that all critical functionalities can be achieved simultaneously. In general terms this entails to provide sites catalytically active for ORR, and to further provide transport to/from these sites for the reactants O2, protons, electrons and products H2O and heat, respectively. Whereas tremendous progress has been achieved over the years using traditional ink based manufacturing methods [3] in reducing Pt loadings, the performance after ohmic correction at high current density shows a significantly larger decrease as the Pt loading approaches 0.1 mg/cm2 than would be expected from a simple Tafel slope scaling due to the reduction in exchange current density. This “loading effect” has been observed by several authors [5] with explanations ranging from the oxide state of the Pt surface [6] to unknown transport losses at or near the Pt surface. Ohma etal. have proposed a transmission line model of CCL mass transport resistances distributed in through plane (z) direction with the Knudsen/ionomer diffusion resistances in series/parallel, respectively. This model could explain the loading effect if the ionomer diffusion resistance dominates mass transport in the CCL[7]. One question arising from these results is if the loading effect could somehow be associated with the structure of the CCL and if there are alternative CCL structures that do not suffer from such limitations. The only alternative catalyst layer structures available on commercial scale are the so called nano-structured thin film (NSTF) catalyst layers available from 3M [4]. M. K. Debe has recently proposed a model based on kinetic gas theory where the collision frequency of gas molecules with the catalyst surface taking place in the Knudsen regime (i.e. assuming a gas phase process as rate determining) explains another effect, i.e. the differences of >1order of magnitude in specific activities [mA/cm2Pt] observed between NSTF and traditional dispersed carbon supported catalysts [8]. According to the model, the high specific activities observed with NSTF are a due to a structural effect, however, the loading effect is still observed albeit to a reduced extent compared to traditional CCL structures. Therefore, in order to be able to design CCL structures that meet the performance and durability requirements, it is necessary to obtain a better understanding of structure versus performance relationships. This requires the capability to fabricate different CCL structures, to characterize the spatial distribution of all components within the catalyst layer (carbon, Pt, ionomer and void), to measure the physico-chemical properties (both ex-situ and in-situ) and finally to use these experimental data as inputs for the development a model based understanding of the relationship between CCL structure and CCL performance and durability References: Y. Wang, K.S. Chen, J. Mishler, S.C. Cho, X.C. Adroher, Applied Energy 88 (2011) 981. R. Borup, J. Meyers, B. Pivovar, Y. Seung Kim, R. Mukundan, N. Garland, D. Myers, F. Garzon, D. Wood, P. Zelanay, K. More, K. Stroth, T. Zawodinski, J. Boncella, J. E. McGrath, M. Inaba, J. Miyatake, M. Hori, K. Ota, Z. Ogumi, S. Miyata, A. Niskikata, Z. Siroma, Y. Uchimoto, K. Yasuda, K. Kimijima and Norio Iwashita, Chem. Rev. 107 3904 (2007). S. S. Kocha in Handbook of Fuel Cells Vol. 3, W. Vielstich, A. Lamm, H. Gasteiger (Eds.), Wiley (2003), p 538 M. K. Debe in Handbook of Fuel Cells Vol. 3, W. Vielstich, A. Lamm, H. Gasteiger (Eds.), Wiley (2003), p 576 T. Greszler, S. Kumaraguru, N. Subramanian, B. Litteer, Z. Liu, andR. Makharia, 2008 Fuel Cell Seminar, Phoenix, AZ, Oct. 28, (2008) http://www.fuelcellseminar.com/assets/pdf/2008/wednesdayPM/GHT33-2_SKumaraguru.ppt.pdf N. P Subramanian J Electrochem Soc 159(5) B531 (2012) A.Ohma, T.Mashio, K.Sato, H.Iden, Y.Ono, K.Sakai, K.Akizuki, S.Takaichi, K.Shinohara, Electrochim Acta, 56 10832 (2011) M. K. Debe J Electrochem Soc 159(1) B54 (2012)

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.004

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 head0.025
GPT teacher head0.221
Teacher spread0.196 · 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 designNot applicable
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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