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Record W4309813892 · doi:10.1149/ma2022-02401481mtgabs

Effects of Wet Film Application Parameters on the Structure and Performance of Fuel Cell Catalyst Layers Prepared Using Scalable Methods

2022· article· en· W4309813892 on OpenAlexaffabout
Jonas Stoll, Erik Kjeang, Philip Huynh

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCoatingMaterials scienceCatalysisLayer (electronics)Membrane electrode assemblyChemical engineeringCatalyst supportThroughputElectrolyteComposite materialElectrodeComputer scienceChemistryEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The use of high-volume manufacturing processes for polymer electrolyte fuel cells is obligatory to bring the manufacturing cost closer to 80USD/kW fuel cell system cost target for long-Haul Trucks [1] and thereby make this technology an economical competitor in the carbon neutral transportation sector. For the membrane electrode assembly, thin film roll good materials are therefore the norm in the industry. On the lab scale however, catalyst layers and catalyst coated membranes (CCMs) are commonly prepared with low throughput multi-sub-layer coating application techniques, such as ultrasonic spray coating. Overall, each catalyst sub-layer is dried before the next sub-layer is applied via another spraying pass, until the desired catalyst loading and therewith the overall catalyst layer is created. This multi-sub-layer coating approach is therefore time consuming. However, to simulate high volume manufacturing at the lab scale, direct layer coating techniques such as film applicator or Mayer bar coating can be utilized. These coating methods are designed to form a one pass final catalyst wet film thickness, similar to what transpires in a continuous high throughput roll to roll (R-2-R) manufacturing process. As opposed to the multi-layer coating techniques, the entire wet thickness of the catalyst layer is dried in one step, which allows greater throughput, but also influences the catalyst layer formation. Because when a catalyst wet film dries, there are forces related to evaporation, diffusion, and sedimentation which influence the distribution of materials and the ultimate catalyst layer structure [2]. Furthermore, catalyst inks used for Mayer-rod, film applicator, and R-2-R coating methods generally have higher solids content than the inks used in spray coating. With the one pass techniques, CCMs are typically fabricated by coating catalyst ink on a decal film, such as polytetrafluoroethylene (PTFE), followed by a hot lamination process, such as decal transfer, of the catalyst layer to the membrane [3]. Due to the interaction of the catalyst ink with the coating substrate and drying process, there are many factors that may influence the quality of the catalyst layer obtained with direct layer coating, considering both ink formulation and coating parameters. To understand the interaction of these influences, we investigate the effects of wet film application parameters on the structure and performance of fuel cell catalyst layers, prepared using scalable methods. The same drying technique of hot air drying is used to obtain a closer representation of the direct layer R-2-R process. In more detail, we are determining the influence of the following factors on the catalyst layer formation, when coated on a PTFE substrate: drying temperature, ionomer-to-carbon support ratio, ink water-to-alcohol ratio, and wet film thickness for each of the two-lab scale direct layer coating methods. Finally, we will discuss the influence of these factors on the catalyst structure via microscopy. As well as performance and electrochemical analysis data, of selected catalyst layers, after being decal transferred onto a membrane and tested. Figure 1 shows an example of the variance of dried catalyst layers coated via two direct coating methods on PTFE substrates from the alterations of Ink Water-to-Alcohol (2-Propanol, 1-Propanol, Ethanol) ratio and coating wet thicknesses. As can be seen the dried catalyst layer structure vary depending on the Ink Water-to-Alcohol ratio, wet thickness and application method used. Acknowledgments This research was supported by the Simon Fraser University Community Trust Endowment Fund, Natural Sciences and Engineering Research Council of Canada, Canada Foundation for Innovation, British Columbia Knowledge Development Fund, Western Economic Diversification Canada, and Canada Research Chairs. Reference [1] Menezes, Mark W., et al. “"US Department of Energy Hydrogen Program Plan” https://www.hydrogen.energy.gov/roadmaps_vision.html (2020) [2] C.M. Cardinal, Y.D. Jung, K.H. Ahn, L.F. Francis, Drying regime maps for particulate coatings, AIChE J. 56 (2010) 2769–2780, https://doi.org/10.1002/ aic.12190. [3] Wei, Zhaoxu, et al., High performance polymer electrolyte membrane fuel cells (PEMFCs) with gradient Pt nanowire cathodes prepared by decal transfer method. International Journal of Hydrogen Energy 40.7 (2015): 3068-3074. 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)0.000
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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
Published2022
Admission routes2
Has abstractyes

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