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Record W3025138358 · doi:10.1149/ma2020-01381667mtgabs

Pt/Nanoporous Carbon Scaffold Catalyst Layer for Use in Symmetrical Proton Exchange Membrane Fuel Cells

2020· article· en· W3025138358 on OpenAlexaff
Mohammad Javad Parnian, Marwa Atwa, Xia Tong, Scott Paulson, Viola Birss

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsProton exchange membrane fuel cellMaterials scienceCatalysisChemical engineeringCarbon fibersNanoporousCathodeAnodeMembrane electrode assemblyPorosityCatalyst supportCarbon blackNafionNanotechnologyComposite materialElectrochemistryChemistryElectrodeOrganic chemistry

Abstract

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Carbon microstructures are an essential component of both the cathode and anode catalyst layer in PEMFCs, typically serving as the catalyst support [1], and it is known that the carbon porosity, surface area, and its hydrophobicity/hydrophilicity have a significant effect on electrocatalytic activity and transport issues. This is due to the impact of the carbon on catalyst dispersion, Nafion distribution, electronic conduction, and mass transport limitations [2-4]. Typically, carbon black is used as the catalyst support because of its high surface area, low cost, good electrical conductivity [4,5], but it is susceptible to corrosion and does not pack uniformly, thus resulting in uncontrolled pathways for reactants/products through the catalyst layer [6,7]. Recently, there has been an increasing interest in using mesoporous carbons as catalyst supports for fuel cell application, due to the better accessibility of the internal carbon surfaces, and the tethered Pt nanoparticles, to the reactants, and better mass transport of reactants and products during fuel cell operation [8,9]. Our team has recently developed a novel nanoporous carbon scaffold (NCS), which is self-supported, scalable, and highly tunable (its monodisperse pore size can be controllably varied from 10 to 100 nm), giving specific surface areas of 200 to 600 m2 g-1. These films are fully percolating, have very low tortuosity and a 90% porosity, good electronic conductivity of 2-10 S cm-1 and are robust [10]. While prior work has involved the preparation and testing of an MEA that contained Pt/NCS on the cathode side of the separator, in this work, the focus was on the first-time testing of a symmetrical cell with Pt/NCS on both sides of a Nafion membrane. Here, the Pt nanoparticles (NPs) were deposited throughout the NCS material by wet impregnation of the chloroplatinic acid precursor (Fig. 1) and then infiltrated with Nafion by drop-casting. The thickness of the Pt/NCS films used in the current study was 25 ± 2 μm, the NCS pore size was 85 nm, and the Pt loading at the cathode was < 1 mg Pt cm-2. The Nafion®117 membrane was sandwiched between two NCS catalyst layers by hot pressing and the performance of these new symmetrical MEAs was evaluated in a fuel cell test station using different temperatures. This presentation will focus on the performance of the symmetrical MEA design in comparison to conventional ink-deposited systems, as well as the durability of these novel materials. Because the NCS has such a uniform structure, discussion will also be focused on the changes observed to the carbon itself as well as to the Pt NP size and distribution after testing under PEMFC conditions. References [1] E.H. Majlan, D. Rohendi, W.R.W. Daud, T. Husaini, M.A. Haque, Renewable and Sustainable Energy Reviews, 89,2018, 117-134. [2] C. Arbizzani, S. Righi, F. Soavi, M. Mastragostino, International Journal of Hydrogen Energy, 36, 2011,5038-5046. [3] J.A. Prithi, N. Rajalakshmi, G. Ranga Rao, International Journal of Hydrogen Energy,43, 2018, 4716-4725. [4] A. Bharti, G. Cheruvally, Journal of Power Sources, 360, 2017,196-205. [5] D.V. Dao, G. Adilbish, I. Lee, Y. Yu, International Journal of Hydrogen Energy, 44, 2019, 24580-24590. [6] M.F.L. De Volder, S.H. Tawfick, R.H. Baughman, A.J. Hart science, 339, 2013, 535-539. [7] C.A. Reiser, L. Bregoli, T.W. Patterson, J.S. Yi, J.D. Yang, M.L. Perry, T.D. Jarvi, Electrochem. Solid State Lett., 8, 2005, A273. [8] E. Antolini, Applied Catalysis B: Environmental, 88, 2009, 1-24. [9]S. Songa, Y. Liang, Z. Li, Y. Wang, R. Fu, D. Wu, P. Tsiakarasc, Applied Catalysis B: Environmental, 98, 2010, 132-137. [10] V. Birss, X. Li, D. Banham, D. Y. Kwok, Porous carbon films. PCT/CA2015/000516, 2015. Figure 1. FESEM image of Pt (open circles) within the NCS film. Figure 1

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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 score0.001
Threshold uncertainty score0.004

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.026
GPT teacher head0.219
Teacher spread0.193 · 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
Published2020
Admission routes1
Has abstractyes

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