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Record W4244171260 · doi:10.1149/ma2018-02/41/1358

Microstructural Analysis of Electrode Performance in Fuel Cells at Varying Water Contents

2018· article· en· W4244171260 on OpenAlexaff
Mayank Sabharwal, Marc Secanell

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsProton exchange membrane fuel cellMaterials scienceWater transportPorosityDiffusionPorous mediumGaseous diffusionElectrochemistryLiquid waterNuclear engineeringElectrodeChemical engineeringWater flowEnvironmental scienceComposite materialFuel cellsChemistrySoil scienceThermodynamics

Abstract

fetched live from OpenAlex

Optimizing the water management in proton exchange membrane fuel cells (PEMFCs) is paramount to improving their performance at high current densities. Numerical models describing the physical processes in the PEMFC are an important tool to analyze and understand experimental results and optimize the performance with respect to material properties and operating conditions. Macro-scale models of PEMFCs describing two-phase flow have previously been developed (1, 2). These models rely on effective transport properties and morphological information of the porous layers, such as pore size distribution (PSD), to accurately describe the physical processes. These properties are often estimated using empirical correlations (1). Microstructural modeling provides a feasible alternative to estimate the required transport properties based on the morphology of the porous layers. Microstructural models describing gas transport and liquid water intrusion have previously been developed for gas diffusion layers (GDLs) (3) and micro-porous layers (MPLs) (4). For catalyst layers (CLs), microstructural models describing the transport and electrochemical reactions under dry conditions are available in literature (5, 6). However, very few studies (7, 8) have accounted for liquid water intrusion in the CLs and the corresponding effect on the transport properties and electrochemical performance. The aim of this work is to develop numerical models to simulate liquid water intrusion in the fuel cell CLs and study the corresponding effect on the transport properties and performance. A focused ion beam-scanning electron microscopy (FIBSEM) CL reconstruction is used to study gas and charge transport and liquid water intrusion in the CL. Liquid water intrusion in the CL is simulated using a full morphology based model. In-situ μ-CT data was used to validate the liquid water intrusion model. The validated model will be used to simulate liquid water intrusion in CLs with different modes of injection such as nucleation (8), PSD based (7) and boundary based (9). The obtained saturation distributions in the CLs would be used as meshes for continuum simulations to study the changes in the effective diffusivity and electrochemical performance of the CLs. For the electrochemical simulations, charge transport is simulated in an ionomer film and oxygen transport is simulated in the ionomer, pores and liquid water. Figure 1a shows a schematic of a single pore with ionomer film and the reaction boundary. Since FIBSEM does not provide any information about the ionomer, carbon or platinum, it is assumed that the ionomer forms a thin film at pore-solid interface. This film is digitally reconstructed based on the composition of the CL. The reaction is simulated at the ionomer-solid interface assuming that the solid interface corresponds to platinum particles. The numerical framework for the microscale simulations has been developed in the open-source package OpenFCST (10). The current results show that the different modes of water injection result in different threshold saturations, i.e., the saturation at which percolating pore volume for gas transport is lost. The effective diffusivity of oxygen reduces with an increase in saturation. Electrochemical simulations on partially saturated CLs are underway. References 1. J. Zhou, A. Putz and M. Secanell, J. The Electrochem. Soc., 164(6), F530 (2017). 2. A. Z. Weber, R. M. Darling and J. Newman, J. Electrochem. Soc., 151(10), A1715 (2004). 3. J. T. Gostick, M. A. Ioannidis, M. W. Fowler and M. D. Pritzker, J. Power Sources, 173(1), 277 (2007). 4. R. Wu, X. Zhu, Q. Liao, H. Wang, Y.-d. Ding, J. Li and D.-d. Ye, International Journal Hydrogen Energy, 35(14), 7588 (2010). 5. M. Sabharwal, L. Pant, A. Putz, D. Susac, J. Jankovic and M. Secanell, Fuel Cells, 16(6), 734 (2016). 6. K. J. Lange, P.-C. Sui and N. Djilali, J. Electrochem. Soc., 157(10), B1434 (2010). 7. T. Hutzenlaub, J. Becker, R. Zengerle and S. Thiele, J. Power Sources, 227, 260 (2013). 8. M. El Hannach, J. Pauchet and M. Prat, Electrochimica Acta, 56(28), 10796 (2011). 9. M. Sabharwal, J. T. Gostick and M. Secanell, J. The Electrochem. Soc. (under review) (2018). 10. M. Secanell, A. Putz, P. Wardlaw, V. Zingan, M. Bhaiya, M. Moore, J. Zhou, C. Balen and K. Domican, ECS Transactions, 64(3), 655 (2014). 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)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.197
Teacher spread0.190 · 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
Published2018
Admission routes1
Has abstractyes

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