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Record W4416600504 · doi:10.1149/ma2025-02422137mtgabs

Contact and Performance of Low Anode Loading CCMs for PEM Water Electrolysis at Different Clamping Pressures

2025· article· W4416600504 on OpenAlexaff
Oskar Weiland, Lukas Stein, L. Padilla, Patrick Trinke, Boris Bensmann, Richard Hanke‐Rauschenbach

Bibliographic record

VenueECS Meeting Abstracts · 2025
Typearticle
Language
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsClampingAnodeElectrolysisOhmic contactPorosityCurrent densityCurrent (fluid)Proton exchange membrane fuel cell

Abstract

fetched live from OpenAlex

Reducing the anodic loading of the catalyst-coated membrane (CCM) is necessary for upscaling low-cost PEM electrolyzers. Various studies show that good cell performance is attainable with low-loading CCMs. 1 For this, effective electrical contact between the anodic porous transport layer and CCM is necessary to increase CCM utilization, as electric in-plane conduction inside the catalyst layer is reduced. 2, 3 The cell clamping pressure and mechanical pressure distribution affect the electric contact between the anodic porous transport layer and the catalyst layer. 4 The impact of the cell clamping pressure on low-loading CCMs is shown in this work. Anodic loadings of the CCM, varied down to 0.11 mg/cm², are electrochemically characterized, with particular emphasis on the effects of varying clamping pressure. Also, different porous transport layers and flow fields are applied to study the impact of the cell setup on the overall cell performance. The clamping pressure is gradually increased from 1 MPa to 6 MPa and post mortem SEM images are recorded. A 2D CCM model is used to explain the results from the electrochemical characterization. Both kinetic and ohmic losses are affected by the IrOx-loading. The exchange current density varies with the loading but appears to be relatively insensitive to the clamping pressure. The HFR, on the other hand, appears to be higher and more sensitive to the clamping pressure with decreased loadings (Fig. 1a). Increased HFR values can be explained by inhomogeneous CCM utilization, mostly due to low electric in-plane aCL conductivity. References [1] C. Wang, K. Lee, C. P. Liu, D. Kulkarni, P. Atanassov, X. Peng and I. V. Zenyuk, International Materials Reviews, 69 (1), 3–18 (2024). [2] E. Padgett, G. Bender, A. Haug, K. Lewinski, F. Sun, H. Yu, D. A. Cullen, A. J. Steinbach and S. M. Alia, J. Electrochem. Soc., 170 (8), 84512 (2023). [3] M. F. Ernst, V. Meier, M. Kornherr and H. A. Gasteiger, J. Electrochem. Soc., 171 (7), 74511 (2024). [4] A. Martin, P. Trinke, M. Stähler, A. Stähler, F. Scheepers, B. Bensmann, M. Carmo, W. Lehnert and R. Hanke-Rauschenbach, J. Electrochem. Soc., 169 (1), 14502 (2022). The authors gratefully acknowledge funding by BMBF in the framework of project DERIEL, FKZ 03HY122G, and NEO-PEM-KLX, FKZ 03SF0762A, as well as André Koch and the Solar Energy Research Hamelin (ISFH) for recording the SEM images. 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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.009
GPT teacher head0.223
Teacher spread0.215 · 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
Published2025
Admission routes1
Has abstractyes

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