Stochastic Generation of Electrolyzer Catalyst Layers
Bibliographic record
Abstract
Developing clean energy technologies, such as polymer electrolyte membrane electrolysis (PEMWE), is essential to mitigate the negative impacts of climate change. However, one persistent barrier to PEMWE uptake is high catalyst costs, which can account for up to 47% of total stack costs [1]. This makes the catalyst a key target for cost savings via material optimization, as the mechanisms linking structure, mass transport, and charge transport remain relatively ambiguous in the literature [2], [3]. Previous studies have explored stochastic material generation and pore network modelling in the porous transport layer (PTL), but such analysis has yet to be performed for the bulk catalyst layer [4]. In this study, we introduced a method for replicating the complex pore structure observed in commercially available catalyst materials using stochastic generation techniques. This method creates catalyst structures by using a desired pore size distribution (PSD) as an input, identifying and generating the macro- and microporous regions of the PSD, and then combining the two regions using a custom divider. Statistical analysis (two sample t-test and two sample K-S test) was used to determine the similarity of generated structures to previously imaged commercial catalyst materials. Moreover, pore network modelling techniques were applied to the generated structures to evaluate electrical and mass transport properties. Through these methods, we show that both transport properties and PSD characteristics of generated materials were within experimentally measured ranges, and altering elements of the PSD can result in order of magnitude changes in electrical conductivity, proton conductivity, and through plane permeability. The methodology presented in this work can be used to evaluate transport properties of various catalyst and catalyst-layer designs with novel pore size distributions. The local transport properties obtained can provide estimates for catalyst properties seldom reported in the literature, facilitating the design and informing the fabrication of next generation, ultra low loading catalyst materials. [1] A. Mayyas, M. Ruth, et al. , Manufacturing Cost Analysis for Proton Exchange Membrane Water Electrolyzers , United States (2019). [2] Z. Taie, X. Peng, et al. , ACS Appl. Mater. Interfaces, 12 , 47, 52701–52712 (2020). [3] J. Lopata, Z. Kang, et al. , J. Electrochem. Soc. , 167. 064507 (2020). [4] J. K. Lee, C. H. Lee, and A. Bazylak, J. Power Sources , 437 , 226910 (2019).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".