Designing Microporous Layers for Electrolyzers Using Stochastic Approach
Bibliographic record
Abstract
Electrochemical energy conversion devices, such as water and carbon dioxide electrolyzers, offer significant advantages in achieving net-zero emissions and in mitigating further increases in global temperature. However, their widespread adoption necessitates enhancements in performance and durability. Microporous layers (MPLs) have been gaining attention as a promising means to enhance the performance and durability of membrane-electrode-assembly (MEA) based electrolyzers, but their nontrivial mechanisms and complexity in fabrication pose challenges for optimizing the microporous layer structure experimentally. This study introduces a stochastic model for generating MPLs in application to electrolyzers. The model produces 3D reconstructions of MPLs, with porosity and particle size as input parameters, and is capable of generating biased MPLs by taking the pre-existing 3D reconstruction as an input. The model applies a dilation and erosion algorithm to replicate sinter-necks formed in the MPL during the sintering process, and captures their impact on structural and transport properties. In this work, three types of MPLs are generated by using the presented model, which include single-layer MPLs, MPLs with pore formers, and bilayer MPLs. Surface roughness analysis and pore network simulations on the MPLs highlight the significance of particle size in the MPL design. Using finer particles at higher porosities are favored over using larger particles at lower porosities. Such findings are examples of the valuable insights offered from the presented stochastic model, and the model will guide seminal discovery of next-generation MPLs that will greatly progress the shift toward net-zero electrochemical energy conversion technologies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".