Scalable Implementation of Recombination Catalyst Layers to Mitigate Gas Crossover in PEM Water Electrolyzers
Bibliographic record
Abstract
Hydrogen permeation across the membrane is a critical safety hurdle within polymer electrolyte membrane (PEM) water electrolysis (WE). It is crucial to implement recombination catalysts into the membrane electrode assemblies (MEAs) for reducing hydrogen concentrations and allow the use of much thinner membrane architectures that allow high efficiency operation. Here we show how recombination catalyst layers can be fabricated into MEAs by using a scalable method. In subsequent slot-die coating steps, an electrically insulating and then a recombination layer (both 5 μ m thick) are applied directly to the anode. This three-layer system is then processed into a 5-layer MEA with a cathode and membrane using the decal process. The 5-layer MEA shows a reliable hydrogen reduction in the anode product gas for a wide-range of membrane thicknesses. The long-term stability of the recombination layer is shown for a 5-layer Nafion™ HP-MEA in comparison to a 3-layer MEA. Even after long-term operation, the MEA shows a safe hydrogen concentration reduction on the anode. Finally, the presented technique is used to produce 5-layer MEAs with active areas of 1056 cm 2 and 60 μ m membrane thicknesses. Measurements on reference MEAs show a successful scale-up, proving the technique to be applicable to all scales.
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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 | 0.000 |
| 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 | 0.001 |
| 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".