A Review of Diagnostic Tools for Evaluating Porous Transport Layers for Proton Exchange Membrane (PEM) Water Electrolysis
Bibliographic record
Abstract
Abstract As a key component of the proton exchange membrane water electrolyzer (PEMWE), the porous transport layer (PTL) not only provides mechanical support but also facilitates the supply of reactants to the electrode and the removal of produced gases and ensures efficient electrical and thermal management. Commercially available PTLs are often repurposed for other applications, such as filtration, and are not specifically tailored for PEMWE applications. Given this context, research output on PTL development has increased notably in recent years. Optimized, structured PTLs with preferred properties require applicable, relevant, and convenient diagnostic tools for PTL material development. As such, this work aims to identify and review a wide range of techniques for evaluating developed PTLs, including electrochemical techniques, custom-engineered cells, operando diagnosis, ex situ characterization, and postmortem analysis. By providing detailed information on these characterization techniques, this review aims to catalyze further research and development in the academic and industrial sectors, enhancing the understanding, development, and quality control of PTL components. Graphical Abstract
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".