Advances in protective coatings for porous transport layers in proton exchange membrane water electrolyzers: Performance and durability insights
Bibliographic record
Abstract
Proton exchange membrane water electrolyzers offer a promising pathway for sustainable hydrogen production. However, the high cost and limited durability of key components, particularly porous transport layers, hinder their widespread adoption. The porous transport layer enables water and electron transport and oxygen removal. Titanium typically enables the required durability, yet the oxidative conditions lower its electrical conductivity. Protective coatings on porous transport layers play a pivotal role in enhancing electrochemical performance and durability by mitigating interfacial contact resistance and maintaining structural integrity under harsh oxidative conditions. This Review highlights the critical impact of porous transport layer coatings on the electrochemical performance and durability by comparing various porous transport layer coatings, including precious metals, non-precious metals, and their combinations. It clarifies the desirable coating material specifications, along with the appropriate morphological, structural, and physical characteristics of the resulting porous transport layers. It also provides a detailed comparative analysis of polarization characteristics, electrochemical impedance responses, potentiostatic and potentiodynamic polarizations, and interfacial contact resistances for various porous transport layer coatings. This Review overviews coating materials and highlights the promising candidates to be considered in the design of next-generation porous transport layers for proton exchange membrane water electrolyzers. The Review assesses the porous transport layer materials from various aspects, including performance, durability, and scalability – all centered around practicality. The Review concludes with the recent progress, remaining challenges, and future research directions.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".