Green Hydrogen Production by Low‐Temperature Membrane‐Engineered Water Electrolyzers, and Regenerative Fuel Cells
Bibliographic record
Abstract
Abstract Green hydrogen (H 2 ) is an essential component of global plans to reduce carbon emissions from hard‐to‐abate industries and heavy transport. However, challenges remain in the highly efficient H 2 production from water electrolysis powered by renewable energies. The sluggish oxygen evolution restrains the H 2 production from water splitting. Rational electrocatalyst designs for highly efficient H 2 production and oxygen evolution are pivotal for water electrolysis. With the development of high‐performance electrolyzers, the scale‐up of H 2 production to an industrial‐level related activity can be achieved. This review summarizes recent advances in water electrolysis such as the proton exchange membrane water electrolyzer (PEMWE) and anion exchange membrane water electrolyzer (AEMWE). The critical challenges for PEMWE and AEMWE are the high cost of noble‐metal catalysts and their durability, respectively. This review highlights the anode and cathode designs for improving the catalytic performance of electrocatalysts, the electrolyte and membrane engineering for membrane electrode assembly (MEA) optimizations, and stack systems for the most promising electrolyzers in water electrolysis. Besides, the advantages of integrating water electrolyzers, fuel cells (FC), and regenerative fuel cells (RFC) into the hydrogen ecosystem are introduced. Finally, the perspective of electrolyzer designs with superior performance is presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".