Green Hydrogen Production by Low‐Temperature Membrane‐Engineered Water Electrolyzers, and Regenerative Fuel Cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it