Recent Progresses in Electrocatalysts for Water Electrolysis
Bibliographic record
Abstract
Abstract The study of hydrogen evolution reaction and oxygen evolution reaction electrocatalysts for water electrolysis is a developing field in which noble metal-based materials are commonly used. However, the associated high cost and low abundance of noble metals limit their practical application. Non-noble metal catalysts, aside from being inexpensive, highly abundant and environmental friendly, can possess high electrical conductivity, good structural tunability and comparable electrocatalytic performances to state-of-the-art noble metals, particularly in alkaline media, making them desirable candidates to reduce or replace noble metals as promising electrocatalysts for water electrolysis. This article will review and provide an overview of the fundamental knowledge related to water electrolysis with a focus on the development and progress of non-noble metal-based electrocatalysts in alkaline, polymer exchange membrane and solid oxide electrolysis. A critical analysis of the various catalysts currently available is also provided with discussions on current challenges and future perspectives. In addition, to facilitate future research and development, several possible research directions to overcome these challenges are provided in this article. 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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".