Electrocatalytic Oxygen Evolution Reaction in Acidic Conditions: Recent Progress and Perspectives
Bibliographic record
Abstract
Abstract The electrochemical oxygen evolution reaction (OER) is an important half‐cell reaction in many renewable energy conversion and storage technologies, including electrolyzers, nitrogen fixation, CO 2 reduction, metal‐air batteries, and regenerative fuel cells. Among them, proton exchange membrane (PEM)‐based devices exhibit a series of advantages, such as excellent proton conductivity, high durability, and good mechanical strength, and have attracted global interest as a green energy device for transport and stationary sectors. Nevertheless, with a view to rapid commercialization, it is urgent to develop highly active and acid‐stable OER catalysts for PEM‐based devices. In this Review, based on the recent advances in theoretical calculation and in situ/operando characterization, the OER mechanism in acidic conditions is first discussed in detail. Subsequently, recent advances in the development of several types of acid‐stable OER catalysts, including noble metals, non‐noble metals, and even metal‐free OER materials, are systematically summarized. Finally, the current key issues and future challenges for materials used as acidic OER catalysis are identified and potential future directions are proposed.
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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 | 0.001 | 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".