Perovskite Oxides Toward Oxygen Evolution Reaction: Intellectual Design Strategies, Properties and Perspectives
Bibliographic record
Abstract
Developing electrochemical energy storage and conversion devices (e.g., water splitting, regenerative fuel cells and rechargeable metal-air batteries) driven by intermittent renewable energy sources holds a great potential to facilitate global energy transition and alleviate the associated environmental issues. However, the involved kinetically sluggish oxygen evolution reaction (OER) severely limits the entire reaction efficiency, thus designing high-performance materials toward efficient OER is of prime significance to remove this obstacle. Among various materials, cost-effective perovskite oxides have drawn particular attention due to their desirable catalytic activity, excellent stability and large reserves. To date, substantial efforts have been dedicated with varying degrees of success to promoting OER on perovskite oxides, which have generated multiple reviews from various perspectives, e.g., electronic structure modulation and heteroatom doping and various applications. Nonetheless, the reviews that comprehensively and systematically focus on the latest intellectual design strategies of perovskite oxides toward efficient OER are quite limited. To bridge the gap, this review thus emphatically concentrates on this very topic with broader coverages, more comparative discussions and deeper insights into the synthetic modulation, doping, surface engineering, structure mutation and hybrids. More specifically, this review elucidates, in details, the underlying causality between the being-tuned physiochemical properties [e.g., electronic structure, metal-oxygen (M-O) bonding configuration, adsorption capacity of oxygenated species and electrical conductivity] of the intellectually designed perovskite oxides and the resulting OER performances, coupled with perspectives and potential challenges on future research. It is our sincere hope for this review to provide the scientific community with more insights for developing advanced perovskite oxides with high OER catalytic efficiency and further stimulate more exciting applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".