Advances in Stabilizing Spinel Cobalt Oxide‐Based Catalysts for Acidic Oxygen Evolution Reaction
Bibliographic record
Abstract
Abstract Oxygen evolution reaction (OER) is pivotal to sustainable energy storage and conversion technologies. Yet, its sluggish kinetics in acidic media and reliance on expensive noble metal catalysts limit its efficiency in these applications. Spinel cobalt(II, III) oxide (Co3O4)‐based catalysts are cost‐effective alternatives with high theoretical catalytic activity. However, their practical deployment is hindered by their poor stability in acidic electrolytes. This review critically examines recent advances in enhancing the stability of spinel Co3O4‐based catalysts for acidic OER. The fundamental reaction mechanisms of acidic OER are first analyzed to illustrate how different catalyst design strategies can be used to improve their stability. Next, five key catalyst design strategies reported in recent studies are summarized: 1) constructing protective surface layers, 2) modulating reaction pathways, 3) controlling cobalt redox dynamics, 4) tuning cobalt‐oxygen covalency, and 5) stabilizing lattice oxygen. Further, recent research progress in understanding the structure‐activity‐stability relationship of spinel Co3O4‐based catalysts is summarized, with a focus on identifying their catalytically active sites, tracking surface reconstruction, and elucidating degradation mechanisms. This review ends with a discussion of future research directions for addressing key challenges in realizing durable, high‐performance Co3O4‐based catalysts for acidic OER 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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".