Recent Progresses in Oxygen Reduction Reaction Electrocatalysts for Electrochemical Energy Applications
Bibliographic record
Abstract
Abstract Electrochemical energy storage systems such as fuel cells and metal–air batteries can be used as clean power sources for electric vehicles. In these systems, one necessary reaction at the cathode is the catalysis of oxygen reduction reaction (ORR), which is the rate-determining factor affecting overall system performance. Therefore, to increase the rate of ORR for enhanced system performances, efficient electrocatalysts are essential. And although ORR electrocatalysts have been intensively explored and developed, significant breakthroughs have yet been achieved in terms of catalytic activity, stability, cost and associated electrochemical system performance. Based on this, this review will comprehensively present the recent progresses of ORR electrocatalysts, including precious metal catalysts, non-precious metal catalysts, single-atom catalysts and metal-free catalysts. In addition, major technical challenges are analyzed and possible future research directions to overcome these challenges are proposed to facilitate further research and development toward practical application. Graphic Abstract
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".