Defect Engineering of Carbon‐based Electrocatalysts for Rechargeable Zinc‐air Batteries
Bibliographic record
Abstract
Rechargeable zinc-air batteries (ZABs) are considered as one of the most promising electrochemical energy devices due to their various unique advantages. Oxygen electrocatalysis, involving the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), determines the overall performance of zinc-air batteries. Therefore, the development of highly efficient bifunctional ORR/OER catalysts is critical for the large-scale application of ZABs. Carbon-based nanomaterials have been widely reported to be efficient electrocatalysts toward both ORR and OER. The enhanced activity of these electrocatalysts are usually attributed to different doping defects, synergistic effects and even the intrinsic carbon defects. Herein, an overview of the defect engineering in carbon-based electrocatalysts for ORR and OER is provided. The different types of intrinsic carbon defects and strategies for the generation of other defects in carbon-based electrocatalysts are presented. The interaction of heteroatoms doped carbon and transition metals (TMs) is also explored. In the end, the existing challenges and future perspectives on defect engineering are discussed.
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 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 | 0.001 |
| 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".