Hf and Co Dual Single Atoms Co‐Doped Carbon Catalyst Enhance the Oxygen Reduction Performance
Bibliographic record
Abstract
Abstract Single‐atom metal‐doped M–N–C (M═Fe, Co, Mn, or Ni) catalysts exhibit excellent catalytic activity toward oxygen reduction reactions (ORR). However, their performance still has a large gap considering the demand for their practical applications. This study reports a high‐performance dual single‐atom doped carbon catalyst (HfCo–N–C), which is prepared by pyrolyzing Co and Hf co‐doped ZIF‐8 . Co and Hf are atomically dispersed in the carbon framework and coordinated with N to form Co–N 4 and Hf–N 4 active moieties. The synergetic effect between Co–N 4 and Hf–N 4 significantly enhance the catalytic activity and durability of the catalyst. In an acidic medium, the ORR half‐wave potential ( E 1/2 ) of the catalyst is up to 0.82 V , which is much higher than that of the Co–N–C catalyst without Hf co‐doping (0.80 V). The kinetic current density of the catalyst is up to 2.49 A cm −2 at 0.85 V , which is 1.74 times that of the Co–N–C catalyst without Hf co‐doping. Moreover, the catalyst exhibits excellent cathodic performance in single proton exchange membrane fuel cells and Zn–air batteries. Furthermore, Hf co‐doping can effectively suppress the formation of H 2 O 2 , resulting in significantly improved stability and durability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".