A Gas‐Phase Migration Strategy to Synthesize Atomically Dispersed Mn‐N‐C Catalysts for Zn–Air Batteries
Bibliographic record
Abstract
Abstract Mn and N codoped carbon materials are proposed as one of the most promising catalysts for the oxygen reduction reaction (ORR) but still confront a lot of challenges to replace Pt. Herein, a novel gas‐phase migration strategy is developed for the scale synthesis of atomically dispersed Mn and N codoped carbon materials (g‐SA‐Mn) as highly effective ORR catalysts. Porous zeolitic imidazolate frameworks serve as the appropriate support for the trapping and anchoring of Mn‐containing gaseous species and the synchronous high‐temperature pyrolysis process results in the generation of atomically dispersed Mn‐Nx active sites. Compared to the traditional liquid phase synthesis method, this unique strategy significantly increases the Mn loading and enables homogeneous dispersion of Mn atoms to promote the exposure of Mn‐Nx active sites. The developed g‐SA‐Mn‐900 catalyst exhibits excellent ORR performance in the alkaline media, including a high half‐wave potential (0.90 V vs reversible hydrogen electrode), satisfactory durability, and good catalytic selectivity. In the practical application, the Zn–air battery assembled with g‐SA‐Mn‐900 catalysts shows high power density and prominent durability during the discharge process, outperforming the commercial Pt/C benchmark. Such a gas‐phase synthetic methodology offers an appealing and instructive guide for the logical synthesis of atomically dispersed catalysts.
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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) | 0.000 | 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 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".