Boosting the Catalytic Activity of Nitrogen Sites by Spin Polarization Engineering for Oxygen Reduction and Wide‐Temperature Ranged Quasi‐Solid Zn–Air Batteries
Bibliographic record
Abstract
Abstract The oxygen reduction reaction (ORR) is a crucial cathode reaction for developing quasi‐solid zinc–air batteries (QZABs) with high energy density. However, the activity and stability of catalysts under extreme conditions have not been fully explore. Herein, a series of systematic experiments and theoretical calculations have been conducted to investigate the potential of introducing Fe x Co y into nitrogen (N)‐doped porous carbon (NPC) via one‐step pyrolysis to form a core–shell structure that can effectively enhance the activity of the catalysts, particularly at low temperatures. Due to the difference in the work function of 5.12, 5.11, and 5.06 eV, the spin‐polarized charge is transferred to the pyridinic‐N site on the surface under the charge transfer. Consequently, the pyridinic‐N site on the surface exhibits varying degrees of magnetic moment 0.024 µ B , which is crucial for forming OOH* and enhances ORR activity. The Fe 5 Co 5 @NPC catalyst is evaluated for QZABs at −40 °C and achieved a power density of up to 117.6 mW cm −2 , which is only 18.7% lower than normal temperature, and a cycle life of up to 300 h. This study provides a means to realize the design of QZABs catalysts in extreme environments and explore their application potential.
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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".