Hollow Porous Nitrogen‐Doped Carbon‐Confined FeP/Fe <sub>2</sub> P Nanoparticle‐Armored Catalyst for Efficient Oxygen Reduction Reaction in Aqueous/Flexible Zinc‐Air Batteries
Bibliographic record
Abstract
ABSTRACT The design of nanoparticles confined in hollow N‐doped carbon structures is crucial for improving the oxygen reduction reaction (ORR) kinetics, yet achieving this remains a significant challenge. In this work, hollow porous nitrogen‐doped carbon encapsulated FeP/Fe 2 P (H‐FeP/Fe 2 P) were successfully constructed via a templating method combined with dopamine hydrochloride coating, acid etching, and subsequent high‐temperature phosphating. In situ spectroelectrochemical investigations and theoretical results demonstrate that the adsorbed hydroxyl species (*OH) can be readily released from the catalyst surface by facilitating the dissociation of oxygen–oxygen bonds at the active sites of Fe, thus accelerating the kinetics of the ORR. The optimized H‐FeP/Fe 2 P achieves a high limiting current density of 5.5 mA cm −2 and a low Tafel slope of 39 mV dec −1 in 0.1 M KOH, outperforming corresponding solid samples and most reported transition metal phosphide catalysts. Moreover, the H‐FeP/Fe 2 P‐based aqueous ZAB exhibits remarkable performance, including high peak power density (175 mW cm −2 ), large specific capacity (813 mAh g −1 Zn ), and stable charge/discharge stability over 800 h. The corresponding solid‐state zinc‐air battery also delivers a high peak power density of 101 mW cm −2 and excellent flexibility. The carbon confinement strategy proposed in this study opens new avenues for developing high‐performance and cost‐effective non‐precious metal ORR catalysts in zinc‐air batteries.
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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".