Hierarchically Porous Multimetal‐Based Carbon Nanorod Hybrid as an Efficient Oxygen Catalyst for Rechargeable Zinc–Air Batteries
Bibliographic record
Abstract
Abstract The lack of efficient strategies to address the intrinsic activity, site accessibility, and structural stability issues of metal‐carbon hybrid catalysts is restricting their real‐world implementation on the basis of rechargeable zinc–air batteries. Herein, a dual metal–organic frameworks (MOFs) pyrolysis strategy is developed to regulate the intrinsic activity and porous structure of the derived catalysts, where a Fe 2 Ni_MIL‐88@ZnCo_zeolitic imidazolate framework (ZIF), with a hierarchically porous structure, multifunctional components, and an integrated architecture, acts as an ideal precursor to obtain multimetal based porous nanorod (FeNiCo@NC‐P). Benefitting from the synergetic effect of the multimetal components, facilitated reactant accessibility, and the well‐retained integrated structure, the resultant FeNiCo@NC‐P catalyst exhibits an oxygen reduction reaction half‐wave potential of 0.84 V as well as an oxygen evolution reaction potential of 1.54 V at 10 mA cm –2 . Furthermore, the practical application of FeNiCo@NC‐P in the zinc–air battery displays a low voltage gap and long‐term durability (over 130 h at a current density of 10 mA cm –2 ), which outperforms the commercial noble metal benchmarks. This work not only affords a competitive bifunctional oxygen electrocatalyst for zinc–air batteries but also paves a new way to design and fabricate MOF‐derived materials with tunable catalytic properties.
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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".