Fe/Co Double Hydroxide/Oxide Nanoparticles on N‐Doped CNTs as Highly Efficient Electrocatalyst for Rechargeable Liquid and Quasi‐Solid‐State Zinc–Air Batteries
Bibliographic record
Abstract
Abstract Oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) are the cornerstones of rechargeable zinc–air batteries (ZABs). The exploration and rational design of high‐performance, durable, and nonprecious metal bifunctional oxygen electrocatalysts is highly desired for the large‐scale application of rechargeable ZABs. Herein, an effective and straightforward coupling approach is developed to fabricate high‐performance bifunctional ORR/OER electrocatalysts based on novel nanostructured amorphous bimetal Fe/Co hydroxide/oxide nanoparticles (10–20 nm) inlaid on multiwalled N‐dopted carbon nanotubes (FeCo‐DHO/NCNTs). Fe/Co nanoparticles achieve a maximum contact area on the NCNTs, effectively facilitating the rapid electron transport and preventing the aggregation of nanoparticles. Consequently, the as‐prepared FeCo‐DHO/NCNTs show a half‐wave potential of 0.86 V for ORR and a low operating potential of 1.55 V at 10 mA cm−2 for OER in 1.0 m KOH, superior to most bifunctional oxygen electrocatalysts reported so far. Moreover, the assembled all‐solid‐state zinc–air batteries with FeCo‐DHO/NCNTs catalyst as the air electrode demonstrate remarkable stability over long‐term cycling and excellent charging–discharging performance, with a low voltage gap (1.085 V at 60 mA cm−2) and high energy efficiency (60% at 10 mA cm−2) under ambient conditions.
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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".