A Rechargeable Urea‐Assisted Zn‐Air Battery With High Energy Efficiency and Fast‐Charging Enabled by Engineering High‐Energy Interfacial Structures
Bibliographic record
Abstract
Abstract Electrochemical urea oxidation reaction (UOR) offers a promising alternative to the oxygen evolution reaction (OER) in clean energy conversion and storage systems. Nickel‐based catalysts are regarded as highly promising electrocatalysts for the UOR. However, their effectiveness is significantly hindered by the unavoidable self‐oxidation reaction of nickel species during UOR. To address this challenge, we proposed an interface chemistry modulation strategy to boost UOR kinetics by creating a high‐energy interfacial heterostructure. This heterostructure incorporates Ag at the CoOOH@NiOOH heterojunction interface, where strong interactions significantly promote the electron exchanges at the heterojunction interface between ‐OH and ‐O groups. Consequently, the improved electron delocalization leads to the formation of stronger bonds between Co sites and urea CO(NH 2 ) 2 , promoting a preference for urea to occupy Co active sites over OH*. The resulting catalyst, Ag−CoOOH@NiOOH, demonstrates ultrahigh UOR activity with a low potential of 1.33 V at 100 mA cm −2 . The fabricated catalyst exhibits a mass activity over 11.9 times greater than the initial cobalt oxyhydroxide. The rechargeable urea‐assisted zinc‐air batteries (ZABs) achieve a record‐breaking energy efficiency of 74.56 % at 1 mA cm −2 , remarkable durability (1000 hours at a current density of 50 mA cm −2 ), and quick charge performances.
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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".