Defect Engineering of Chalcogen‐Tailored Oxygen Electrocatalysts for Rechargeable Quasi‐Solid‐State Zinc–Air Batteries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A critical bottleneck limiting the performance of rechargeable zinc–air batteries lies in the inefficient bifunctional electrocatalysts for the oxygen reduction and evolution reactions at the air electrodes. Hybridizing transition‐metal oxides with functional graphene materials has shown great advantages due to their catalytic synergism. However, both the mediocre catalytic activity of metal oxides and the restricted 2D mass/charge transfer of graphene render these hybrid catalysts inefficient. Here, an effective strategy combining anion substitution, defect engineering, and the dopant effect to address the above two critical issues is shown. This strategy is demonstrated on a hybrid catalyst consisting of sulfur‐deficient cobalt oxysulfide single crystals and nitrogen‐doped graphene nanomeshes (CoO 0.87 S 0.13 /GN). The defect chemistries of both oxygen‐vacancy‐rich, nonstoichiometric cobalt oxysulfides and edge‐nitrogen‐rich graphene nanomeshes lead to a remarkable improvement in electrocatalytic performance, where CoO 0.87 S 0.13 /GN exhibits strongly comparable catalytic activity to and much better stability than the best‐known benchmark noble‐metal catalysts. In application to quasi‐solid‐state zinc–air batteries, CoO 0.87 S 0.13 /GN as a freestanding catalyst assembly benefits from both structural integrity and enhanced charge transfer to achieve efficient and very stable cycling operation over 300 cycles with a low discharge–charge voltage gap of 0.77 V at 20 mA cm −2 under ambient conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it