“Ship in a Bottle” Design of Highly Efficient Bifunctional Electrocatalysts for Long-Lasting Rechargeable Zn–Air Batteries
Bibliographic record
Abstract
The poor durability of bifunctional oxygen electrocatalysts is one main bottleneck that suppresses the widespread application of rechargeable metal–air batteries. Herein, a “ship in a bottle” design is achieved by impregnating fine transition metal dichalcogenide nanoparticles into defective carbon pores that act as interconnected nanoreactors. The erected 3D porous conductive architecture provides a “highway” for expediting charge and mass transfer. This design not only delivers a high surface-to-volume ratio to increase numbers of exposed catalytic sites but also precludes nanoparticles from aggregation during cycling owing to the pore spatial confinement effect. Therefore, the long-term plague inherent to nanocatalyst stability can be solved. Moreover, the synergistic coupling effects between defect-rich interfaces and chemical bonding derived from heteroatom-doping boost the catalytic activity and prohibit the detachment of nanoparticles for better stability. Consequently, the developed catalyst presents superior bifunctional oxygen electrocatalytic activities and durability, out-performing the best-known noble-metal benchmarks. In a practical application to rechargeable Zn–air batteries, long-term cyclability for over 340 h is realized at a high current density of 25 mA cm –2 in ambient air while retaining an intact structure. Such a universal “ship in a bottle” design offers an appealing and instructive model of nanomaterial engineering for implementation in various fields.
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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 | 0.001 |
| 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".