Nanoreactor‐Driven Uniform Nano ZnS Deposition in Tunable Porous Carbon Spheres for High‐Performance Zn‐S Batteries
Bibliographic record
Abstract
Abstract Zn‐S batteries have garnered widespread attention in recent years due to their higher safety and low cost. However, challenges such as incomplete sulfur redox reactions and the tendency of ZnS to agglomerate have impeded the continued advancement of high‐performance Zn‐S batteries. Hollow hierarchical porous carbon spheres (HCs) are designed as efficient sulfur hosts for Zn‐S batteries. The tailored HCs, featuring optimized shell thickness, hierarchical porosity, facilitate uniform nano‐ZnS deposition, and improve ion/electron transport, which are revealed by in situ impedance technology. This nano reactor design ensures highly reversible S‐ZnS conversion, reducing internal polarization and mitigating structural degradation. Electrochemical tests demonstrate outstanding cycling stability, with minimal capacity decay (0.068%) over 500 cycles, and 463 mAh g −1 reversible capacity at 5 A g −1 . Finite element simulations further confirm the effective stress dispersion of HCs, preserving electrode integrity. This work provides a promising strategy for developing high‐performance Zn‐S batteries.
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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".