Cobalt‐Doped SnS<sub>2</sub> with Dual Active Centers of Synergistic Absorption‐Catalysis Effect for High‐S Loading Li‐S Batteries
Bibliographic record
Abstract
Abstract The application of Li‐S batteries is hindered by low sulfur utilization and rapid capacity decay originating from slow electrochemical kinetics of polysulfide transformation to Li 2 S at the second discharge plateau around 2.1 V and harsh shuttling effects for high‐S‐loading cathodes. Herein, a cobalt‐doped SnS 2 anchored on N‐doped carbon nanotube (NCNT@Co‐SnS 2 ) substrate is rationally designed as both a polysulfide shield to mitigate the shuttling effects and an electrocatalyst to improve the interconversion kinetics from polysulfides to Li 2 S. As a result, high‐S‐loading cathodes are demonstrated to achieve good cycling stability with high sulfur utilization. It is shown that Co‐doping plays an important role in realizing high initial capacity and good capacity retention for Li‐S batteries. The S/NCNT@Co‐SnS 2 cell (3 mg cm −2 sulfur loading) delivers a high initial specific capacity of 1337.1 mA h g −1 (excluding the Co‐SnS 2 capacity contribution) and 1004.3 mA h g −1 after 100 cycles at a current density of 1.3 mA cm −2 , while the counterpart cell (S/NCNT@SnS 2 ) only shows an initial capacity of 1074.7 and 843 mA h g −1 at the 100th cycle. The synergy effect of polysulfide confinement and catalyzed polysulfide conversion provides an effective strategy in improving the electrochemical performance for high‐sulfur‐loading Li‐S batteries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".