Tungsten‐Nitride‐Coated Carbon Nanospheres as a Sulfur Host for High‐Performance Lithium‐Sulfur Batteries
Bibliographic record
Abstract
Abstract Lithium‐sulfur batteries have attracted wide attention, owing to their outstanding properties such as high theoretical specific capacity, low cost, and non‐toxic nature. However, the low conductivity of the sulfur cathode and its shuttling effects are still a challenge for the energy‐storage system. In this work, we describe a potential solution to address this challenge, using carbon nanospheres encapsulated in a tungsten nitride (WN) layer, interconnected with WN nanorods. After successfully synthesizing this composite in situ by using a straightforward method, we applied it as the sulfur host for lithium‐sulfur batteries. The results demonstrate a strong chemical trapping ability of the WN shell towards lithium polysulfides (LiPSs), and a strong electron‐transfer ability of the WN nanorods. Together, these effects alleviate LiPSs′ shuttling from carbon nanospheres (CNS) and give rise to a high sulfur content (70 wt %) in the as‐prepared S/WN‐CNS material. When compared to traditional S/N‐CNS electrodes, the tuned S/WN‐CNS cathodes deliver an outstanding electrochemical performance, including a high initial capacity of 1351 mAh g−1 at 0.1 C and superior long‐term cycling stability with 80 % retention of the initial capacity with 3 mg cm−2 after 500 cycles at 0.5 C. As such, a high specific capacity, excellent rate capacity, and long cycling stability are achieved. Our approach provides a path to a broad class of high‐performance Li‐S battery applications based on nanostructured WN materials.
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".