Titanium nitride nanorod array/carbon cloth as flexible integrated host for highly stable lithium–sulfur batteries
Bibliographic record
Abstract
Abstract Lithium–sulfur (Li–S) batteries have been regarded as promising energy‐storage systems, due to their high theoretical capacity and energy density. However, the carbonaceous sulfur hosts suffer from weak binding force between the hosts and polysulfides, restricting the cyclic stability of sulfur electrode. Meantime, the presence of binder and conductive agent in the traditional electrode reduces its energy density. This study demonstrates that titanium nitride (TiN) nanorod array on carbon cloth (CC) is employed as a flexible host for highly stable Li–S batteries via solvothermal synthesis‐nitridation strategy. On the one hand, the flexible integrated network composed of three‐dimensional TiN nanorod array and CC significantly improves the conductivity, increases the electron transport and electrolyte penetration of cathode. On the other hand, the 3D structure of TiN/CC and the enhanced polarity of TiN effectively strengthen the physical and chemical double adsorption for polysulfides. As a result, the combination of TiN nanorod array and CC synergistically promotes sulfur utilization and electrochemical performances of S@TiN/CC cathode. A discharge capacity of 1015.2 mAh·g −1 at 0.5C after 250 cycles and 604.1 mAh·g −1 at 3C after 250 cycles is realized. Under a larger current density of 5C, the resulting S@TiN/CC cathode maintains a high discharge capacity of 666.6 mAh·g −1 and the Coulombic efficiency of about 100%.
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".