Encapsulating sulphur inside Magnéli phase <scp> Ti <sub>4</sub> O <sub>7</sub> </scp> nanotube array for high performance lithium sulphur battery cathode
Bibliographic record
Abstract
Abstract The lithium–sulphur battery is a promising system for next‐generation energy storage because of its high energy density and the abundant supply of sulphur. In this study, Magnéli phase Ti 4 O 7 nanotube arrays (NTA) were grown on titanium nitride mesh substrates via anodization of titanium mesh followed by high‐temperature reduction under a hydrogen atmosphere. When prepared into a composite material with sulphur via electrodeposition, Ti 4 O 7 NTA provides sulphur with high electronic conductivity and strong polysulphide chemisorption during battery operation. The structure of NTA allows sufficient access of incorporated sulphur to the liquid electrolyte and supports high sulphur loadings per unit area of the electrode. The application of an additional layer of conductive carbon coating to confine electrodeposited sulphur inside the NTA further improved the cell cycling performance. Under sulphur loadings of around 2.0 mg/cm 2 , high values of specific capacity (1604 mAh/g at a 1/20 C rate), ultra‐low capacity decay rate (0.03% per cycle for 1800 cycles), and versatile rate capability (660 mAh/g at 2 C and 500 mAh/g at 4 C) were achieved. Under high sulphur loadings of around 5.0 mg/cm 2 , stable cycling (decay rate below 0.10% per cycle) and high areal capacity (4.97 mAh/cm 2 ) were attained.
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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".