Enhanced Alkali-Ion Adsorption in Strongly Bonded Two-Dimensional TiS<sub>2</sub>/MoS<sub>2</sub> van der Waals Heterostructures
Bibliographic record
Abstract
Despite their remarkable properties, many two-dimensional (2D) materials do not possess the desired characteristics to enhance the energy density, rate performance, and cycle life of batteries when used as standalone materials for battery electrodes. However, engineering 2D van der Waals (vdW) heterostructures by stacking different 2D materials offers new opportunities for battery electrodes, combining desirable features and overcoming the limitations of the constituent 2D layers. In this work, using first-principles calculations, we investigated the electronic structure, thermal stability, stress–strain response, alkali (Li/Na/K)-ion adsorption, and diffusion characteristics of 2D heterostructures of titanium disulfide (TiS 2 ) and molybdenum disulfide (MoS 2 ) monolayers with attractive applications in alkali-metal ion batteries (AMIBs). This work revealed relatively strong interlayer interactions in the heterostructure, resulting in significant enhancements in the adsorption of alkali ions compared to the respective monolayers. This work demonstrates that TiS 2 /MoS 2 heterostructures offer excellent mechanical flexibility, increased strength, and greater strain endurance. The results indicate that TiS 2 /MoS 2 heterostructures are promising materials for next-generation flexible anodes for AMIBs.
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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".