MoSe<sub>2</sub>@CNT Core–Shell Nanostructures as Grain Promoters Featuring a Direct Li<sub>2</sub>O<sub>2</sub> Formation/Decomposition Catalytic Capability in Lithium‐Oxygen Batteries
Bibliographic record
Abstract
Abstract For lithium‐oxygen batteries (LOBs), the strong oxidant intermediate and byproducts during the charge/discharge process are the main reasons for the degradation of the electrochemical performance. Searching for highly efficient catalysts for the direct formation/decomposition of Li2O2 is essential for the development of LOBs. In this study, core–shell nanostructured MoSe2@CNT with uniform MoSe2 coating layers are purposefully synthesized through a facile hydrothermal strategy to address the negative intermediate and side‐product issues, therefore enhancing the battery performance. The continuous and multiwalled MoSe2 layers can not only work as grain promoters that induce the initial nucleation and growth of equiaxed Li2O2 grains on the cathode surface even under a high rate, but also prevent the byproducts formation from corrosive issues between carbon and electrolyte. Moreover, density functional theory (DFT) calculations reveal the intrinsic layer dependent direct formation/decomposition catalytic capability of 2D MoSe2 and the LiO2 avoidable reaction pathway during the discharge/charge process, theoretically revealing the direct epitaxial growth mechanisms of Li2O2. As a consequence, the MoSe2@CNT cathode exhibited a superior specific capacity over 32 000 mAh g−1, excellent rate capabilities, and ultralong cycle life of 280 cycles at a high rate of 500 mA g−1.
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) | 0.000 | 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".