Facilitating Highly Reversible Li‐Ion Storage of MoSe<sub>2</sub>‐TiO<sub>2</sub>‐MXene via Double Heterostructures
Bibliographic record
Abstract
Abstract Heterostructured composites, inheriting the integrated properties of the individual components due to the synergistic effect, have engendered great attention in materials science and energy storage. However, conventional biphasic heterostructures not only optimize the performance of the composites but also aggregate the inevitable drawbacks, which can be addressed with the construction of the triphasic heterostructures by introducing an appropriate intermediate phase, significantly. Herein, a two‐dimensional (2D) double‐heterostructures MoSe 2 ‐TiO 2 ‐MXene anode is architected for Li‐ion storage, which combines the advantages of the high theoretical capacity of MoSe 2 and metallic conductivity of MXene. Besides, the in‐situ derived TiO 2 can alleviate the irreversible phase transition of MoSe 2 , arising from the low electronic/ionic conductivities, through electronic coupling effects. Meanwhile, the intermediate phase of TiO 2 can further prevent the restacking issue of MXene, thus sustaining its high conductivity. Finally, the three‐dimensional (3D) printing technology is employed to further improve the kinetics of the electrodes for Li‐ion capacitors (LICs), which deliver the power density of 5563 W kg −1 at an energy density of 51 Wh kg −1 , and a remarkable cycling stability for 20 000 cycles at 1 A g −1 . This work deepens the understanding of the influence of heterostructured engineering on the design of high‐energy/power storage devices.
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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".