Few‐Layer MoSe<sub>2</sub> Nanosheets with Expanded (002) Planes Confined in Hollow Carbon Nanospheres for Ultrahigh‐Performance Na‐Ion Batteries
Bibliographic record
Abstract
Abstract Sodium‐ion batteries (SIBs) are considered as a promising alternative to lithium‐ion batteries, due to the abundant reserves and low price of Na sources. To date, the development of anode materials for SIBs is still confronted with many serious problems. In this work, encapsulation‐type structured MoSe 2 @hollow carbon nanosphere (HCNS) materials assembled with expanded (002) planes few‐layer MoSe 2 nanosheets confined in HCNS are successfully synthesized through a facile strategy. Notably, the interlayer spacing of the (002) planes is expanded to 1.02 nm, which is larger than the intrinsic value of pristine MoSe 2 (0.64 nm). Furthermore, the few‐layer nanosheets are space‐confined in the inner cavity of the HCNS, forming hybrid MoSe 2 @HCNS structures. When evaluated as anode materials for SIBs, it shows excellent rate capabilities, ultralong cycling life with exceptional Coulombic efficiency even at high current density, maintaining 501 and 471 mA h g −1 over 1000 cycles at 1 and 3 A g −1 , respectively. Even when cycled at current densities as high as 10 A g −1 , a capacity retention of 382 mA h g −1 can be achieved. The expanded (002) planes, 2D few‐layer nanosheets, and unique carbon shell structure are responsible for the ultralong cycling and high rate performance.
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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".