Prussian Blue Analogues “Dressed” in MXene Nanosheets Tightly for High Performance Lithium‐Ion Batteries
Bibliographic record
Abstract
Abstract MXenes, have been considered as a new generation anode material in lithium‐ion batteries for lower lithium‐ion diffusion barriers and superior conductivity. Unfortunately, their structures are prone to aggregation and stacking, hindering further shuttle of lithium ions and electrons, resulting in lower discharge capacity. Therefore, the introduction of interlayer spacers for the preparation of MXene‐based hybrids has attracted much attention. Introducing Prubssian blue analogues (PBAs) as a new interlayer spacer to combine with MXene nanosheets can not only preserve the high conductivity of MXene and inhibit the volume expansion and structural degradation of the PBA component, but also inherit the characteristics of large specific surface area and high porosity of PBAs. By intelligent regulating the size of MXene sheets, Co‐PBA@MXene hybrids with common sandwich‐like structures and superior core‐shell‐like structures have been successfully obtained. Furthermore, Co@M(x:y) hybrids are prepared by intelligently adjusting the shell thickness of MXene through intelligently controlling of the mass ratio between Co‐PBA and MXene. Among them, the Co@M(5:2) anode exhibits an excellent capacity (603 mA h g−1 at 0.2 A g−1 after 100 cycles) and superior long‐term cycling stability due to the protective and conductive properties provided by the MXene shell and multi‐redox pairs and rich porosity from Co‐PBA core.
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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".