Hierarchical Hollow‐Nanocube Ni−Co Skeleton@MoO<sub>3</sub>/MoS<sub>2</sub> Hybrids for Improved‐Performance Lithium‐Ion Batteries
Bibliographic record
Abstract
Abstract Improving the performance of anode materials for lithium‐ion batteries (LIBs) is a hotly debated topic. Herein, hollow Ni−Co skeleton@MoS2/MoO3 nanocubes (NCM‐NCs), with an average size of about 193 nm, have been synthesized through a facile hydrothermal reaction. Specifically, MoO3/MoS2 composites are grown on Ni−Co skeletons derived from nickel–cobalt Prussian blue analogue nanocubes (Ni−Co PBAs). The Ni−Co PBAs were synthesized through a precipitation method and utilized as self‐templates that provided a larger specific surface area for the adhesion of MoO3/MoS2 composites. According to Raman spectroscopy results, as‐obtained defect‐rich MoS2 is confirmed to be a metallic 1T‐phase MoS2. Furthermore, the average particle size of Ni−Co PBAs (≈43 nm) is only about one‐tenth of the previously reported particle size (≈400 nm). If assessed as anodes of LIBs, the hollow NCM‐NC hybrids deliver an excellent rate performance and superior cycling performance (with an initial discharge capacity of 1526.3 mAh g−1 and up to 1720.6 mAh g−1 after 317 cycles under a current density of 0.2 A g−1). Meanwhile, ultralong cycling life is retained, even at high current densities (776.6 mAh g−1 at 2 A g−1 after 700 cycles and 584.8 mAh g−1 at 5 A g−1 after 800 cycles). Moreover, at a rate of 1 A g−1, the average specific capacity is maintained at 661 mAh g−1. Thus, the hierarchical hollow NCM‐NC hybrids with excellent electrochemical performance are a promising anode material for LIBs.
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) | 0.001 | 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".