Recent Developments and Understanding of Novel Mixed Transition‐Metal Oxides as Anodes in Lithium Ion Batteries
Bibliographic record
Abstract
Mixed transition‐metal oxides (MTMOs), including stannates, ferrites, cobaltates, and nickelates, have attracted increased attention in the application of high performance lithium‐ion batteries. Compared with traditional metal oxides, MTMOs exhibit enormous potential as electrode materials in lithium‐ion batteries originating from higher reversible capacity, better structural stability, and high electronic conductivity. Recent advancements in the rational design of novel MTMO micro/nanostructures for lithium‐ion battery anodes are summarized and their energy storage mechanism is compared to transition‐metal oxide anodes. In particular, the significant effects of the MTMO morphology, micro/nanostructure, and crystallinity on battery performance are highlighted. Furthermore, the future trends and prospects, as well as potential problems, are presented to further develop advanced MTMO anodes for more promising and large‐scale commercial applications of lithium‐ion batteries.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".