Advances and Future Prospects of Micro‐Silicon Anodes for High‐Energy‐Density Lithium‐Ion Batteries: A Comprehensive Review
Bibliographic record
Abstract
Abstract Silicon (Si), stands out for its abundant resources, eco‐friendliness, affordability, high capacity, and low operating potential, making it a prime candidate for high‐energy‐density lithium‐ion batteries (LIBs). Notably, the breakthrough use of nanostructured Si (nSi) has paved the way for the commercialization of Si anodes. Despite this, challenges like high processing costs, severe side reactions, and low volumetric energy density have impeded widespread industrial adoption. Micron‐scale Si (µSi) has always faced setbacks compared to nSi due to its greater volume expansion. However, recent years have witnessed a resurgence of interest in µSi‐based anodes. Capitalizing on its inherent advantages, including low cost and high tap density, µSi has once again captured the attention of both academic and industrial communities. This review begins by contrasting the strengths and weaknesses of µSi and nSi, then outline potential solutions to enhance µSi performance, covering aspects like structural regulation, composite anodes, binder design, and electrolyte exploration. Additionally, this work explores the application of machine learning‐assisted high‐throughput screening. Concluding the review, this work provides insights into the future prospects of µSi in LIBs, outlining challenges and proposing integrated coping strategies. This review anticipates that it will provide valuable perspectives for the commercial application of high‐energy‐density Si‐based anodes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".