Silicon‐Based Anodes for Lithium‐Ion Batteries: From Fundamentals to Practical Applications
Bibliographic record
Abstract
Silicon has been intensively studied as an anode material for lithium-ion batteries (LIB) because of its exceptionally high specific capacity. However, silicon-based anode materials usually suffer from large volume change during the charge and discharge process, leading to subsequent pulverization of silicon, loss of electric contact, and continuous side reactions. These transformations cause poor cycle life and hinder the wide commercialization of silicon for LIBs. The lithiation and delithiation behaviors, and the interphase reaction mechanisms, are progressively studied and understood. Various nanostructured silicon anodes are reported to exhibit both superior specific capacity and cycle life compared to commercial carbon-based anodes. However, some practical issues with nanostructured silicon cannot be ignored, and must be addressed if it is to be widely used in commercial LIBs. This Review outlines major impactful work on silicon-based anodes, and the most recent research directions in this field, specifically, the engineering of silicon architectures, the construction of silicon-based composites, and other performance-enhancement studies including electrolytes and binders. The burgeoning research efforts in the development of practical silicon electrodes, and full-cell silicon-based LIBs are specially stressed, which are key to the successful commercialization of silicon anodes, and large-scale deployment of next-generation high energy density 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.002 |
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".