Si‐, Ge‐, Sn‐Based Anode Materials for Lithium‐Ion Batteries: From Structure Design to Electrochemical Performance
Bibliographic record
Abstract
As state‐of‐the‐art rechargeable energy‐storage devices, lithium‐ion batteries (LIBs) are widely applied in various areas, such as storage of electrical energy converted from renewable energy and powering portable electronic devices and electric vehicles (EVs). Nevertheless, the energy density and working life of current commercial LIBs cannot satisfy the rapid development of these applications. It is urgently required that the electrochemical performance of LIBs, which is mainly determined by the electroactive electrode materials, is improved. However, commercial graphite‐based anode materials deliver a relatively low theoretical capacity of 372 mA h g −1 , severely hindering the increase of the energy density of LIBs. Recently, M‐based anode (M represents Si, Ge, and Sn) materials have attracted great attention due to their high theoretical capacity and reasonable anodic voltage. However, the application of M‐based anode materials is seriously limited by a series of several critical problems, such as poor kinetics and huge volume change on cycling. Here, these fundamental problems leading to poor electrochemical performance are discussed, and a series of reasonable nanostructures for M‐based anodes with improved electrochemical performance is summarized, demonstrating that the dimensional control in structure design plays a critical role for solving these problems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".