A comprehensive review of silicon anodes for high-energy lithium-ion batteries: Challenges, latest developments, and perspectives
Bibliographic record
Abstract
Lithium-ion batteries (LIBs) have become the predominant and widely used energy storage systems in portable electronic devices, such as video cameras, smartphones, laptops, and plug-in hybrid vehicles, along with in stationary energy storage applications like power banks and backup energy storage systems. Moreover, they are widely used in the latest models of all electric vehicles (EVs) and hybrid electric vehicles (HEVs). However, to meet the demand for EVs and HEVs, notable improvements in commercially available LIBs are required. These include improving energy density, cycling life, power and rate capabilities, safety, and cost. In spite of the initial commercialization of LIBs in 1990 by Sony, current commercial LIBs still rely on graphite/carbon as the anode material, providing a theoretical capacity of approximately 372 mAh g−1. The search is on for viable alternatives to graphite with higher capacity materials, and silicon (Si) has emerged as a promising candidate with a theoretical capacity of approximately 4200 mAh g−1. However, Si anodes face several challenges, such as considerable volume expansion during the lithiation/delithiation process, which leads to significant crystallographic-related phase-induced stresses, continuous formation of a solid electrolyte interface (SEI), and cycle retention decay. The volume expansion caused by stress leads to the pulverization of Si electrodes. This results in the loss of electrical contact with the substrate or current collector, causing a significant and rapid decrease in capacity and ultimately leading to battery failure. This review explores the challenges associated with Si-based anodes, their underlying causes, and their comparative advantages over conventional anodes. Furthermore, the review discusses innovative solutions to address these challenges, such as utilizing novel binders, electrolyte additives, structural, interfacial, composite engineering techniques, and prelithiation methods. Finally, considering the material cost, the suggestion to transition entirely to using up to 100% wt. silicon for anode development is proposed, streamlining practical and commercial implementation in future LIBs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".