Tuning Diffusion Preferences in Silicon-Based Anodes for Enhanced Rapid and Homogeneous Lithiation
Bibliographic record
Abstract
The inherently sluggish lithiation kinetics of silicon (Si), coupled with its severe volume expansion, causes lithiation retardation and thus exacerbates the failure of Si-based anodes. Here, a Li + -diffusion-preference tuning strategy for homogeneous lithiation of Si has been proposed to address these issues and successfully validated through both simulation and experiments using commercial photovoltaic silicon waste (Si pv ). In detail, Li + preferentially diffuses along grain boundaries (GBs) and then into grains, enabling rapid and homogeneous lithiation throughout the Si particles rather than the conventional outside-to-inside lithiation process that suffers from lithiation retardation. Furthermore, the high-concentration GBs impart isotropic lithiation behavior and induce a fine-grain strengthening effect, enhancing the structural stability of Si pv . The anode prepared by combining Si pv with graphite (Si pv /g) thus demonstrates stable cycling with a capacity retention of 93.8% after 1000 cycles. Even at −20 °C, Si pv /g delivers a 136.2% increase in specific capacity compared to that of commercial Si-based anodes. This work proposes a constructive strategy to essentially improve the electrochemical performance of 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".