Atomistic Origins of Ductility Enhancement in Metal Oxide Coated Silicon Nanowires for Li‐Ion Battery Anodes
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Silicon nanowires (SiNWs) are a promising anode material for Li‐ion batteries due to their exceptionally high charge capacity. However, direct implementation is hindered by large volume expansion induced during lithiation, which results in mechanical failure during repeated charge cycling. Recent experimental works show thin metal oxide coatings can significantly increase the cycle stability of SiNWs. However, the deformation mechanisms underpinning this performance enhancement are not understood, presenting an opportunity for a fundamental investigation of core–shell mechanics. In this study, molecular dynamics simulations investigating the mechanical behavior of silica‐ and alumina‐coated SiNWs under uniaxial tension are performed. Metal oxide coated nanowires possess significantly improved ductility, increasing the elongation to failure from 16% to greater than 47%. This occurs without significant reduction in tensile strength, resulting in apparent toughness 2–4 times that of uncoated nanowires. During loading, the oxide coating absorbs strain energy through breaking of bonds between highly coordinated atoms. At the same time, the coating maintains the structural integrity of the silicon core by increasing the defect nucleation rate from the core‐coating interface, preventing localized deformation. Under both athermal (0 K) and room temperature conditions, the underlying deformation mechanism changes from amorphization within a localized shear band to dislocation twinning and large‐scale amorphization.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it