Atomistic Origins of Ductility Enhancement in Metal Oxide Coated Silicon Nanowires for Li‐Ion Battery Anodes
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.
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) | 0.000 | 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".