Influence of Silicon Nanowire Morphology and Defect on Its Lithium-Ion Battery Anode Performance
Bibliographic record
Abstract
This study investigates the electrochemical performance of Si nanowires (SiNWs), synthesized through chemical vapor deposition, by different mechanisms of vapor–liquid–solid (VLS) and vapor–solid–solid (VSS), for applications as anodes in lithium-ion batteries (LIBs). The synthesized VLS-SiNWs and VSS-SiNWs exhibit different morphologies and defect densities, leading to varying performance in terms of specific capacity, cycling stability, and capacity retention. Despite morphological variations, all SiNWs have a specific capacity greater than 2500 mA h g –1, along with reversible rate capabilities at various C-rates, and Coulombic efficiencies exceeding 90%. Cyclic voltammograms confirm the presence of a solid electrolyte interphase (SEI) and Li–Si phase transition leading to the formation of Li 15 Si 4 at room temperature, with the VSS-SiNW electrode experiencing an irreversible loss of active materials, while the VLS-SiNW electrode shows gradual kinetic enhancement during cycling. Additionally, electrochemical impedance spectroscopy is employed to evaluate the bulk resistance, SEI resistance, as well as the charge transfer resistance in the electrode materials and to understand the effect on their specific capacity. X-ray diffraction analysis on the electrodes after cycling confirms the formation of amorphous Li 15 Si 4 in VLS-SiNWs and the irreversible formation of crystalline Li 15 Si 4 for the VSS-SiNWs. By maintaining consistent control over parameters, such as loading mass, areal density, NW diameter, and crystallite size across the samples, this study highlights the superior LIB anode performance of VLS-SiNWs in all evaluated metrics compared to that of VSS-SiNWs, mainly due to the lower defect density present in the VLS-SiNWs.
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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) | 0.000 | 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 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".