Nanostructured silicon on stainless steel as a binder-free and carbon-free anode for lithium-ion batteries
Bibliographic record
Abstract
The chemical vapor deposition method with an Au thin film as the catalyst and SiCl 4 as the Si-containing precursors was used to fabricate nanostructured Si, including Si nanowires, on stainless-steel substrates. All samples synthesized at 750–1000 °C and 15–60 min showed Si deposition, with Si nanowires obtained at 1000 °C for the duration of 25–30 min. The nanostructured Si produced on the conductive stainless-steel substrates was used directly as a working electrode in a Li-ion battery (LIB) cell, enabling a simple binder-free and carbon-free cell assembly. The electrochemical performance of the nanostructured Si electrode materials in LIB, including specific capacity, capacity retention, Coulombic efficiency, and rate capability, was investigated. The nanostructured Si sample prepared at 1000 °C and 20 min (SiNS-20) showed the best electrochemical performance, delivering 3379 mAh g −1 for the second discharge specific capacity when cycled at a rate of 0.5 C, corresponding to the highest possible Li–Si phase of Li 15 Si 4 . The discharge capacity remained at 2197 mAh g −1 after 50 cycles with a high Coulombic efficiency of 97%, showing promising performance as LIB electrodes. More importantly, the synthetic approach of nanostructured Si directly on a conductive substrate provides a method towards the binder-free and carbon-free electrode design for use in 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 | 0.000 | 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 | 0.001 |
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".