Synthesis of silicon nanowires from carbothermic reduction of silica fume in RF thermal plasma
Bibliographic record
Abstract
Silica fume, which is a by‐product of metallurgical‐grade silicon production, is a low cost material with high SiO2 concentration and small particle size (<1 µm). These properties make it a good candidate for radio‐frequency (RF) thermal plasma processing. In this article, the use of silica fume as a reactant is promoted for the RF thermal plasma synthesis of high‐charge capacity, high cyclability anode materials for lithium‐ion batteries. In order to obtain these materials, the carboreduction of silica fume is followed by an in‐flight growth of silicon nanowires in the plasma reactor. The impact of the addition of catalysts and the use of different plasma gases on the yield and the properties of the product has been investigated by X‐ray diffractometry (XRD), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), energy dispersion spectrometry (EDS), and transmission electron microscopy (TEM). It is found that the addition of metal catalysts has a significant effect on the synthesis. It not only promoted the formation of silicon nanowires, but also improved the yield of the reaction upwards of 300%. An insight on the mechanisms leading to the silicon nanowires formation is also discussed in the results section.
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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".