Reduction behaviour of rice husk ash for preparation of high purity silicon
Bibliographic record
Abstract
The reduction of rice husk ash (RHA) silica for the preparation of high purity silicon was studied using magnesium as the reducing agent. Composite magnesium–RHA pellets with magnesium content varying 0–25 wt-% in excess of stoichiometry requirement were made and heated in the temperature range of 600–900°C under flowing argon. It was found through differential thermal analysis and temperature profile recording that the reaction of RHA silica with magnesium was triggered at ∼575°C. Quantitative X-ray diffraction analyses of the reduction products showed that both initial magnesium content of the pellets and the reduction dwell temperature had a significant influence on the yield of silicon. In this study, a charge with 5 wt-% magnesium in excess of the stoichiometric amount at a reduction temperature of 900°C gave a maximum silicon yield.On a étudié la réduction de la silice de cendre de balle de riz (RHA) dans la préparation de silicium à haut degré de pureté en utilisant du magnésium comme agent de réduction. On a fabriqué des boulettes composites de magnésium-RHA avec une teneur en magnésium variant de 0 à 25% en poids en excès des besoins de la stoechiométrie et on les a chauffées dans la gamme de température de 600 à 900°C sous un flux d’argon. On a trouvé, par analyse thermique différentielle (DTA) et par enregistrement du profil de température, que la réaction de la silice de RHA avec le magnésium était déclenchée à environ 575°C. Des analyses quantitatives par XRD des produits de réduction ont montré que tant la teneur initiale en magnésium des boulettes que la température de maintien de la réduction avaient une influence importante sur le rendement en silicium. Dans cette étude, une charge avec 5% en poids d’excès de magnésium sur la quantité stoechiométrique à la température de réduction de 900°C donnait un rendement maximal de silicium.
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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".