Separation and Recovery of SiC Particles Discharged from Silicon Wafer Production Process
Why this work is in the frame
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Bibliographic record
Abstract
In the slicing process of silicon wafer from silicon single crystal, it has been the general way to cut silicon by wire saws with the lubricant mixture of silicon carbide, as SiC, particles and wrapping oil. After slicing the silicon single crystal, the waste liquor containing SiC and silicon powders is discharged from the process. The particle sizes of SiC and Si are about 10μm and 1μm, respectively and the weight ratio is about 9:1. The particles discharged from slicing waste liquor become the mixture of SiC and SiO2, when the waste liquor is burned after treating the lubricant oil by a filter press. In terms of the minimization of wastes and environment, it is preferable to separate and recover the valuable SiC from SiO2. In order to solve the problem mentioned above, flotation method can be applied to accomplish the separation of SiC from SiO2. The cationic surfactants of dodecyl-tri-methyl-ammonium chloride (abbreviated as DTMAC hereafter) and tri-methyl-octyl-ammonium chloride (abbreviated as TMOAC hereafter) were used in this study. The adsorption amount of surfactants on SiC and SiO2 particles was measured. The flotation behaviors of SiC and SiO2 were investigated by changing pH, gas flow rate and flotation time in the presence of DTMAC. The purity and yield of SiC were also discussed in the flotation process comprising of roughing, cleaning and scavenging steps. A series of flotation process for SiC gave the purity and yield of 99.7% and 96.7%, respectively.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it