Recovery of high purity Si from kerf-loss Si slurry waste by flotation method using PEA collector
Bibliographic record
Abstract
Separation and recovery of high-purity Si powder from kerf-loss Si slurry waste is a critical challenge for the photovoltaic industry. A green surfactant poly (propylene glycol) bis (2-aminopropyl ether) (PEA) was employed as a collector to facilitate the separation of Si and SiC from kerf-loss Si waste during flotation process. Single flotation tests of Si and SiC were conducted using 5 x 10(-6) mol/L PEA, respectively. The separation efficiencies of Si and SiC in conjunction with PEA adsorption mechanism were investigated. It was found that the maximum recoveries rate of SiC and Si were 90.59% (pH 9.00) and 80.93% (pH 1.96), respectively. Furthermore, the maximum Si grade was determined as 92.31% at pH 8.95 for the sinking part of the mixture generating excellent floatability and selectivity. Zeta potential measurements, FT-IR spectra, and XPS analyses demonstrated that PEA was present on the surface of Si and SiC through electrostatic and hydrogen-bond interactions. The adsorption mechanism was explained based on the results. This research provides an efficient and environmentally friendly route for the separation and recovery of high purity silicon from kerf-loss Si waste. (C) 2020 Elsevier Ltd. All rights reserved.
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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 | 0.001 | 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".