Indium Recovery from Waste Liquid Crystal Display via Chloride Volatilization Process: Thermodynamic Computation
Bibliographic record
Abstract
With the increase of the scrap liquid crystal displays (LCDs), recycling indium from waste LCDs has captured an international attention.Chloride metallurgy is a promising method for indium recovery from LCD panels, due to the lower boiling point of indium chloride.In the present study, thermodynamic analyses of indium recovery from waste LCDs via chloride volatilization process by the HSC Chemistry software was carried out to understand the reaction mechanism between chlorinating agent and LCDs to avoid adverse factors, and simultaneously obtain the optimal conditions for the extraction of indium.The results show that the recovered indium from LCDs with HCl o as the chlorinating agent from the PVC pyrolysis is feasible, with the chlorination temperature controlled between 134.49 and 554.25 C, o and the evaporation temperature higher than 490 C, and simultaneously, the oxygen partial pressure controlled or under anaerobic conditions.As such, the influences of SiO , Al O and Fe O , contained in LCDs, can be ignored or avoided, and only CaO, K O and Na O 2 2 3 2 3 2 2 would consume partial pressure of HCl gas, reducing the indium recovery reaction rate.The present study might provide important inspiration for indium recovery from waste LCDs via chloride volatilization process.
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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 | 0.001 |
| 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 | 0.001 |
| 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".