Nitric Acid Leaching of Base Metals from Waste PDP Electrode Scrap and Recovery of Ruthenium Content from Leached Residues
Bibliographic record
Abstract
Nitric acid leaching of waste plasma display panel (PDP) electrode scrap was investigated as a part of development for a pre-treatment process to increase Ru content in the scrap. Leaching performance was evaluated in terms of different experimental parameters such as nitric acid concentration, reaction temperature and time.An aqueous nitric acid leaching solution with a concentration range of 1.5 M–3.0 M at 60°C and 1.5 M–4.2 M at 75°C demonstrated as the most effective condition for the selective removal of Pb and Ba from waste PDP scrap powders with about 90% of Pb and 95% of Ba leached in 30 min. The rate of dissolution decreased after a certain level of HNO3 concentration due to formation of Pb(NO3)2 which has limited solubility in the aqueous solution. Other impurities such as Bi, Zn, Ag and Co were dissolved at the level of 75%–90% at all the leachant concentrations, leaching time and temperatures applied, while Si, Al and Fe showed a poor leachability with only 7%, 30% and 40% dissolution, respectively. Ru and Zr were almost insoluble in an aqueous nitric acid solution. The total concentration of Ru in the undissolved residue (27.96%) of the scrap powder after nitric acid leaching was brought up to 93.8% from the initial concentration (14.43%) of the scrap in the final process. The precipitation behavior of Pb(NO3)2 as well as the solubility of SiO2 were also investigated.
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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) | 0.000 | 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 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".