Extraction of Zinc and Chromium(III) and Its Application to Treatment of Alloy Electroplating Wastewater
Bibliographic record
Abstract
Extraction of Zn and Cr(III) with solvents containing (1) DEHPA, di-2-ethylhexyl phosphoric acid, (2) PC-88A, 2-ethylhexylphosphonic acid mono-2-ethylhexyl ester, (3) Cyanex 272, bis(2,4,4-trimethylpentyl)phosphinic acid, and (4) Cyanex 302, bis(2,4,4-trimethylpentyl)thiophosphinic acid, was studied with a view to separately recovering the two metals from alloy electroplating wastewater. All solvents extracted Zn well. Extraction of Cr(III) required a higher pH and Cr(III) hydroxide precipitation placed an upper limit on the pH that could be used, which in turn limited the extraction percentage. Among the four extractants, DEHPA performed the best, achieving close to 100% extraction at 0.1 M. The extracted Cr(III) could not be completely stripped (back-extracted). By using ammoniated DEHPA, both the precipitation and incomplete stripping problems were averted. The different performances of the DEHPA and ammoniated DEHPA solvents were explained in terms of the different extracted Cr(III) species and the slow kinetics of reactions involving ligand displacement of the Cr(III) species. This explanation was supported by stoichiometric and UV–visible spectral data. A flow sheet based on Zn extraction with DEHPA and Cr(III) extraction with ammoniated DEHPA was developed for treatment of Zn–Cr(III) alloy electroplating wastewater. The flow sheet was tested in an automated mixer–settler solvent extraction system. The treated wastewater contained <0.1 mg l−1 Zn and <1 mg l−1 Cr(III), and the recovered metals were in good purity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".