Highly selective solvent extraction of Zn(<scp>II</scp>) and Cr(<scp>III</scp>) with trioctylmethylammonium chloride ionic liquid
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Bibliographic record
Abstract
Abstract This study investigates the recovery of Zn(II) and Cr(III) from aqueous solutions based on solvent extraction with trioctylmethylammonium chloride [TOMA + ][Cl‐], commercialy named Aliquat 336. Single metal solutions and binary mixtures of both metals were considered. The effect of relevant operating conditions such as pH, contact time, initial concentration, O/A phase volumetric ratio, and temperature were evaluated. Additionally, loading capacity and stripping studies were performed. Results showed that [TOMA + ][Cl − ] is an effective extracting agent for Zn(II), reaching maximum removal capacity at pH 1.8 and demonstrating fast extraction kinetics. Extraction efficiencies above 99% were achieved at 0.5, 0.75, and 1.00 O/A volumetric phase ratios for 0.1 g/L initial Zn(II) concentration. At 1 g/L and 10 g/L concentration, for the same O/A ratios, approximately 88% of the initial Zn(II) was extracted. In contrast, it was found that negligible amounts of Cr(III) were transferred to the [TOMA + ][Cl − ] phase at the 1‐5 pH range. Selectivity studies showed that Zn(II) removal is boosted in the presence of Cr(III), although no Cr(III) is extracted. [TOMA + ][Cl − ] exhibited a high Zn(II) storage capacity, since after 25 loading cycles with 1 g/L, the loading capacity reached approximately 13.5 g/L, and after five loading cycles with 5 g/L, the capacity reached 19.4 g/L. Stripping tests revealed that NaOH is an efficient agent for the removal of Zn(II) from the ionic liquids, reaching 98.5% removal after two cycles, whereas HNO 3 is not a suitable agent, reaching less than 40% removal after three cycles. [TOMA + ][Cl − ] revealed high potential for separating Zn(II) from Cr(III).
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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 | 0.001 |
| 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