Synergistic role of Cyanex 272 saponification and acetate buffering in selective Co(II)/Ni(II) separation via Green Emulsion Liquid Membrane (GELM)
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Bibliographic record
Abstract
This study explores the selective extraction of cobalt and nickel ions from acidic solutions using a Green Emulsion Liquid Membrane (GELM) system. Corn oil was selected as a green diluent based on shake-out tests. The membrane phase was formulated by dissolving Cyanex 272 (as extractant) and a binary surfactant system (Span 80 and Tween 80) in corn oil, then emulsified with sulfuric acid (H 2 SO 4 ) as the internal stripping agent. Partial saponification of Cyanex 272 enhanced extraction efficiency, while sodium acetate served as a buffering agent to improve selectivity. Equilibrium studies validated the extraction mechanism. An optimized surfactant blend of 4% v/v (80% Span 80, 20% Tween 80) provided superior emulsion stability. Key operational parameters were optimized, including extractant concentration (25% v/v, 30% saponified), sodium acetate concentration (1.5 M), feed pH (5), treatment ratio (5:1), stirring speed (200 rpm), time (20 min), phase ratio (2:3), and stripping agent (1 M H 2 SO 4 ). Under these conditions, cobalt extraction reached 95.0%, with minimal nickel co-extraction (3.8%), yielding a high separation factor. The membrane phase was successfully recycled twice with minimal loss in performance, demonstrating the feasibility of this sustainable approach for selective metal separation. • Selective extraction of Co(II) over Ni(II) achieved with a GELM system. • Corn oil identified as a sustainable green solvent in shake-out tests. • Surfactant mix and saponified Cyanex 272 improved stability and extraction efficiency. • Sodium acetate buffer maintained pH and enhanced metal selectivity. • 95% Co(II) and 3.8% Ni(II) extraction achieved; membrane reused for 2 cycles.
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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 | 0.001 |
| 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 it