Synergistic role of Cyanex 272 saponification and acetate buffering in selective Co(II)/Ni(II) separation via Green Emulsion Liquid Membrane (GELM)
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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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".