Screening of parameters and optimization of nickel extraction by green emulsion liquid membrane using statistical experimental design
Bibliographic record
Abstract
Abstract This study focuses on the extraction of nickel ions from an aqueous solution using a green emulsion liquid membrane (GELM). Its primary objective is to choose between corn oil and sunflower oil as a solvent in GELM and compare their performance with a kerosene‐based emulsion liquid membrane (ELM). The membrane phase was made by dissolving the carrier (D2EHPA) and the surfactant (tween 80), in the solvents. Subsequently, the membrane was emulsified with the stripping agent (sulphuric acid) to produce the GELM. A Plackett–Burman design was employed to determine the key parameters influencing nickel extraction. Among the considered parameters, treatment ratio, surfactant concentration, carrier concentration, and stripping agent concentrations were identified as the significant factors affecting nickel extraction. Parameters such as stirring speed and time, external phase pH, and phase ratio were found to be non‐significant and were kept constant. The central composite design method was employed to determine the optimum value of the key parameters. Under the optimal conditions, 98.1% of the nickel ions were successfully extracted. The feasibility of recycling the membrane phase was examined, and the performance of GELMs prepared using both fresh and recovered membrane phases was analyzed. The experimental results showed that the extraction efficiency decreased by 1.02% and 7.99% after two membrane recycling cycles, which was still within the acceptable range.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".