The extraction of nickel by emulsion liquid membranes using Cyanex 301 as extractant
Bibliographic record
Abstract
The removal of nickel ions from waste streams discharged from mining and metal plating industries has become a popular research topic over the past few decades. In this work, the emulsion liquid membrane (ELM) technique was used to remove nickel ions from synthetic aqueous solutions using bis(2,4,4‐trimethylpentyl)dithiophosphinic acid (Cyanex 301) as the extractant. Sulphuric acid was selected as the internal stripping agent. Central composite design methodology was used to obtain the optimum conditions, with the factors selected in the design being extractant concentration, stripping agent concentration, NiSO4 solution pH, and NiSO4 solution/emulsion volume ratio. It was found that the extractant concentration, stripping agent concentration, and NiSO4 solution/emulsion volume ratio had a significant effect on nickel removal. Optimum operating conditions achieved a maximum nickel removal of more than 99 %. Validation tests confirmed the good agreement between the predicted and experimental data. The emulsion was successfully broken afterwards and the oil phase was re‐tested. The effects of kinetics, loading capacity, and pH variation tests between the emulsion phase and organic phase were investigated. Zeta potential measurements suggest a final pH of around 2.0 is desirable for the post‐reaction treatment of the emulsion droplets.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".