Investigating The Synergistic Effect Of Non-Ionic Surfactants And Ethylene Thiourea On Chalcopyrite Bioleaching
Bibliographic record
Abstract
The slow kinetics of chalcopyrite (CuFeS₂) leaching limits the efficiency of hydrometallurgical copper extraction.This study investigates the impact of non-ionic surfactants (Tween 20, Tween 40, and Span 60) and ethylene thiourea (ETu) on copper extraction from a chalcopyrite concentrate.Ferric sulfate leaching experiments were conducted at ambient temperature, with copper extraction monitored via ICP-OES.Results demonstrate that the addition of Tween 20 at 30 ppm, particularly when combined with ETu, enhances copper recovery, achieving approximately 23% extraction after 864 hours-however, without addition of bacteria this increase is not significant compared to control samples.Surfactant performance correlated strongly with the hydrophile-lipophile balance (HLB); surfactants with moderate-to-high HLB values (Tween 20 and Tween 40) improved extraction efficiency, while low-HLB surfactants (Span 60) showed negligible effects.However, surfactant efficacy diminished above the critical micelle concentration, highlighting the need for careful optimization.This research provides insights into additive-assisted ferric sulfate leaching strategies and emphasizes future exploration of bioleaching approaches to further enhance copper extraction from chalcopyrite ores.
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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".