Effect of organic surfactant TRITON CG-110 on bioleaching of chalcopyrite
Bibliographic record
Abstract
Chalcopyrite leaching in acidic solutions is often hindered by slow kinetics due to surface passivation, which reduces copper leaching efficiency. This study explored the effects of Triton CG-110, a non-ionic surfactant, on chalcopyrite bioleaching facilitated by mesophilic acidophilic bacteria. Bio-oxidation experiments demonstrated that bacterial metabolism was enhanced with the addition of CG-110; at a concentration of 20 ppm, the oxidation of Fe 2+ to Fe 3+ was accelerated, indicating improved bacterial activity. However, at concentrations exceeding 500 ppm, CG-110 inhibited bacterial metabolism, highlighting the importance of optimizing surfactant dosage to maximize chalcopyrite dissolution. The addition of 20 ppm CG-110 enhanced leaching efficiency by altering the surface wetting properties, which increased the interaction between ferric ions and the chalcopyrite surface, thereby improving the copper leaching rate. This surfactant also reduced the impact of surface passivation by promoting greater contact between ferric ions and the mineral surface, facilitating copper dissolution. Electrochemical analysis revealed that CG-110 improved charge transfer at higher potentials without participating in chemical oxidation, thus enhancing anodic activity. Surface analysis using X-ray photoelectron spectroscopy (XPS) showed a decrease in elemental sulfur accumulation—a key contributor to passivation—on the chalcopyrite surface in the presence of CG-110. Additionally, 16S rRNA sequencing showed that CG-110 influenced microbial community shifts, increasing the relative abundance of bacteria associated with enhanced sulfate formation and reduced elemental sulfur accumulation, favoring bioleaching conditions.
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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".