Application of Bioreagents to Mitigate the Negative Effects of Anisotropic Clay Particles in Mineral Processing
Bibliographic record
Abstract
The depletion of high-grade ore resources and ongoing demand for mineral products has led to an increase in the exploitation of low-grade complex ores, which often contain colloidal clay particles that are detrimental to flotation and dewatering operations. Non-renewable chemical reagents are commonly used as dispersants and flocculants for clay particles in wastewater treatment, papermaking, and mineral processing; however, environmentally benign reagents are highly desired due to the non-biodegradability and negative impacts of synthetic reagents on aquatic life. As part of an effort to find environmentally benign reagents, I evaluated the performance of six protein- and polysaccharide-based biopolymers for their potential as dispersants and/or flocculants for swelling and non-swelling clay particles (kaolinite, serpentine, talc, illite, and vermiculite) at various pH via a capillary suction timer, filtration, froth flotation, settling, and turbidity tests. Dewatering results were compared under the same conditions using polyacrylamide as a traditional organic polymer. Zeta potential, adsorption isotherm by total organic carbon, and quartz crystal microbalance with dissipation tests were also conducted to elucidate interactions between biopolymers and clay mineral surfaces. Cationic proteins (lysozyme and protamine) significantly improved the flocculation of clays, whereas polysaccharide-based biopolymers (pectin, alginic acid and lignin) were effective dispersants of clays such as kaolinite, talc, illite, and vermiculite at pH 7 and 10. The use of anionic pectin as a dispersant significantly improved the grade and recovery of Ni and Cu in flotation at pH 10. Pectin successfully flocculated talc and serpentine in dewatering at pH 7, showing its potential as a switchable biopolymer. Similarly, chitosan was an effective switchable biopolymer for kaolinite and illite. Thus, biopolymers could switch from being an effective dispersant at pH 10 to an efficient flocculant at pH 7. The main mechanisms of biopolymer adsorption on clay surfaces are electrostatic attraction and/or hydrogen bonding, while for dispersion, they could be electrostatic repulsion or steric stabilization. This study demonstrated that the anisotropic nature of clay mineral particles should not be overlooked when analyzing adsorption data. Surface properties of clay minerals and polymer/biopolymers characteristics such as chain length, charge, solubility, and functional groups influence interactions between clay particles and biopolymers.
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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".