Molecular Weight Dependence of Synthetic Glycopolymers on Flocculation and Dewatering of Fine Particles
Bibliographic record
Abstract
In this study, poly(2-lactobionamidoethyl methacrylamide) of various molecular weights (MWs) was synthesized using conventional free-radical polymerization. The effect of MW and polymer dosage on the settling rate of kaolin particles, turbidity of the supernatant, mud-line position, and solid content was investigated to determine the flocculation performance. The interaction forces, polymer conformation, particle sizes, and MWs were determined using several techniques, including surface forces apparatus, atomic force microscopy (AFM), dynamic light scattering, and gel permeation chromatography. Our results reveal that the initial settling rate of kaolin particles and the clarity of supernatants increase with increasing MW of the glycopolymers. Surface force measurements and AFM imaging of the adsorbed polymer surfaces show strong polymer-particle adhesion and bridging attraction between the glycopolymers and clay surfaces, which increase with increasing MW of the glycopolymer. The strengthened bridging attraction with the polymer MW is attributed to the formation of stronger adhesion (e.g., via hydrogen bonding) between the fine particles and the abundant hydroxyl groups in the glycopolymers of higher MW, thus contributing to enhanced flocculation behaviors. Our results provide new insights into the development of eco-friendly polymer flocculants based on glycopolymers for an efficient solid-liquid separation in tailing treatment and into the fundamental understanding of associated intermolecular interactions and flocculation mechanisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".