Colloidal and Sedimentation Behavior of Kaolinite Suspension in Presence of Non-Ionic Polyacrylamide (PAM)
Bibliographic record
Abstract
Colloidal behavior of kaolinite particles in water was investigated in this manuscript, without and with the addition of a polymer flocculant (non-anionic polyacrylamide (PAM)), using diverse imaging techniques in addition to LUMisizer. The addition of PAM was found to be causing the formation of bridges among particles thus increasing their settling rates to the bottom of the container. To assess the size of flocs and the potential morphology of PAM around particles and their clusters, the state of flocs formation and polymer distribution was analyzed through various microscopical techniques, namely scanning electron microscopy (SEM) and transmission electron microscopy (TEM). SEM and TEM results revealed that, in the absence of PAM, the floc structure of the sediment was loose and irregularly distributed, while the presence of PAM made the sediment structures greatly denser. Later, using LUMisizer, dynamic light scattering (DLS) and the zeta potential of kaolinite, sedimentation, and colloidal behavior of suspension came under scrutiny. Using LUMisizer, the maximum packing and settling rates of the particles were experimentally obtained as roughly 44 vol%; settling rates were estimated in 63-352 µm/s when centrifugal force varied and, using maximum packing values, compressive yield was estimated to vary between 48-94 kPa. The results of this study are instructive in choosing appropriate polymers and operating conditions to settle clay minerals in tailing ponds. Additionally, the maximum packing of kaolinite particles was simulated with spherical particles with varied polydispersity to connect DLS data to the maximum packing values obtained using LUMisizer; the little discrepancy between simulation and experimental values was found to be encouraging.
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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".