PEO flocculation of kaolinite – Molecular weight effect
Bibliographic record
Abstract
The adsorption of poly(ethylene oxide) (PEO) of different molecular weights (MW) on kaolinite and its effects on kaolinite suspension stability were investigated. Fourier transform infrared spectroscopic and X-ray photoelectron spectroscopic analyses showed that the adsorption mechanisms of PEOs with different molecular weights appeared to be identical, i.e., through the formation of hydrogen bonds between ether‑oxygen groups of PEO and hydroxyl groups on the surface of kaolinite. Zeta potential measurements indicated that the adsorption of PEO resulted in the outward expansion of the diffuse layer on the surface of kaolinite, as evidenced by a decrease in the magnitude of zeta potential. The maximum decrease was observed to correlate to the thickness of the adsorbed layer, which in turn was proportional to the MW of PEO, indicative of the lateral extension of the “loop” and “tail” structures of adsorbed PEO. As the adsorption layer thickness increased, the likelihood of bridging flocculation increased, resulting in the formation of larger flocs with greater strength and lower density in a shorter time, observed with higher MW PEO. This conclusion was confirmed by experimental results obtained from an online monitoring system for flocculation processes composed of focused beam reflectance measurement (FBRM) particle size analyzer and confocal scanning microscopy (CSM).
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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 | 0.001 |
| 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) | 0.001 | 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".