Probing Adsorption of Polyacrylamide-Based Polymers on Anisotropic Basal Planes of Kaolinite Using Quartz Crystal Microbalance
Bibliographic record
Abstract
Quartz crystal microbalance with dissipation (QCM-D) was applied to investigate the adsorption characteristics of polyacrylamide-based polymers (PAMs) on anisotropic basal planes of kaolinite. Kaolinite basal planes were differentiated by depositing kaolinite nanoparticles (KNPs) on silica and alumina sensors in solutions of controlled pH values. Adsorption of an in-house synthesized organic-inorganic Al(OH)3-PAM (Al-PAM) as an example of cationic hybrid PAM and a commercially available partially hydrolyzed polyacrylamide (MF1011) as an example of anionic PAM was studied. Cationic Al-PAM was found to adsorb irreversibly and preferentially on tetrahedral silica basal planes of kaolinite. In contrast, anionic MF1011 adsorbed strongly on aluminum-hydroxy basal planes, while its adsorption on tetrahedral silica basal planes was weak and reversible. Adsorption study revealed that both electrostatic attraction and hydrogen-bonding mechanisms contribute to adsorption of PAMs on kaolinite. The adsorbed Al-PAM layer was able to release trapped water overtime and became more compact, while MF1011 film became more dissipative as backbones stretched out from kaolinite surface with minimal overlapping. Experimental results obtained from this study provide clear insights into the phenomenon that governs flocculation-based solid-liquid separation processes using multicomponent flocculants of anionic and cationic nature.
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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 | 0.001 |
| 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".