Adsorption Kinetics of a Novel Organic–Inorganic Hybrid Polymer on Silica and Alumina Studied by Quartz Crystal Microbalance
Bibliographic record
Abstract
Adsorption kinetics of an organic–inorganic hybrid polymer, Al(OH) 3 –polyacrylamide (Al-PAM), on silica and alumina were studied by a dissipative quartz crystal microbalance (QCM-D). The effects of molecular weight and aluminum content of Al-PAM on its adsorption at varying solution pH were investigated. For comparison, the adsorption of a commercial partially hydrolyzed polyacrylamide, Magnafloc1011 (MF1011), on both silica and alumina was also studied. For Al-PAM of a given molecular weight and aluminum content, the adsorption of Al-PAM on both silica and alumina was found to be highly dependent on polymer concentration and solution pH, showing stronger and more rapid adsorption on silica than on alumina within the pH range studied. Adsorption rate of Al-PAM on silica was observed to increase with increasing molecular weight and aluminum content, while Al-PAM of lower aluminum content adsorbed more favorably on alumina. Commercially available MF1011 showed negligible adsorption on silica but a stronger affinity to alumina than Al-PAM. The results of polymer adsorption determined by QCM-D correlated well with flocculation results of silica and alumina with Al-PAM and MF1011 as flocculants. Atomic force microscopic imaging revealed the morphology of polymers adsorbed on silica and alumina.
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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) | 0.001 | 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".