Stimuli-Responsive Hybrid Polymer for Enhanced Solid–Liquid Separation of Industrial Effluents
Bibliographic record
Abstract
In the current study, a novel stimuli-responsive hybrid polymer with aluminum hydroxide colloids incorporated into a cationic copolymer of N-isopropylacrylamide and N-[3-(dimethylamino)propyl]methacrylamide was synthesized to enhance the settling and filtration performance of fine clay suspensions. The conformation of the synthesized hybrid copolymer was shown to respond to changes in both temperature and pH. Compared with a cationic copolymer of similar structure without aluminum hydroxide colloids, settling and filtration rates were significantly enhanced using the hybrid copolymer, which is attributed to the synergy between the inorganic aluminum hydroxide cores and organic copolymer. While the ideal treatment protocol for the hybrid polymer involved the addition of the polymer at room temperature, followed by heating to 45 °C for enhanced settling and dewatering, the synergistic effect between colloidal cores and polymer also allowed the hybrid polymer to perform well when added at temperatures above the LCST, demonstrating the robustness of the hybrid polymer to the process environment. The ideal treatment protocol resulted in an optimal adsorption of polymer on clays before inducing a coil-globule transition to form large and dense flocs, resulting in a more porous filter cake.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".