Temperature- and pH-Responsive Benzoboroxole-Based Polymers for Flocculation and Enhanced Dewatering of Fine Particle Suspensions
Bibliographic record
Abstract
Random copolymers based on N-isopropylacrylamide (NIPAAm) containing 2-aminoethyl methacrylamide hydrochloride (AEMA) and 5-methacrylamido-1,2-benzoboroxole (MAAmBo) were synthesized and subsequently evaluated for their performance in solid-liquid separation at various pH and temperatures. The strong interactions between benzoboroxole residues and kaolin hydroxyl groups were evaluated for the first time in the flocculation of fine particle suspensions. The lower critical solution temperatures (LCSTs) of PAMN decreases because of the hydrophobic nature of the benzoboroxole moieties, resulting in strong hydrophobic interaction at temperatures higher than the LCSTs. Temperature and pH responsive polymer, P(AEMA51-st-MAAmBo76-st-NIPAM381) (denoted as PAMN) shows the ability to induce fastest settling at a low dosage of 25 ppm and under the condition of pH 9 and 50 °C. The accelerated settling rate is considered to be due to the strong adhesion of benzoboroxole residues to the kaolin hydroxyl groups, the electrical double layer force, and the hydrophobic force. During condensation phase, increasing the pH of sediment to pH 11 could attain the most compact structure. Random copolymers containing benzoboroxole groups act as dispersants (due to pH-responsive character) rather than flocculants at pH 11, providing repulsive force that enables particles to rearrange their position and consolidate well. Through a two-step solid-liquid separation including settling phase and consolidation phase, rapid settling and compact sediment are feasible simultaneously.
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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".