CONTROLLED AGGREGATION OF POLYMER LATICES PART 3. INFLUENCE OF TEMPERATURE AND SURFACE CHEMISTRY
Bibliographic record
Abstract
The possibility of obtaining narrowly dispersed particles through the controlled aggregation of polymer latices has been investigated. Concentrated polystyrene, poly(styrene-co-acrylic acid) and poly(styrene-co-butadiene-co-acrylic acid) latices were aggregated through the addition cationic surfactant under stirring. The method under investigation was proven to be effective in obtaining 5-15 urn particles with a narrow size distribution and a strong control of the average size. The aggregation properties of polymer latices were related to their chemical nature (chemical structure of the polymer and surface chemistry); the trends observed were explained through calculations of the fundamental forces involved in this process. Important differences were found between the aggregation of the latices that contain acrylic acid as a comonomer and those without acrylic acid. Narrow size distributions, with geometric standard deviations between 1.2–1.35 were obtained only for the latices containing acrylic acid. It was shown that on the surface of these particles there is a hairy layer formed by the polyacrylic acid chains. The thickness of this layer is dependent on the chemical environment and temperature. Its presence was demonstrated by capillary viscometry and electrophoretic mobility measurements.
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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".