Influence of Particle Nucleation in Pressure Sensitive Adhesive Properties: Miniemulsion <i>versus</i> Emulsion Polymerization
Bibliographic record
Abstract
Abstract Miniemulsion polymerization is a promising approach to produce and tailor pressure sensitive adhesives (PSAs). In this paper, a systematic comparison of the adhesive properties of latexes produced by miniemulsion and conventional emulsion polymerization is presented. Specifically, the influence of the total surfactant concentration, chain transfer agent concentration and chemical composition on the final adhesive properties of the polymer 2‐ethyl hexyl acrylate/methyl methacrylate/acrylic acid was discerned using a 23 factorial design for each polymerization method. In addition to the adhesive properties (i.e., loop tack, peel strength and shear strength), molecular weight distribution, particle size distribution (PSD) and glass transition temperature were analyzed. The results show that under the conditions used in this work, it is possible to produce PSAs using miniemulsion polymerization, a process wherein monomer droplet nucleation is the dominant particle nucleation mechanism. The use of a miniemulsion polymerization process, as opposed to the conventional emulsion technique, produced several differences such as larger particles sizes and narrower molecular weight distributions. Focusing on the PSA films that exhibited adhesive rather than cohesive failure, the PSA films generated via miniemulsion polymerization displayed higher values of loop tack and peel strength compared to those produced via conventional emulsion polymerization. Shear strength results were strongly dependent on the amount of gel content and sol molecular weight for both cases.
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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 | 0.001 |
| 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".