Empirical Modeling of Butyl Acrylate/Vinyl Acetate/Acrylic Acid Emulsion‐Based Pressure‐Sensitive Adhesives
Bibliographic record
Abstract
Abstract Summary: Butyl acrylate/vinyl acetate/acrylic acid (BA/VAc/AA) emulsion latexes were produced in a semi‐batch mode. The objective was to generate polymers with properties favoring their application as pressure‐sensitive adhesives. The influence of the individual monomer concentrations on final properties such as glass transition temperature ( T g ), peel strength, shear strength and tack was investigated. To obtain the maximum amount of information in a reasonable number of runs, a constrained three‐component mixture design was used to define the experimental conditions. Latexes were coated onto a polyethylene terephthalate carrier and dried. Different empirical models (e.g. linear, quadratic and cubic mixture models) governing the individual properties (i.e. T g , peel adhesion, shear resistance and tack) were developed and evaluated. In the given experimental region, no single model was found to fit all of the responses (i.e. the final properties). However, in all models the most significant factor affecting the final properties was the AA concentration, followed by the VAc concentration. Shear strength contour lines over the investigated region. image Shear strength contour lines over the investigated region.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Empirical modelling of polymer adhesive formulations; materials engineering.
This study models properties of pressure-sensitive adhesives, not research.
Polymer chemistry modelling of pressure-sensitive adhesives.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".