Mechanical Force Sensitive Acrylic Latex Coating
Bibliographic record
Abstract
We prepared force sensitive acrylic latex coatings by covalently incorporating spiropyran mechanophore. The acrylic latexes were obtained through emulsion copolymerization of butyl acrylate (BA), methyl methacrylate (MMA) with vinyltriethoxysilane (VTES) as interparticle cross-linker, and (1′-(2-(methacryloyloxy)ethyl)-3′,3′-dimethylspiro[chromene-2,2′-indolin]-6-yl)methyl methacrylate) (SP) as intraparticle cross-linker. The latexes of P(BA- co -MMA- co -SP- co -VTES) were subsequently cast onto Teflon-coated surface to form latex coatings. The condensation of hydrolyzed VTES provided interparticle cross-linking and improved mechanical properties of the formed thin films. Intraparticle cross-linker SP endowed the coatings with mechanoreponsiveness. The mechanoactivation of SP-containing latex films was demonstrated. Increasing the content of intra-cross-linker SP resulted in higher stress sensitivity and lower critical stress required for mechanoactivation. Increasing the content of interparticle cross-linker VTES resulted in higher critical stress for SP mechanoactivation but had little effect on the stress sensitivity. T g and operation temperature also showed significant effect on mechanoactivation. Slower strain rate allowed for higher SP-to-MC conversion. This work represents the first example of mechanochromic acrylic latexes and provides insight into the design of force sensitive and self-reporting polymer coatings.
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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) | 0.001 | 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 | 0.001 |
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".