Photoinduced Thermal Polymerization Reactions
Bibliographic record
Abstract
The combination of thermally induced and photoinduced free radical polymerization of (meth)acrylic monomers has only been scarcely presented in the literature. In this study, a two-component system with a near-infrared (NIR) dye combined with a thermal initiator is presented. The dye acts as a very efficient heat generator (heater) upon irradiation with NIR light. Several thermal initiators are presented such as an alkoxyamine (e.g., BlocBuilder-MA), azo derivatives, and (hydro)peroxides. The heat delivered by the dye dissociates the thermal initiator, which initiates the free radical polymerization of (meth)acrylates. Several types of heat generators are presented such as borate-based dyes and a silicon phthalocyanine derivative. For the first time, the effects of the NIR heater concentration, light intensity, and monomer structure on the heat released are studied using thermal imaging studies. NIR light curing is challenging but offers significant advantages: it is safer than shorter wavelength, and it allows a deeper penetration of the light and therefore a better curing of filled samples for a unique access to composites. Systems using a cyanine borate as a dye give high conversion rate of C═C for methacrylate monomer under air. Two wavelengths of irradiation are studied: 785 and 850 nm. The presence of additives (phosphines or iodonium salts) can also improve the polymerization profiles.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".