Early bird gets the network: The relative importance of reactivity ratios, Ψ parameter, and crosslinker level on gel formation in <scp>FRP</scp>
Bibliographic record
Abstract
Abstract Network topology is manipulated in free‐radical copolymerization via proper selection of crosslinker type and its respective properties when paired with specific monomers. Our prior work has focused on the impact of the reduced reactivity parameter Ψ applied to a pendent vinyl, characteristic for each monomer/crosslinker pair, yet here we assess the relative importance of the comonomer reactivity ratios to see whether one factor can counterbalance the other. The traditional reactivity ratio determines when the crosslinker molecule is incorporated into the polymer backbone, while the reduced reactivity Ψ parameter relates to the efficiency of the resulting pendent side chain vinyl being utilized to form a crosslink node at some later point during the polymerization. Both factors are then contrasted with simply the overall loading of crosslinker. Either n‐butyl methacrylate (n‐BMA) or styrene (STY) was chosen as a primary backbone monomer to copolymerize with one of three crosslinkers: 1,4‐butanediol dimethacylate (BDDMA), 1,4‐butanediol diacrylate (BDDA), or divinylbenzene (DVB). Both kinetics and gel can be most dramatically boosted when a crosslinker is applied having a reactivity ratio favouring early insertion. This in turn leads to an earlier onset of gel formation, which ultimately results in greater final gel content. This amplification of both kinetics and gel can overcome an otherwise small Ψ due to crosslinker or main monomer choice. This reactivity ratio effect was further confirmed by Monte Carlo simulations. By whatever mechanism (higher Ψ, lower rA, or higher crosslinker level), earlier onset of gelation produces more gel overall and a tighter network topology.
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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) | 0.001 | 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".