Use of a Novel Tetrafunctional Initiator in the Free Radical Homo‐ and Copolymerization of Styrene, Methyl Methacylate and α‐Methyl Styrene
Bibliographic record
Abstract
An investigation into the effect of initiator functionality on the free radical polymerization of various monomer systems including styrene, methyl methacrylate (MMA), styrene–MMA and α‐methyl styrene–MMA with a tetrafunctional peroxide initiator (JWEB50) was completed. The performance of the tetrafunctional initiator was compared to a monofunctional counterpart (TBEC). Kinetic results showed that regardless of the monomer system studied, JWEB50 produced a faster rate of polymerization compared to TBEC at an equivalent concentration. Molecular weights, radii of gyration and intrinsic viscosities were obtained from two size exclusion chromatography setups: one equipped with a multi‐angle laser light scattering detector and the other with low‐angle laser light scattering and viscosity detectors. For the homopolymerization of styrene, JWEB50 produced polymer molecular weights similar to TBEC at the same concentration, while experiments with MMA indicated that JWEB50 produced molecular weights closer to those obtained with TBEC at a concentration four times as great. Runs with a feed mixture of styrene and MMA gave results that were a combination of the observations made for the individual homopolymerizations. The results for the feed mixture of MMA and α‐methyl styrene were similar to the findings with pure styrene. Plots of radius of gyration, intrinsic viscosity and their corresponding branching factors provided evidence of branching for all cases except for the homopolymerization of MMA.
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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 | 0.001 |
| 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".