Process variables and their effects on grafting reactions of styrene and methyl methacrylate onto natural rubber
Bibliographic record
Abstract
Abstract The graft copolymerization of styrene and methyl methacrylate onto natural rubber latex was studied under various reaction conditions using a cumene hydroperoxide redox initiator. The monomer conversion, graft copolymer compositions, and grafting efficiency were determined. The synthesized graft copolymers were purified and then characterized by proton nuclear magnetic resonance (1H‐NMR) analysis and differential scanning calorimetry (DSC). A 2 fractional factorial experimental design was applied to study the main effects on the grafting. The variables investigated in this work were the amount of the initiator and emulsifier, the presence or absence of a chain‐transfer agent, the styrene‐to‐methyl methacrylate ratio, the monomer‐to‐rubber ratio, and the reaction temperature. The measured response for the experimental design was the grafting efficiency. The analysis of the results from the design showed the sequence of the main effects on the observed response of the grafting of styrene and methyl methacrylate onto natural rubber, in ascending order. The amount of the chain‐transfer agent and the reaction temperature in the range of the test had significant effects and one marginally significant effect was the monomer‐to‐rubber ratio. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 89: 63–74, 2003
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".