Factorial design of nanosized polyisoprene synthesis via differential microemulsion polymerization
Bibliographic record
Abstract
Abstract The synthesis of nanosized polyisoprene latex was carried out by differential microemulsion polymerization using 2, 2′‐Azoisobutyronitrile (AIBN) initiator system under various reaction conditions. A $2_V^{5 - 1} $ fractional factorial experimental design was applied to study the effects of reaction variables: amount of initiator and surfactant, monomer‐to‐water ratio, reaction temperature, and stirring speed on rubber particle size and monomer conversion. The analysis of the results from the design showed the main effects on the observed response and the amount of initiator, reaction temperature and stirring speed in the range of the test had significant effects on polyisoprene particle size. The significant effects on monomer conversion were reaction temperature, stirring speed, and interaction between reaction temperature and stirring speed in the range of the test. The optimum conditions gave highest monomer conversion of 90% and average particle size of polyisoprene of 27 nm. The nanosized polyisoprene was also characterized by Fourier transform infrared (FTIR) spectroscopy and nuclear magnetic resonance (NMR) spectroscopy. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".