Diene‐based polymer nanoparticles: Preparation and direct catalytic latex hydrogenation
Bibliographic record
Abstract
Abstract The diene‐based polymer nanoparticles represented by poly(butadiene‐ co ‐acrylonitrile) were prepared in the semibatch emulsion polymerization system using Gemini surfactant (GS) trimethylene‐1,3‐bis(dodecyldimethylammonium bromide) as the emulsifier. The nanoparticles within the range of 17–54 nm were achieved with narrow molecular weight and particle size distributions. A spherical morphology was observed for the produced nanoparticles. The effects of GS concentration on the particle size, molecular weight, polymerization conversion and solid content, and composition of copolymer were investigated. The semibatch process using monomeric and conventional surfactant sodium dodecyl sulfate (SDS) was compared. At the second stage of this study, the prepared unsaturated nanoparticles were employed as the substrates for the latex hydrogenation in the presence of Wilkinson's catalyst, that is, RhCl(P(C 6 H 5 ) 3 ) 3 . The effects of the particle size and catalyst concentration on the latex hydrogenation rate were investigated. The particle size is found to have a significant effect on the reaction rate. When the 17‐nm nanoparticles were used as the substrates, a high conversion of 95 mol % was obtained within 18 h using only 0.1 wt % RhCl(P(C 6 H 5 ) 3 ) 3 . The latex hydrogenation process was completely free of organic solvents. The present synthesis and following “green” hydrogenation process can be extended to latices made from semibatch emulsion containing other diene‐based polymers. This study shows great promise for decreasing the demanded quantity of expensive catalyst and eliminating the organic solvent in the hydrogenation process. © 2012 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem, 2012
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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".