SYNTHESIS OF POLYSTYRENE/SiO2 AND POLYISOPRENE/SiO2 NANOPARTICLES VIA RAFT EMULSION POLYMERIZATION
Bibliographic record
Abstract
Polystyrene-silica (PS-co-RAFT-SiO2) and polyisoprene-silica (PIP-co-RAFT-SiO2) nanoparticles were synthesized via reversible addition-fragmentation chain-transfer (RAFT) emulsifier-free emulsion polymerization. The core-shell morphology of polymer-silica nanoparticles have attributed to reduce the agglomeration of silica. The effects of macro-RAFT agent to initiator ratio on monomer conversion, particle size, particle size distribution, grafting efficiency and silica encapsulation efficiency were investigated. The particle size of PS-co-RAFT-SiO2 and PIP-co-RAFT-SiO2 nanoparticles decreased with increasing macro-RAFT agent to initiator ratio ([R]:[I]) and showed a narrow size distribution for all polymerizations. For PIP-co-RAFT-SiO2 preparation, the type of water-soluble initiator were also studied. The particle size of emulsion prepared using ACP initiator was smaller than that using V50 initiator due to the different structure of the initiators. Furthermore, Poly(methyl methacrylate)-silica (PMMA-SiO2) and poly(styrene-co-methyl methacrylate) )-silica (poly(ST-co-MMA)-SiO2) nanoparticles were prepared via differential microemulsion polymerization. The effects of silica loading and surfactant concentration on monomer conversion, particle size, particle size distribution and silica encapsulation efficiency were investigated. PMMA-SiO2 nanoparticles with a size range of 30–50 nm and high monomer conversion of 99.9% were obtained at a low surfactant concentration of 5.34 wt% based on monomer. For poly(ST-co-MMA)-SiO2 nanoparticles, a high monomer conversion and small particle size (20–40 nm) were obtained under optimum reaction conditions with a low surfactant concentration (3 wt% based on monomer). The nanocomposites have been used as nano-filler in natural rubber latex. Accordingly, NR/polymer-SiO2 blends had improved thermal and mechanical properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 teacher head, 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".