Poly(styrene)‐ and Poly(styrene‐ <i>co</i> ‐methyl methacrylate)‐ <i>graft</i> ‐hydrogenated natural rubber latex: Aspect on synthesis, properties, and compatibility
Bibliographic record
Abstract
The undesirable properties of natural rubber (NR) can be improved via hydrogenation and graft copolymerization. Hydrogenated NR (HNR) latex was prepared via diimide reduction and then grafted with styrene (ST) or ST/methyl methacrylate (MMA) to form poly(ST)‐ graft ‐HNR (poly(ST)‐ g ‐HNR, GHNRS) or poly(ST‐ co ‐MMA)‐ g ‐HNR (GHNRSM), respectively. For the grafting of ST monomer onto HNR particles, the %monomer conversion and %grafting efficiency (%GE) were monitored as functions of %hydrogenation, monomer and initiator concentrations, temperature, and time. Under the optimum condition (HNR with 54.3% hydrogenation; 100 phr of ST, 1 phr of initiator at 50°C for 8 h), maximum %conversion and %GE of 44.6% and 36.9%, respectively, were achieved. Thermogravimetric analysis revealed that the HNR grafted with ST or ST/MMA had higher decomposition temperature than an ungrafted one. When these graft products were blended at 10% (w/w) with acrylonitrile‐butadiene‐styrene (ABS) resin, the GHNRS/ABS and GHNRSM/ABS composites exhibited the higher flexural strength and heat aging tolerance compared to the ungrafted HNR/ABS composite. Scanning electron microscopy (SEM) also showed the higher degree of homogeneity at the fractural surface, supporting the higher compatibility between the ABS and the GHNRS or GHNRSM phases in the blends. J. VINYL ADDIT. TECHNOL., 22:100–109, 2016. © 2014 Society of Plastics Engineers
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".