Influence of Particle Size and Content of Agglomerated Polybutadiene on Mechanical and Rheological Properties of Acrylonitrile-Butadiene-Styrene Terpolymer
Bibliographic record
Abstract
The particle size of polybutadiene (PB) particles can be effectively controlled through polymer agglomeration. However, up to now, the impact of the particle size and concentration of agglomerated PB on the mechanical properties and rheological behavior of ABS terpolymer remains unclear. Our research described here studied the effect of the particle size and content of agglomerated PB on the mechanical and rheological properties of ABS. It was found that the impact strength of ABS with a bimodal size distribution of PB (67/452 nm) exceeded that of ABS with a unimodal PB size distribution within the PB content range studied. Furthermore, the impact strength of the ABS containing 319 nm PB above the brittle-toughness transition point was greater than that of other ABS containing unimodal PB at the same PB content. For ABS with a unimodal PB distribution, the PB contents of ABS containing 220, 319, and 415 nm PB at the brittle ductile transition point were 14%, 12%, and 16%, respectively. The tensile strength and melting index of the ABS containing different particle sizes of agglomerated PB decreased linearly with the increase of PB content. Rheological tests showed the viscosity and dynamic viscoelastic properties of the ABS blends. All ABS exhibited shear-thinning behavior, and the addition of PB content resulted in more elastic than viscous behavior in the ABS blends. At the same angular frequency (ω), higher PB content corresponded to greater complex viscosity in the ABS blends. In addition, as the PB content in ABS was increased, it exhibited more solid-like behavior. This study, we suggest, provides a good insight into the effect of agglomerated PB on the ABS/PB blend properties and explores practical applications for the polymer agglomeration of polybutadiene latex (PBL).
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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".