Melt compounding of different grades of polystyrene with organoclay. Part 1: Compounding and characterization
Bibliographic record
Abstract
Abstract Nanocomposites of polystyrene (PS) with up to 20 wt% organoclay were prepared by melt compounding in a co‐rotating, intermeshing twin‐screw extruder. Three grades of PS with different molecular weights were used. In this paper we discuss preparation and characterization of the mixtures. Residence time and its distribution were measured by ultrasonics (US). They were found to be independent of the PS grade, but the US attenuation and sound velocity varied with the organoclay loading. According to the XRD diffraction data, the organoclay was dispersed into two types of platelet stacks evidenced by the diffraction peaks at about 2θ = 2.1 and 5.5°, i.e., with the interlayer spacings of d001 = 4.20 and 1.6 nm, respectively. Since neat organoclay has d001 = 1.93 nm, the first peak indicates intercalation by PS and the other a collapse of the interlamellar gallery during compounding. The XRD spectra depended on the organoclay content, but not on PS grade. According to TEM analysis, the degree of intercalation of organoclay in the polymer matrix is highest for the low‐molecular‐weight polystyrene. TEM results also confirm the collapse of interlamellar spacing in parts of the samples. FT‐IR spectroscopy showed that there was some thermal degradation of the onium compound present in the nanoclay. Polym. Eng. Sci. 44:1046–1060, 2004. © 2004 Society of Plastics Engineers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".