Preparation and properties of polyester nanocomposites: Effects of mixing
Bibliographic record
Abstract
Abstract A multistep mixing technique to manufacture highly intercalated/exfoliated nanocomposites of layered silicates in thermosetting resins was developed and successfully tested up to 9% nanoparticle content. We investigated the influences of mixing conditions on linear and nonlinear viscoelastic properties of processed clays and polyester suspensions. The effects of shear and mechanical mixing on the state of dispersion and microstructure of the cured nanocomposites were examined by the XRD and TEM techniques. Transient viscosities and linear viscoelastic data for the nanocomposites demonstrated significant enhancement by increasing speed and time of mechanical mixing. The solid state characterization techniques confirm mixed to highly exfoliated and delaminated microstructures were attained by the proposed method. Our observations indicate that the multistage mechanical mixing following a stationary step can be used to produce thermosetting and thermoplastic nanoparticles with the improved properties. POLYM. COMPOS., 2012. © 2012 Society of Plastics Engineers
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".