Flow‐induced particle orientation and rheological properties of suspensions of organoclays in thermoplastic resins
Bibliographic record
Abstract
Abstract The influence of nano‐scale particles on the viscoelastic properties of polymer suspensions is investigated. We have developed a simulation technique for the particle orientation and polymer conformation tensors to study various features of the suspensions. The nano‐particles are modeled as thin rigid oblate spheroid particles and the polymers as FENE‐P type viscoelastic and Newtonian fluid. Both interparticle and polymer‐particle interactions have been taken into account in our numerical computations. The nonlinear viscoelastic properties of nanocomposites of layered silicate particles in non‐Newtonian fluids are examined at the start‐up of shear flow and are interpreted using the model to examine the effects of model parameters as well as flow conditions on particle orientation, viscosity, and first normal stress difference of the suspensions. We have studied the microstructure of polymer‐clay nanocomposites using X‐ray diffraction (XRD) scattering and transmission electron microscopy (TEM). The rheology of these nanocomposites in step‐shear is shown to be fairly well predicted by the model. © 2010 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 48: 2003–2011, 2010
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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".