The design and performance of a new miniature mixer for specialty polymer blends and nanocomposites
Bibliographic record
Abstract
Abstract A novel miniature mixer called the “Alberta Polymer Asymmetric Minimixer” (APAM) was designed, built and tested. In this study, polymer blends and nanofiber composites were compounded using a total of approximately 2 grams per sample. This mixer has a unique, asymmetric design consisting of a varying clearance between the rotor blade tips and the cup wall, enabling the material to be squeezed, stretched and kneaded in high shear and converging zones. Unlike the few other miniature mixing devices that are commercially available, the APAM has a combination of good mixing capability and complex flow modes required for dispersive flow, and requires minimal sample mass. In this work, the final morphology of non‐reactive blends created in the APAM was similar to that obtained in an internal mixer and in a twin‐screw extruder, and was much finer than that obtained in a MiniMAX™ mixer. The final morphology in reactive blends was comparable for all the mixers. The dispersion of nanofibers is uniform for the nanocomposites blended in the APAM and comparable to the dispersion obtained in the internal batch mixer, whereas for the MiniMAX™, the nanofibers remained in bundles and were not wetted by the matrix polymer. Polym. Eng. Sci. 44:868–879, 2004. © 2004 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".