Accurate Measurement of PVT Data for PP/Gas and TPO/Gas Mixtures
Bibliographic record
Abstract
Foaming of a thermoplastic polyolefin (TPO) is gaining interests because of its superior mechanical properties of foamed automotive parts, such as lightweight and high performance to weight ratio, etc. In this context, understanding of the thermophysical properties of PP/gas and TPO/gas mixtures is critically important. This paper will present the newly developed experimental technique to accurately measure the swelling of PP and TPO due to gas dissolution at elevated temperatures and pressures. Our technique measures the geometry of the pendent drop accurately from the captured images to obtain the volume swelling data. It determines the boundary location of the polymer/gas sample accurately by magnifying the sample drop locally along its edge before capturing the image. The automated high-precision XY stage is chosen as the platform to control the motion of the CCD camera. Those images are reconstructed into one big image in the virtual domain based on the original pixel coordinates from individual images. By applying the curve fitting and integration technique, we finally determine the volume swelling of the PP/gas and TPO/gas mixtures.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".