A novel method to characterize thermal properties of the polymer and gas/supercritical fluid mixture using dielectric measurements
Bibliographic record
Abstract
Gases or supercritical fluids (SCF) are widely used in polymer science and engineering, as their dissolution into polymeric materials will alter their inherent thermal properties; including melting and crystallization temperatures (Tm and Tc). One possible method to determine these temperatures, at elevated pressures, is to use a high-pressure differential scanning calorimeter (HP-DSC). However, the elevated pressures used in HP-DSC may result in signal instabilities, limiting the testing window for these pressures. This study presents a novel testing system using dielectric measurements to determine the effects of dissolved gas/SCF on the Tm and Tc of polymers. We have developed an instrument to determine the dielectric properties of both polymer/gas and polymer/SCF mixtures, at elevated pressures and temperatures. Using the change in the measured dielectric constant or loss, Tm and Tc were determined. The effects of hydrostatic pressure and plasticization due to dissolved carbon dioxide (CO2) and Helium (He) on the Tm and Tc of high density polyethylene (HDPE) are presented and discussed. Both Tm and Tc increase with pressure and decrease due to plasticization, i.e., pressure and plasticization are competing variables. The dissolution of He, having a low solubility into HDPE, reveals that pressure is the dominant effect. In contrast, the dissolution of CO2, having a high solubility into HDPE, shows that plasticization is predominant.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".