Polymer–CO<sub>2</sub> systems exhibiting retrograde behavior and formation of nanofoams
Bibliographic record
Abstract
Abstract The sorption of compressed gases in polymers causing a reduction in the glass transition temperature (Tg) is well established. There is, however, limited information on polymer–gas systems with favorable interactions, producing a unique retrograde behavior. This paper reports on using a combination of established techniques of in situ gravimetric and stepwise heat capacity (Cp) measurements using high‐pressure differential scanning calorimetry (DSC) to demonstrate the occurrence of this behavior in acrylonitrile–butadiene–styrene copolymer (ABS)–CO2 and syndiotactic poly(methyl methacrylate) (sPMMA)–CO2 systems. The solubility and diffusion coefficient of CO2 in the range 0 to 65 °C and pressures up to 5.5 MPa were determined, which resulted in a heat of sorption of − 15.5 and − 15 kJ mol−1, and an activation energy for diffusion of 28.3 and 32.1 kJ mol−1 in the two systems, respectively. The fundamental kinetic data and the changes in Cp of the polymer–gas systems were used to determine the plasticization glass transition temperature profile, its relationship to the amount of gas dissolved in the polymer, and hence the formation of nano‐morphologies. Copyright © 2006 Society of Chemical Industry
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 | 0.000 | 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".