Morphology formation during the nonisothermal thermally‐induced phase separation (<scp>TIPS</scp>) process
Bibliographic record
Abstract
Abstract In high‐viscosity polymer solutions and blends, temperature variations during the quench process of the thermally‐induced phase separation (TIPS) influence the dynamics and thermodynamics of phase separation. Hence, this study aims to investigate the impact of temperature variations on the morphology formation during the TIPS process. First, the influence of temporal temperature variations on phase separation is investigated by coupling a transient heat conduction model and the Cahn–Hilliard equation, and the results are compared with the isothermal phase separation process. Next, the morphology formation during phase separation is inspected by applying quench from two opposite sides of the sample to the same and different temperatures through coupling the Fourier heat transfer equation and the Cahn–Hilliard equation. The influence of the enthalpy of demixing on the morphology formation and the competition between the heat and mass transfer is also evaluated. It is confirmed that temporal variations of temperature alone have a significant impact on the morphology formation during the TIPS process. In addition, quenching the system to the same and different temperatures both leads to anisotropic morphology formation, which is affected by the quench rate, quench temperature, solution viscosity, and enthalpy of demixing. Upon applying different quench temperatures from opposite sides, two different types of morphologies and droplet sizes were formed as a result of the difference in the cooling rates between the two sides. Employing the enthalpy of demixing during phase separation induced a shallow quench effect on the deep quench side due to the fact that the heat moved toward the lowest temperature in the system, which led to the formation of a distinctive structure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".