Numerical Modeling and Simulation of the Nonisothermal Double Quench Phase Separation Process for the Production of Polymeric Membranes Using Polystyrene‐Cyclohexanol Polymer Solution
Bibliographic record
Abstract
Abstract The double quench phase separation is a simplified type of continuous cooling process that is widely seen in industrial processes for polymeric membrane formation. Uncommon quenching conditions can lead to the creation of novel membrane microstructures. This study aims to theoretically investigate the impact of nonisothermality on the morphology formation during the double‐quench thermally‐induced phase separation process. First, quench is employed during different stages of phase separation to observe the possibility of secondary morphology formation. Next, two initial quench temperatures are selected, one shallow and the other deep. The initial solution temperature and the secondary quench temperature are kept constant to inspect the impact of the initial quench temperature on the structure formation. Lastly, the results of the secondary quench are compared with and without employing the enthalpy of demixing. Results verified that the stage of phase separation, the initial and secondary quench temperatures, cooling rate, and the secondary quench composition are the most important parameters in the the nonisothermal double quench phase separation process. The morphology should be well‐developed in order for the secondary structure formation. In addition, it is shown that heat generation during demixing in the primary and secondary quenches significantly influences the secondary morphology formation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".