Modeling pore wetting in direct contact membrane distillation—effect of interfacial capillary pressure
Bibliographic record
Abstract
In this study, we aimed to develop a model for computing direct contact membrane distillation (DCMD) performance, taking into account capillary pressure effects at the liquid–gas interface within membrane pores. We developed a simulation model to investigate how factors such as pore radius, feed/permeate temperature, pressure, and contact angle influenced the distance of liquid intrusion into the pore, the weight flow rate in a single pore, and the temperature at the liquid–gas interface. The model predicted that the permeation rate would decrease with an increase in the feed pressure when the permeate pressure was kept constant and also when the pressure difference between the feed and permeate was kept constant. It also predicted that the permeation rate would increase with an increase in the permeate pressure when the feed pressure was kept constant. The model also indicated that partial pore wetting would be enhanced with an increase in feed pressure when the pore size was as large as 1 μm but would diminish when the pore size was as small as 0.1 μm. According to the model, partial pore wetting diminished with a decrease in the permeate pressure. The model’s predictions were in line with the trends observed in the experimental DCMD flux data by many authors, particularly those regarding the effects of feed and permeate temperature and the effect of contact angle. The model’s predictions were compared with the experimental data recorded in the literature, validating the model’s accuracy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".