On the Effective Permeability of Mixed Matrix Membranes
Bibliographic record
Abstract
Mixed matrix membranes (MMMs) are attracting significant interest for pervaporation and gas separation applications. To better comprehend the impact of filler particles within polymer matrices, the species permeation mass transport was theoretically studied by numerical simulation using finite differences. The Fick’s second law of diffusion was solved for a three-dimensional MMM to obtain the concentration profile within the membrane and consequently the steady-state permeation flux of the species. The effective permeability of MMMs was then calculated using the steady-state permeation flux of the permeants. The effects of various structural parameters such as the filler volume fraction, particle size, shape and orientation, the ratio of permeability coefficients in the dispersed and continuous phases (Pd/Pc), membrane thickness and particle sorption isotherms were investigated. Results revealed that the effective permeability of MMMs strongly depends on the permeability ratio of the dispersed phase to the continuous phase and the volume fraction of the filler material. Moreover, the shape and size of the particles had no influence on the effective permeability of MMMs for filler volume fractions that are less than 0.4. For numerical simulations performed with different particle sorption isotherms, results showed that the effective permeability of the membrane depends on the type and parameters of the isotherm as well as the feed concentration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".