Modification of existing permeation models of mixed matrix membranes filled with porous particles for gas separation
Bibliographic record
Abstract
Gas separation methods have received much attention, as the process plays a key role in various industries. Among the gas separation methods, membrane‐based methods, particularly those employing mixed matrix membranes (MMMs), are important. MMMs are formed by modifying the properties of polymeric membranes by fabricating them with inorganic particles. This paper presents the gas transport behaviour in MMMs fabricated with porous particles, as described by two‐phase (ideal) and three‐phase (non‐ideal) models. The effect of particle porosity on gas permeability was incorporated into existing models through the J parameter, which adjusts the particle loading percentage. J ‐modified models were verified against existing models and experimental data for gas permeability were obtained with various MMMs. It was found that the proposed modified models provide a better prediction of the gas transport behaviour in MMMs.
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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 | 0.000 | 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) | 0.000 | 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".