Modified Bruggeman models for prediction of CO<sub>2</sub> permeance in polycarbonate/silica nanocomposite membranes
Bibliographic record
Abstract
Abstract The polycarbonate membranes performances are improved by the incorporation of silica nanoparticles into the polymer matrix. Prepared PC/silica nanocomposite membranes with various silica content are considered and the experimental relative permeance of CO2 gas at 200 kPa (2 bar) feed pressure are used as reference in the predictions using the existing theoretical models such as Maxwell‐Wagner‐Sillar, Bruggeman, Lewis‐Nielsen, Pal, and modified Pal models. TEM and BET analysis were used to characterize the silica nanoparticles; FESEM was used to characterize the morphology of the nanocomposite membranes. The TEM image of the silica nanoparticles reveals that the nanoparticles are mostly spherical. BET results reveal that the surface area and pore diameter of the silica nanoparticles are 618.8 m2/g and 0.28 nm, respectively. The existing models have resulted in poor predictions with errors AARE % of 26.52 to 28.02 %. Observation by FESEM image shows that the dispersed particles are surrounded by interfacial voids and rigidified polymer layer. Modified Bruggeman models that consider the interfacial volume show appreciable prediction with AARE 4.59 % being obtained with the pseudo‐two‐phase Bruggeman model. Moreover, when the model considered pseudo‐three‐phase morphology, the AARE % value reduced to 3.92 %. Thus, the contribution due to the interfacial rigidified layer was minimal.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".