Effect of Raw Multi-Wall Carbon Nanotubes on Morphology and Separation Properties of Polyimide Membranes
Bibliographic record
Abstract
Raw multi wall carbon nanotubes (r-MWCNTs) were embedded as fillers inside the polyimide (PI) matrix and PI/r-MWCNTs mixed matrix membranes were fabricated by the phase inversion method. The TEM images and permeation results using helium as test gas showed that r-MWCNTs were generally closed ended and acted as impermeable nano particles. Gas permeation tests using CO2 and CH4 showed that the addition of r-MWCNTs into the dope solution increased the CO2/CH4 separation factor while decreasing the carbon dioxide and methane permeances. When the r-MWCNTs content was increased from 0% to 6 wt.%, permeance of CO2 in the flat sheet mixed matrix membranes decreased from 9.15 GPU to 5.49 GPU and CO2/CH4 separation factor increased from 19.05 to 45.75. Identical to flat sheet mixed matrix membranes, the addition of 2 wt.% r-MWCNTs into a spinning dope increased the CO2/CH4 separation factor from 46.61 to 72.20. The glass transition temperature of the mixed matrix flat sheet membranes increased with an increase in the r-MWCNTs content. This implies a good segmental-level attachment between the two phases that forms a rigidified polymer region at the polymer/r–MWCNTs interface. FESEM images showed well dispersed r-MWCNTs in the polymer matrix at a loading of 2 wt% r-MWCNTs.
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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 | 0.000 |
| 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) | 0.001 | 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".