Improved Gas Separation of PEBAX-CSWCNTs Mixed Matrix Membranes
Bibliographic record
Abstract
In the present study, mixed matrix membranes (MMMs) were prepared using PEBAX® 3000 as polymer matrix and single-wall carbon nanotubes (SWCNTs) functionalized with carboxyl groups as nanofillers. The effects of the nanofillers on separation of CO2/N2 and CO2/CH4 were investigated. The pristine PEBAX membrane indicated gas selectivity values of 23 and 13 for CO2/N2 and CO2/CH4, respectively. However selectivity of the modified membrane for gas pairs of CO2/N2 and CO2/CH4 improved to the values of 106.4 and 31.3, respectively. In other words, selectivity of modified membranes compared to those of unmodified ones enhanced greatly. The dramatic increase in gas selectivity of the mixed matrix membranes can be attributed to the polar groups of caboxyl-functionalized single-wall carbon nanotubes (CSWCNTs). While CO2 permeability of MMMs increaesd, permeability of nonpolar gases (N2 and CH4) decreased. FTIR spectra depicted that there were inter/intramolecular forces between ether and amide groups of the polymer chains. For PEBAX membrane filled with 10 wt% CSWCNTs, the peaks of C-O-C، N-H, and H-N-C=O functional groups shifted to lower values due to the formation of hydrogen bonds between polar carboxyl groups of CSWCNTs and amide/ether groups of PEBAX copolymer. Relative crystallinity values of the membranes with various CSWCNTs content were calculated using ΔHf data obtained from DSC measurements. Results demonstared that the rise in content of CSWCNTs brought about the decrement in crystallinity values of polyamide segments. The morphology of the membrane containing 10 wt% CSWCNTs was also investigated emplying AFM images, and a suitable compatability and adhere between PEBAX and CSWCNTs was last confirmed.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 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".