Characterization of Electrospun PAN Polymer Nanocomposite Membranes for CO2/N2 Separation
Bibliographic record
Abstract
The focus of this study was to enhance the CO2 capture capabilities of polyacrylonitrile (PAN) nanocomposite membranes by reinforcing them with multi-walled carbon nanotubes (MWCNT) and silica (SiO2). These nanocomposite membranes were created using electrospinning technology, which produced nonwoven nanofiber membranes. The nanoparticles were functionalized using Gum Arabic (GA) to improve the distribution and prevent agglomeration. Fourier transform infrared (FTIR) and scanning electron microscopy (SEM) analysis were conducted to examine the functionalization of nanoparticles and their morphological structures. The membranes were experimentally characterized to obtain the CO2 absorption properties and also to evaluate CO2/N2 permeation properties compared to pure PAN membranes. The results showed that higher nanoparticle concentrations increased CO2 permeability while maintaining stable N2 permeability, ensuring favorable CO2/N2 selectivity ratios. The 4 wt.% MWCNTs nanocomposite membrane achieved the best CO2/N2 separation with a CO2 permeability of 289.4 Barrer and a selectivity of 6.3, while the 7 wt.% SiO2 nanocomposite membrane reached a CO2 permeability of 325 Barrer and a selectivity of 7. These findings indicate significant improvements in CO2 permeability and selectivity for the nanocomposite membranes compared to pure PAN membranes. The Maxwell mathematical model has been used to validate the experimental results. The experimental results of the CO2 separation properties of the nanocomposite membranes exceeded the predicted values by the mathematical models. This might be due to the well-dispersed nanoparticles and functional groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 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".