Effects of halloysite nanotubes on the morphology and CO<sub>2</sub>/CH<sub>4</sub> separation performance of Pebax/polyetherimide thin‐film composite membranes
Bibliographic record
Abstract
ABSTRACT Enhancing the performance of gas separation membranes is one of the major concerns of membrane researchers. Thus, in this study, poly(ether‐block‐amide) (Pebax)/polyetherimide (PEI) thin‐film composite membranes were prepared and their CO2/CH4 gas separation performance was investigated by means of pure and mixed gases permeation tests. To improve the properties of these membranes, halloysite nanotubes (HNT) were added to Pebax layer at different loadings of 0.5, 1, 2, and 5 wt % to form Pebax‐HNT/PEI membranes. Scanning electron microscopy, gas sorption, X‐ray diffraction, Fourier‐transform infrared, and differential scanning calorimetry tests were also performed to investigate the impact of HNT on structure and properties of prepared membranes. Results showed that both CO2/CH4 selectivity and CO2 permeance increased by adding HNT to Pebax layer up to 2 wt %. By increasing HNT loading to 5 wt %, the CO2/CH4 selectivity decreased from 32 to 18, while CO2 permeance increased from 3.25 to 4.2 GPU. Pebax/PEI and Pebax‐HNT/PEI membranes containing 2 wt % of HNT were tested using CO2/CH4 gas mixtures at different feed CO2 concentrations and feed pressure of 4 bar. The results showed that with increasing CO2 concentration from 20 to 80 vol %, CO2/CH4 selectivity of Pebax/PEI composite membranes increased by 19%, while, in Pebax‐HNT/PEI membrane, CO2/CH4 selectivity decreased by 40%. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020, 137, 48860.
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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".