Polypyrrole‐aided surface decoration of graphene oxide nanosheets as fillers for poly(ether‐ <i>b</i> ‐amid) mixed matrix membranes to enhance <scp> CO <sub>2</sub> </scp> capture
Bibliographic record
Abstract
Modified graphene oxide (GO) nanosheets as fillers within poly(ether-b-amid) (PEBA) copolymer as matrix were used for mixed matrix membranes (MMMs) fabrication to improve CO2 capture. The GO nanosheets were modified by polypyrrole (PPy) and zinc cations. The former that have conjugated N-containing groups provided a high degree of affinity toward CO2, and the latter has facilitated the gas molecules transport through the gas channel passages. The different modified GO nanosheets were characterized by HRTEM, FT-IR, FESEM, XRD, and XPS analyses. Also, the FT-IR, XRD, AFM, and SEM methods were used for the structural and morphological characterizations of the prepared MMMs with 0.1 wt% of the nanofiller. Gas permeation tests for CH4, N2, and CO2 were then performed for all prepared membranes. Compared to the neat PEBA membrane, the selectivity of both CO2/CH4 and CO2/N2 for PEBA-GO-PPy membrane increased up to 62% and 51% and for the PEBA-GO-PPy-Zn membrane increased up to 58% and 56%, respectively. Furthermore, the PEBA-GO-PPy-Zn disclosed a 10% increase in permeability of CO2 than the neat membrane. For PEBA-GO-PPy MMM, the permeability of CO2 was 122.4 Barrer, and the selectivity of CO2/CH4 and CO2/N2 was 29.8 and 122.5, respectively. Moreover, the gas separation results for PEBA-GO-PPy-Zn MMM were about 131.8 Barrer for CO2 permeability and 30.7 and 119.2 for CO2/CH4 and CO2/N2 selectivities, respectively. Besides, the CO2/N2 selectivity of PEBA-GO-PPy and PEBA-GO-PPy-Zn overcomes the Robeson's upper bound.
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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".