Diffusion Analysis of Flue Gases Through Carbon Nanotube- and Graphene-Reinforced PEBAX Nanocomposite Membranes: A Molecular Dynamic Study
Bibliographic record
Abstract
Dependency on fossil fuels for global energy demand has led to an increase in the concentration of CO2 in the atmosphere, thereby contributing to environmental challenges such as climate change, rise in atmospheric temperature, etc. Since the major contributions of CO2 emissions are from industries, capturing CO2 from post-combustion flue gas has become the focus of many research communities. As such, membrane-based carbon capture and storage (CCS) is an important pathway for controlling CO2 emissions. However, performance validation for membrane separation is required to find the best composite material with a high diffusion rate. Hence, the objectives of this research included determining the performance of the nanocomposite membranes comprising polyether-block-amide (PEBAX) as a matrix and carbon nanotube (CNT) and armchair graphene as reinforcements as well as obtaining the flue gas diffusion rate using molecular dynamic (MD) analysis. Two different composition ratios of the flue gas with an equal ratio (1:1) and an actual post-combustion ratio were developed. The molecular dynamic simulation results obtained from LAMMPS and OVITO determined that graphene-based nanocomposites were better suited for the diffusion of the CO2/N2 and CO2/N2/O2 flue gas compositions, and CNT-reinforced nanocomposite membranes performed better for the CO2/O2 flue gas blend.
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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".