Nanocomposite membranes incorporated with graphene oxide for CO2/CH4 separation
Bibliographic record
Abstract
Biogas is an alternative energy produced by anaerobic digestion of organic matter. Generally, raw biogas consists of methane (CH4), carbon dioxide (CO2), few amount of hydrogen sulfide (H2S) and traces of water vapor. Nowadays, upgrading raw biogas is required in order to achieve higher calorific value and meet fuel standard by removal of CO2. In this study, composite membranes of PEG 400/Pebax 1657 blended polymer with graphene oxide (GO) and amine functionalized graphene oxide (Fn-GO) were successfully developed for CO2/CH4 gas separation. The effects of graphene oxide, amine functionalized graphene oxide and PEG 400 additions on CO2/CH4 separation performance were studied in this research. The membrane containing 0.25 wt.% GO in Pebax 1657 showed a better separation factor compared to pristine Pebax 1657 composite membrane by increasing from 12.18 to 42.33. However, CO2 permeance dropped when GO was incorporated in Pebax matrix. PEG 400 was added in Peabax 1657 matrix to increase CO2 permeance and it was found that the composite membrane containing 50 wt.% PEG 400 in polymer matrix with 0.25 wt.% GO showed the good CO2/CH4 separation factor up to 42.81 and also CO2 permeance of 13.07 GPU. With the obtained results, it could be concluded that GO mainly influenced separation factor because GO generated a rigidified interface between the polymer and fillers Moreover, GO also block the pathway for CH4 through membrane resulting to an increased diffusion distance and enhance the separation between CO2 and CH4. Whereas PEG 400 provided a higher CO2 permeance due to a loose chain of Pebax 1657 matrix.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".