Investigation of the effect of functionalized carbon nanotube and graphene oxide in guar gum‐based mixed matrix membrane for gas separation application
Bibliographic record
Abstract
Abstract The present study deals with preparing mixed matrix membranes (MMMs), a new polysaccharide‐based natural polymer used as a matrix with functionalized carbon nanotubes (FCNTs) and graphene oxide (GO) used as an inorganic filler. This work identified the effect of the inorganic fillers (FCNTs or GO) with naturally occurring polymer for gas separation. The incorporation of fillers improves the gas separation performance of MMMs. In GG/FCNTs MMMs, the selectivities of CO 2 /N 2 and CO 2 /H 2 were enhanced by 55.24% and 57.89%, respectively. Moreover, in GG/GO MMMs, the selectivities of CO 2 /N 2 and CO 2 /H 2 were improved by 99.50% and 50%, respectively. The membrane was characterized by scanning electron microscopy (SEM) and Fourier‐transform infrared spectroscopy (FTIR). The SEM analysis of GG/GO MMMs reveals layered structure, and GG/FCNTs MMMs create passages to transport gases. The Universal testing machine (UTM) is used to analyze the mechanical properties of pristine and modified membranes.
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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) | 0.000 | 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 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".