Polypropylene hollow fibre membranes treated with grafted‐aminated poly (glycidyl methacrylate) in the gas mixture separation
Bibliographic record
Abstract
Abstract Bonding polymerization is a straightforward and efficient approach for enhancing the quality of adsorbents and improving the properties of polymers and their bonding chains. The objective of this research was to enhance the ability of polypropylene hollow fibres to adsorb CO 2 . The surface of the adsorbent was modified using gamma irradiation in combination with glycidyl methacrylate and different amines, such as ethanolamine, triethylamine, and diethylamine. The efficacy of the modification process was evaluated by altering the graft variables, such as monomer concentration and gamma dose rate to determine the grafting degree (GD, %). Similarly, the amination yield (DA, %) was controlled through changes in the amine parameters, including amine type and concentration. Scanning electron microscope (SEM) techniques were used to examine the morphology of the modified hollow fibre membrane, while Fourier transform infrared spectroscopy (FTIR) was utilized to analyze the chemical structures of the fibres. Subsequently, the impact of gas flow intensity at the CO 2 inlet, with flow rates of 50, 100, and 150 cm 3 /min, and concentrations of 5%, 10%, and 15%, was investigated. The adsorption rate decreased significantly with an increase in gas flow rate at the inlet due to the short contact time and quick saturation. Additionally, the adsorption rate decreases notably with the increment of CO 2 concentration. The findings of this study indicate that the utilization of radiation resulted in the creation of a unique adsorbent with exceptional adsorption capabilities. Furthermore, this adsorbent was effectively recognized during the process of carbon dioxide adsorption.
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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".