Carbon Dioxide Adsorption on Grafted Nanofibrous Adsorbents Functionalized Using Different Amines
Bibliographic record
Abstract
Of late, the demand for new CO2 adsorbents with high adsorption capacity and stability is growing very fast. Nanofibrous adsorbents are potential materials for such application with most attempts made on carbon nanofibers. In this study, a series of electrospun nanofibrous adsorbents containing amines were prepared using a 3-stage promising approach and tested comparatively for CO2 capture. The preparation of adsorbents involved electrospinning of syndiotactic polypropylene (s-PP) solution, radiation-induced grafting (RIG) of glycidyl methacrylate (GMA) onto electrospun nanofibers, and functionalization of poly-GMA grafted s-PP nanofibrous mats with different amines, including ethanolamine (EA) diethylamine (DEA) and triethylamine (TEA). The effect of different amination parameters: namely, amine concentration, reaction time, temperature, and degree of grafting (DG) on the degree of amination (DA), was evaluated. The nanofibrous mats containing amine were tested for CO2 adsorption in a fixed bed column operated under various parameters such as amine density, amine type, initial CO2 concentration and temperature. The adsorbents recorded CO2 adsorption capacities of 2.87, 2.06 and 0.94 mmol/g for EA-, DEA- and TEA-containing adsorbents, respectively, at 30° C using initial CO2 concentration of 15%. This was coupled with the same order of high amine efficiency of 75, 57, and 31%. Results demonstrated that the nanofibrous adsorbent containing amine had strong potential for CO2 capture application.
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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) | 0.001 | 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".