Graft Copolymerization of Glycidyl Methacrylate and Ethylene Glycol Dimethacrylate on Alumina for the Removal of Nitrogen and Sulfur Compounds from Gas Oil
Bibliographic record
Abstract
Functionalized polymers were synthesized and applied in removing nitrogen and sulfur compounds from gas oils. In this work, the polyglycidyl methacrylate- co -ethylene glycol dimethacrylate polymer incorporated with tetranitrofluorenone, PGMA-DAP-TENF, was synthesized with and without alumina support. Different techniques were used to characterize the synthesized polymers including Fourier transform infrared spectroscopy, Brunauer–Emmett–Teller method, dynamic light scattering, thermogravimetry/differenial thermal analyzer, carbon hydrogen nitrogen sulfur elemental analysis, and field emission scanning electron microscopy. The performance of the polymer with alumina, Al-PGMA-DAP-TENF, was compared to that without alumina using light gas oil. In addition, heavy gas oil feed was used to confirm the adsorption behavior of both polymers in a higher nitrogen and sulfur environment. The effect of adsorption time and temperature was tested using a 1:5, by weight, polymer to feed ratio. Results have shown that alumina particles enhanced the nitrogen removal efficiency of PGMA-DAP-TENF polymer while sulfur removal efficiency was not affected. The nitrogen removal efficiency of Al-PGMA-DAP-TENF polymer was more than twice that of PGMA-DAP-TENF polymer in LGO feed, and twice that in HGO feed. This was due to the higher surface area of Al-PGMA-DAP-TENF polymer, 202 m 2 /g, compared to that of PGMA-DAP-TENF polymer, 27 m 2 /g. In addition, Al-PGMA-DAP-TENF polymer removed more basic nitrogen compounds than PGMA-DAP-TENF polymer. This was attributed to the acidic nature of alumina particles that enhance the adsorption of basic nitrogen compounds present in gas oil feeds.
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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 | 0.001 | 0.001 |
| 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".