Adsorptive Removal of Nitrogen, Sulfur, and Aromatic Compounds from Gas Oil by Poly(glycidy methacrylate) Using Two Kinds of Graft Polymerization Methods
Bibliographic record
Abstract
Based on a polyglycidyl methacrylate- co -ethylene glycol dimethacrylate copolymer (PGMA- co -EGDMA), nitrogen, sulfur, and aromatic compounds were removed from light and heavy gas oil feeds. The method in which PGMA- co -EGDMA is synthesized can influence the textural and chemical characteristics of the polymer and thus its adsorption capacity. Studies have shown that using cerium initiated graft polymerization in PGMA- co -EDGMA synthesis can improve the adsorption capacity of the polymer. In this work, nitrogen, sulfur, and aromatics removal capacity of (PGMA- co -EGDMA) polymer incorporated with tetranitrofluorenone (TENF) via 1,3 diaminopropane (PDA) using cerium initiated graft polymerization were compared with the same polymer without using cerium. A third polymer with different linker, ethylenediamine (EDA) instead of PDA, was synthesized using cerium initiated graft polymerization to inspect the impact of the linker on the removal efficiency. The synthesized polymers were characterized using different characterization methods. The synthesized polymers were tested at different nitrogen, sulfur, and aromatic content using light and heavy gas oil feeds. In addition, the removal capacity of the synthesized polymers toward nonbasic nitrogen were determined using automatic potentiometric titrator. Results have shown that using cerium graft polymerization on the synthesis of PGMA- co -EGDMA polymer reduced surface area, pore size and volume, and amount TENF grafted, thus decreasing the removal efficiency of nitrogen, sulfur, and aromatics. However, polymer selectivity toward nonbasic nitrogen was not affected by cerium graft polymerization. Furthermore, the adsorption capacity of the PGMA- co -EGDMA decreased with increasing linker length due to steric hindrance effect that influences the adsorption capacity of the polymer.
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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) | 0.001 | 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".