Alkylation of Asphaltenes Using a FeCl<sub>3</sub>Catalyst
Bibliographic record
Abstract
Friedel–Crafts alkylation as a strategy to convert asphaltenes to maltenes was investigated. It was postulated that conversion of polar hydroxyl groups in the asphaltenes would make the product more soluble in light hydrocarbons. Reactions were performed with oilsands bitumen-derived materials using FeCl 3 as a catalyst and o -xylene and methanol, separately. Only the reaction of o -xylene with asphaltenes was mildly beneficial; it resulted in 6% conversion of asphaltenes to maltenes and an increase of 9% in straight-run distillate and vacuum gas oil. The reactions of o -xylene with maltenes and bitumen were detrimental, as was all alkylation reactions with methanol. To better understand the nature of the conversion, the reactions were repeated with model compounds. With 2-naphthol, it was found that dimerization of 2-naphthol to produce (1,1′-binaphthalene)-2,2′-diol (BINOL) and the subsequent coordination with iron were the two dominant reactions. The adverse consequences of FeCl 3 -catalyzed conversion could be explained by such intermolecular addition reactions. The reaction of 2-naphthol with methanol and FeCl 3 also caused some chlorination of the product. The possibility that FeCl 3 as a catalyst affected ethers was explored by performing the reaction with dibenzyl ether as feed. It was found that the ether bonds were cleaved. In the presence of o -xylene, ether cleavage was followed by C-alkylation. The increase in maltenes after the reaction of o -xylene with asphaltenes was better explained by C-alkylation following ether cleavage than the reaction between o -xylene and the hydroxyl groups in the asphaltenes. The reaction of dibenzyl ether with methanol produced benzaldehyde, rearrangement products, and heavy gums.
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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) | 0.001 | 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".