Slurry-Phase Hydrogenation of Different Asphaltenes to Liquid Fuels on Dispersed MoS<sub>2</sub> Nanocatalysts
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Asphaltene, the most complex and recalcitrant fraction of heavy oil, was investigated in this study to gain new insights into its structure and reactivity. Two types of asphaltenes, ECT-As and COB-As, were extracted from ethylene cracking tar (ECT) and Canada’s oil sands bitumen (COB), respectively, and used as reactants for slurry-phase hydrogenation. Characterization of ECT-As and COB-As was carried out by a combination of techniques, including XRD, elemental analysis, simulated distillation, SEM, TEM, NMR, and FT-IR, to gain insights into their composition and structure. A dispersed MoS 2 nanocatalyst was used to study the reactivity of ECT-As and COB-As under hydrogenation conditions. The results showed that under optimal catalytic conditions, the vacuum residue content of hydrogenation products could be reduced to less than 20%, and the products contained over 50% light components (gasoline and diesel oil), indicating that ECT-As and COB-As were effectively upgraded. The characterization results indicated that ECT-As contained a higher aromatic carbon content, shorter alkyl side chains, fewer heteroatoms, and less highly condensed aromatics than COB-As. The light components (gasoline and diesel oil) of ECT-As hydrogenation products mainly consisted of aromatic compounds with 1–4 rings, with the alkyl chains mainly composed of C1–C2, while light components of COB-As hydrogenation products were mainly composed of aromatic compounds with 1–2 rings and C11–C22 paraffins. The characterization of ECT-As and COB-As and their hydrogenation products revealed that ECT-As was an “archipelago type” asphaltene, composed of multiple small aromatic nuclei interconnected through short alkyl chains, while COB-As was an “island type” asphaltene, with long alkyl chains connected to aromatic nuclei. It is suggested that the structure of asphaltene has a significant impact on both its reactivity and product distribution.
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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".