Kinetics and mechanisms of the catalytic thermal cracking of asphaltenes adsorbed on supported nanoparticles
Bibliographic record
Abstract
The production of heavy and extra-heavy oil is challenging because of the rheological properties that crude oil presents due to its high asphaltene content. The upgrading and recovery processes of these unconventional oils are typically water and energy intensive, which makes such processes costly and environmentally unfriendly. Nanoparticle catalysts could be used to enhance the upgrading and recovery of heavy oil under both in situ and ex situ conditions. In this study, the effect of the Ni-Pd nanocatalysts supported on fumed silica nanoparticles on post-adsorption catalytic thermal cracking of n -C 7 asphaltenes was investigated using a thermogravimetric analyzer coupled with FTIR. The performance of catalytic thermal cracking of n -C 7 asphaltenes in the presence of NiO and PdO supported on fumed silica nanoparticles was better than on the fumed silica support alone. For a fixed amount of adsorbed n -C 7 asphaltenes (0.2 mg/m 2 ), bimetallic nanoparticles showed better catalytic behavior than monometallic nanoparticles, confirming their synergistic effects. The corrected Ozawa–Flynn–Wall equation (OFW) was used to estimate the effective activation energies of the catalytic process. The mechanism function, kinetic parameters, and transition state thermodynamic functions for the thermal cracking process of n -C 7 asphaltenes in the presence and absence of nanoparticles are investigated.
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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".