Application of Nanotechnology for Heavy Oil Upgrading: Catalytic Steam Gasification/Cracking of Asphaltenes
Bibliographic record
Abstract
Nanotechnology is a rapidly growing technology with considerable potential applications and benefits. Among the numerous applications of nanotechnology for energy and the environment, adsorption, oxidation, and gasification/cracking of asphaltenes, a problematic constituent present in heavy oil, on nanoparticle surfaces are one of the most recent examples. In this work, three different types of metal oxide nanoparticles, namely, Fe 2 O 3, Co 3 O 4, and NiO, were selected for asphaltene adsorption and catalytic steam gasification/cracking. Adsorption and gasification of asphaltenes were studied using thermogravimetric analysis. The nanoparticles were found to be very efficient for asphaltene adsorption and catalytic steam gasification/cracking. Asphaltene adsorption affinity on the surface of nanoparticles followed the following order: NiO > Co 3 O 4 > Fe 2 O 3 . The catalytic steam gasification/cracking of asphaltenes in the presence of nanoparticles followed the same order as well. The calculated percent conversion at the onset temperature for NiO, Co 3 O 4, and Fe 3 O 4 nanoparticles was 37, 32, and 21%, respectively. A relationship between adsorption affinity and catalytic activity is also found to exist.
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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".