In Situ Preparation of Alumina Nanoparticles in Heavy Oil and Their Thermal Cracking Performance
Bibliographic record
Abstract
This work details a technique for the in situ preparation of alumina nanoparticles in heavy oil and explores their activity with respect to mild thermal cracking. In principle, in situ-prepared nanoparticles display a high level of dispersion, which should improve their catalytic activity. Dispersed alumina nanoparticles 17 ± 5 nm in mean diameter were successfully prepared at 300 °C and characterized using X-ray diffraction, transmission electron microscopy, and energy-dispersive X-ray spectroscopy. The thermal cracking experiments were conducted in a batch reactor setup under two stages of heating at 300 and 350 °C. The pressure buildup in the reactor and the viscosity and °API gravity of the resultant oil were taken as measures of the extent of thermal cracking. Although there was a general shift toward higher °API gravity, it still fell within the level of uncertainty probably due to agglomeration at 350 °C that limited nanoparticle activity. A higher viscosity was obtained for the liquid fraction because of cross-linking. Furthermore, the uptake of hydrocarbon by the in situ-prepared nanoparticles was compared with that of commercial alumina nanoparticles. Uptake values with and without n -heptane or dichloromethane washing suggest different adsorbed species on the two types of particles.
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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".