Application of Nanotechnology for Heavy Oil Upgrading: Catalytic Steam Gasification/Cracking of Asphaltenes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it