Metal Oxide Nanoparticles for Asphaltene Adsorption and Oxidation
Bibliographic record
Abstract
This study investigates the adsorption and oxidation of asphaltenes onto nanoparticles. Six different metal oxide nanoparticles were employed, namely, Fe 3 O 4, Co 3 O 4, TiO 2, MgO, CaO, and NiO. Batch adsorption experiments were carried out at different initial asphaltene concentrations. Asphaltene adsorption was evaluated by measuring the asphaltene concentration using thermogravimetric analysis, and adsorption kinetics and isotherms were obtained. For all the six nanoparticles, the isotherm data fitted well to the Langmuir model. Results showed that asphaltene adsorption is metal-oxide-specific and the adsorption capacities of asphaltenes onto the oxides followed the order CaO > Co 3 O 4 > Fe 3 O 4 > MgO > NiO > TiO 2 . Furthermore, oxidation of asphaltene was investigated after adsorption onto NiO nanoparticles. The oxidation temperature of asphaltene decreased by ∼140 °C in the presence of nanoparticles, showing their catalytic effect. The activation energies calculated by the Coats−Redfern method for asphaltene oxidation processes in the absence and presence of NiO nanoparticles were found to be approximately 100 and 57 kJ/mol, respectively. This study is a first step in showing the feasibility of using nanoparticles for asphaltene adsorption, followed by catalytic oxidation for heavy oil upgrading.
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.
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".