Nanosize effects of NiO nanosorbcats on adsorption and catalytic thermo‐oxidative decomposition of vacuum residue asphaltenes
Bibliographic record
Abstract
The nanosize effects of NiO nanosorbcats on adsorption and post‐adsorption catalytic thermo‐oxidative decomposition of vacuum residue (VR) n‐C 5 asphaltenes was investigated using a UV‐vis spectrophotometer and thermogravimetric analyzer coupled with a mass spectrometer. Sizes between 5 and 80 nm of different‐sized NiO nanosorbcats were employed. Batch adsorption experiments were carried out for the considered asphaltenes in toluene solutions, monitored via UV‐vis spectrophotometry. The macroscopic adsorption isotherms were described by implementing the solid‐liquid equilibrium (SLE) model. The findings showed that thermally cracked vacuum residue (VR) n‐C 5 asphaltenes interact to different extents with different‐sized NiO nanosorbcats. A normalized surface area basis was used for the amount of VR n‐C 5 asphaltene adsorbed per nm 2 of NiO surface, which was the highest for NiO nanoparticles of size 80 nm, with 5 nm size being the lowest. Thermogravimetric analysis of VR n‐C 5 asphaltenes was also achieved and the reaction products were explored by a mass spectrometer. The Kissinger‐Akahira‐Sunose (KAS) isoconversional model was used to describe the reaction mechanism and to confirm the validity of the catalytic role of the different particle sizes of NiO nanosorbcats. The highest catalytic activity was for smallest NiO when compared to the highest NiO nanosorbcats. Furthermore, the results of thermodynamic transition state parameters of activation; changes in Gibbs free energy (ΔG ‡ ), entropy (ΔS ‡ ), and enthalpy (ΔH ‡ ) highlighted the catalytic activity of NiO nanosorbcats towards VR n‐C 5 asphaltenes oxidation. These findings exhibit the significance of textural properties and nanosize of nanoparticles during adsorption and thermal catalytic processing of asphaltenes.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".