Size Effects of NiO Nanoparticles on the Competitive Adsorption of Quinolin-65 and Violanthrone-79: Implications for Oil Upgrading and Recovery
Bibliographic record
Abstract
Using Quinolin-65 (Q-65) and Violantrone (V-79) as model molecules for polar heavy hydrocarbons and resins, the nanosize effects of NiO nanoparticles on the competitive molecular adsorption on nanoparticles were conducted using multiwavelength UV–visible derivative spectrophotometry method. This is important for understanding the role of nanoparticles in oil upgrading and recovery processes. Computer simulations were also carried out to validate the experimental findings and provide more insights on the interactions between the Q-65 and/or V79 molecules and the NiO nanoparticle surface. Different-sized NiO nanoparticles (5, 40, and >100 nm) were synthesized by controlled thermal dehydroxylation of Ni(OH) 2 . Nanoparticles were characterized using XRD, BET, FTIR, and XPS. Macroscopic adsorption isotherms of Q-65 and V-79 molecules over 5 and 40 nm NiO nanoparticles were evaluated in toluene-based solutions as individual and binary solutions. On a normalized surface area basis, the number of Q-65 and V-79 molecules adsorbed per nm 2 of the NiO surface was the highest for 40 nm NiO nanoparticles and lowest for 5 nm NiO. Results also indicated that NiO nanoparticles were more prone to adsorb V-79 than Q-65. Equilibrium binding constants for V-79 and Q-65, determined by the Langmuir adsorption model, showed the binding affinity for V-79 is size dependent. Simultaneous competitive adsorption between V-79 and Q-65 showed that V-79 concentration is an important factor influencing the uptake of both V-79 and Q-65, whereas the concentration of Q-65 affects only Q-65 uptake. Computational modeling results were consistent with the experimental results, confirming our conclusions about the adsorption process in this binary system.
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".