The effect of the nanosize on surface properties of NiO nanoparticles for the adsorption of Quinolin-65
Bibliographic record
Abstract
Using Quinolin-65 (Q-65) as a model-adsorbing compound for polar heavy hydrocarbons, the nanosize effect of NiO nanoparticles on the adsorption of Q-65 was investigated. Different-sized NiO nanoparticles with sizes between 5 and 80 nm were prepared by the controlled thermal dehydroxylation of Ni(OH)2. The properties of the nanoparticles were characterized using XRD, BET, FTIR, HRTEM and TGA. The effects of the nanosize on the textural properties, the shape and the morphology were studied. The adsorption of Q-65 molecules onto different-sized nanoparticles was tested in toluene-based solutions. On a normalized surface area basis, the number of Q-65 molecules adsorbed per nm(2) of the NiO surface was the highest for NiO nanoparticles of size 80 nm, while that for 5 nm sized NiO nanoparticles was the lowest. Excitingly, the adsorption capacity of other NiO sizes varied from loading suggesting different adsorption behavior, which exhibits the significance of textural properties during the adsorption of Q-65. Computational modeling of the interaction between the Q-65 molecule and the NiO nanoparticle surface was carried out to get more understanding of its adsorption behavior. A number of factors contributing to the enhanced adsorption capacity of nanoscale NiO were determined. These include surface reactivity, topology, morphology and textural properties.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".