Novel photocatalyst system to deep desulfurization of petroleum model and gas condensate by Box–Behnken design
Bibliographic record
Abstract
In this research, cobalt (Co)/molybdenum (Mo) and nickel (Ni) doped with titanium dioxide (TiO2) were loaded onto multi-walled carbon nanotubes (MWCNTs). Then, the magnetization catalyst, iron oxide (Fe3O4), was loaded on them, which was used for the deep desulfurization of dibenzothiophene (DBT). These catalysts were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy, differential reflectance spectroscopy, and Brunauer–Emmett–Teller, Barrett–Joyner–Halenda, and vibrating sample magnetometer techniques. The photocatalytic activity of these catalysts was experienced under visible light using DBT. The response surface methodology based on the Box–Behnken design was used to evaluate parameters, including catalyst dosage (g), time (min), and concentration of DBT (mg L−1). The highest degradation efficiency under optimal conditions for CoMoNi/TiO2/MWCNTs/Fe3O4 catalysts with a catalyst dosage of 0.3 g, a time of 180 min, and a concentration of 50 mg L−1 was 99.99%. Optimum conditions were studied for desulfurization of the gas condensate. The highest desulfurization efficiency (90.33%) was obtained by the CoMoNi/TiO2/MWCNTs/Fe3O4 catalyst.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".