TiO<sub>2</sub>–SiO<sub>2</sub>-Composite-Supported Catalysts for Residue Fluid Catalytic Cracking Diesel Hydrotreating
Bibliographic record
Abstract
Composites of TiO 2 –SiO 2 oxides (CTS) with various Ti/Si atomic ratios were prepared by the sol–gel method, and the CO 2 supercritical fluid extraction method was used to remove solvent in the gel. The effects of the Ti/Si atomic ratio and calcination temperature on the specific surface area, pore structure, acidity, and coordination status of the Ti atoms were investigated by N 2 desorption, pyridine adsorption, X-ray diffraction (XRD), and X-ray absorption fine structure (XAFS), respectively. Hydrotreating catalysts were prepared with cobalt (Co)–molybdenum (Mo) and nickel (Ni)–tungsten (W) as active metal components supported on CTS-1 and CTS-4, respectively. The hydrotreating activities of the catalysts were tested by processing residue fluid catalytic cracking (RFCC) diesel on a fixed-bed reactor. It was found that hydrodesulfurization (HDS), hydrodenitrogenation (HDN), and hydrodearomatization (HDA) were affected by the acidity of the support and/or catalyst, which was related to the Ti/Si atomic ratio. The catalysts with strong Lewis acidity had better hydrotreating activities for HDS, HDN, and HDA. The study provides insight into a fundamental understanding of the relationship between the Ti/Si atomic ratio of the support and acidity of catalysts and the effect of acidity on the hydrotreating activity of the 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) | 0.000 | 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".