Tailor‐Made Mesoporous Ti‐SBA‐15 Catalysts for Oxidative Desulfurization of Refractory Aromatic Sulfur Compounds in Transport Fuel
Bibliographic record
Abstract
Abstract We propose large‐pore titanium‐containing organosilylated mesoporous silica (Ti‐SBA‐15) as a highly efficient catalyst for the oxidative desulfurization (ODS) of refractory aromatic sulfur compounds with the aim to produce ultra‐low sulfur diesel. To achieve this, we synthesized a series of mesoporous Ti‐SBA‐15 catalysts according to a new procedure. The procedure is based on the controlled grafting of titanium chelates on SBA‐15 silica at low temperatures (5 °C). This specific synthesis procedure ensured a high dispersion of the required 4‐coordinate tetrahedral Ti4+ sites located on the mesopore surface. To substantiate the influence of the titanium content and mesopore size on the ODS performance of the catalysts, the parameters were varied in the range of 0.7 to 4.7 mol % (Si/Ti) and 5.1 to 9.0 nm, respectively. The resulting Ti‐SBA‐15 catalysts were then tested in the oxidative desulfurization (ODS) of model sulfur‐containing compounds in the presence of cumene hydroperoxide (CHP) as the organic oxidant. The ODS of a real industrial diesel fuel was also carried out in a continuous fixed bed reactor with the same Ti‐SBA‐15 catalysts and CHP. The catalytic results revealed that the Ti‐SBA‐15 catalysts with the largest pore sizes (>7.3 nm) and highest Ti contents (>2.8 mol %) were highly active catalysts for ODS reactions. Moreover, the catalysts with large pores and high Ti loadings appeared to be stable for over 30 h and were far less prone to deactivation than their equivalent Ti‐SBA‐15 samples with smaller pore diameters and lower Ti contents.
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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) | 0.000 | 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 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".