One‐step synthesis of ordered Sn‐substituted SBA‐16 mesoporous materials using prepared silica source of rice husk and their selectively catalytic activity
Bibliographic record
Abstract
Abstract Highly ordered SBA‐16 silica mesoporous materials were synthesised hydro‐solvothermally under the acidic medium using SiO2/F127/BuOH/HCl/H2O gel. Pure SiO2 powders were prepared from inexpensive and environmentally friendly silica source of rice husk. The pore size of the materials could be optimised by using a blend of P123 and F127 templates. Sn‐substituted SBA‐16 mesoporous materials were yielded via the direct injection of stannic chloride into the fixed gel in acidic medium. X‐ray diffraction, N2 adsorption, scanning electron microscope/transmission electron microscope results suggest that tin ions were incorporated into the Si‐SBA‐16 framework by isomorphous substitution between Sn and Si ions. Elemental analysis indicates that tin can be substituted in the range of Si/Sn = 21.4–10.5. UV–vis, XPS, TPR‐H2, TPD‐NH3 results reveal that tin atoms are highly dispersed in 4+ oxidation state and mostly occupy in the silica framework. The degree of tin incorporation into silica framework can easily be controlled by a simply adjustment of the H2O and HCl molar ratios. The mesoporous Sn‐SBA‐16 materials were an active benzylation catalyst with almost 100% selectivity to monoalkylated product in alkylation of aromatics with benzyl chloride. © 2011 Canadian Society for Chemical Engineering
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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 | 0.001 |
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".