Iron-Modified Mesoporous Silica as an Efficient Solid Lewis Acid Catalyst for the Mukaiyama Aldol Reaction
Bibliographic record
Abstract
Fe-MCM-41 and Fe-SBA-15, two different iron-containing mesoporous silicas, were successfully synthesized by a straightforward and versatile method using iron acetylacetonate as a metal precursor. pH adjustment with ammonia during the synthesis was found to be an efficient way to improve the iron content. Physicochemical parameters of the iron-containing mesoporous silicas were obtained by nitrogen physisorption measurements, and the coordination environment of iron elements was validated by UV–vis diffuse reflectance spectroscopy and X-ray photoelectron spectroscopy. The surface acidity was tested by using a series of Hammett indicators. To further distinguish the Lewis acid sites on the surface, a pyridine adsorption FT-IR method was implemented. These prepared nanoporous catalysts were screened in the Mukaiyama aldol reaction as a model reaction catalyzed by a Lewis acid. The Lewis acid catalytic activity of the materials was fine-tuned, and the corresponding aldol products were obtained in good yield and selectivity. More importantly, the solid catalysts were very stable and could be reused at least nine times while maintaining the same catalytic activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".