Improving the Selectivity toward Three‐Component Biginelli versus Hantzsch Reactions by Controlling the Catalyst Hydrophobic/Hydrophilic Surface Balance
Bibliographic record
Abstract
Abstract The catalytic activities and selectivities of two kinds of mesoporous solid acids SBA‐15‐PrSO 3 H 1 , SBA‐15‐Ph‐PrSO 3 H 2 , and a periodic mesoporous organosilica (PMO) based solid acid Et‐PMO‐Me‐PrSO 3 H 3 that comprise different physicochemical surface properties were compared in an environmentally benign one‐pot, three‐component Biginelli reaction of aldehydes, β‐ketoesters and urea or thiourea under solvent‐free conditions. Among these mesoporous solid acid catalysts, 3 , which has a hydrophobic/hydrophobic balance in the nanospaces (mesochannels) in which the active sites are located, is found to be a significantly more selective catalytic system in the Biginelli reaction; it produces the corresponding 3,4‐dihydropyrimidin‐2‐one\thione (DHPM) 5 derivatives in good to excellent yields and excellent selectivities. Notably, in the case of conducting the three‐component coupling reaction of benzaldehyde, metylacetoacetate and urea in the presence of 1 result in the generation of a mixture of Hantzsch dihydropyridine 4 (≈37 %) and Biginelli dihydropyrimidinone 5 (≈49 %), whereas the same reaction with 2 (catalyst loading of 1 mol % as well) furnishes the corresponding aldolic product methyl‐2‐benzylidene‐3‐oxobutanoate 6 as the major product (≈80 %) with concomitant formation of small amounts of 5 (<10 %) under essentially the same reaction conditions that are employed with catalyst 3 . Water adsorption–desorption analysis of the catalysts is employed to possibly relate the observed selectivity to the difference in physicochemical properties of the materials.
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".