A magnetic hybrid sol–gel ionic network catalyst for direct alcohol esterification under solvent-free conditions
Bibliographic record
Abstract
adsorption-desorption, CHNS elemental analysis, transmission electron microscopy (TEM) and vibrating sample magnetometry (VSM). Optimization experiments demonstrated that the best results were achieved in the esterification of both primary and secondary alcohols with acetic acid (5-7 equivalents), using as little as 0.1 mol% of the catalyst at 85 °C under solvent-free conditions. Under these optimized conditions, the developed catalyst demonstrated exceptional catalytic activity, selectivity, water resistance, and durability in the direct esterification of primary and secondary benzylic, aliphatic, and cyclic alcohols, yielding the corresponding esters in excellent yields ranging from 78% to 99%. Notably, the catalyst could be recovered and reused for up to 10 cycles without any significant loss in its performance and magnetic susceptibility. The strong reactivity and selectivity of the developed catalyst can be attributed to the well-distributed acidic sites on the 3D PIL support, which offers accessible nano-ionic active sites. Additionally, the hydrophobic nature of the network catalysts facilitates the easy diffusion of starting materials and provides excellent water repellency, thereby enhancing the reaction yield.
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 | 0.001 |
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".