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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".