Structural modulation of ionic liquids as efficient catalysts for esterification reaction
Bibliographic record
Abstract
Abstract There is an urgent need to develop green catalytic transesterification technology to reduce liquid waste and instrument corrosion. Ionic liquids with controlled activity, easy recovery, and corrosion resistance have great potential as catalysts for transesterification reactions. In this paper, a series of ionic liquids were prepared using imidazole and ammonium compounds as cations, and sulphuric acid, p ‐toluenesulfonate, and bistrifluoromethanesulfonate anions as anions. In comparison, the ionic liquid made from imidazole with H 2 SO 4 (IMIHS) was found to be the best catalyst for the esterification reaction with high speed, high activity, low corrosion, and high stability, and its catalytic performance was superior to that of sulphuric acid. The optimum reaction conditions were as follows: the molar ratio of ethanol to acetic acid was 1:1, the catalyst amount was 5 wt.%, the reaction temperature was 80°C, and the reaction time was 120 min. The maximum yield of ethyl acetate was 66.2% under the optimum reaction conditions. The catalytic activity was not declined obviously by reusing six times. The activation energy of the reaction is 48.748 kJ/mol.
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".