Experimental validation of equilibrium based mathematical modelling of liquid‐liquid phase transfer catalysis
Bibliographic record
Abstract
The reaction between two compounds which reside in immiscible phases can be accelerated using a phase transfer catalyst. The catalyst helps to transfer a species ion from one phase to the other phase, thus promoting its reaction. Several mechanisms have been proposed to describe phase transfer catalyzed reactions. In this work, we have carried out an experimental and theoretical study of phase transfer catalysis in batch mode. A mathematical model is developed which helps to predict the progress of the reaction under different operating conditions. Here the different species in the aqueous phase are assumed to be in equilibrium and these react with the species in the organic phase. The effect of diffusional resistance inside the dispersed organic phase is shown to be negligible. The two reaction systems studied are the phase transfer catalyzed (i) thioetherification and (ii) benzyl alcohol oxidation using sodium hypochlorite. Batch experiments were performed to determine the effect of catalyst loading, solvent dilution, and pH. The experimental results show that with an increase in catalyst loading, the phase transfer catalyzed reactions were accelerated. The use of a minimal amount of solvent results in a better performance. Benzyl alcohol oxidation is favoured under low pH conditions. The developed model is able to capture the performance of the system over a wide range of operating conditions accurately.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".