Experimental and numerical investigation of the reaction of 2, <scp>4‐thiazolidinedione</scp> and <i>p</i> <scp>‐methoxybenzaldehyde</scp> in microreactors for the production of drugs for diabetes mellitus type 2 treatment
Bibliographic record
Abstract
Abstract The use of microreactors (MRs) in chemical and pharmaceutical industries allows for a series of advantages due to their reduced sizes regarding conventional batch reactors. In the present paper, the transposition of the reaction between 2,4‐thiazolidinedione (TZD) with p ‐methoxybenzaldehyde from batch to a continuous capillary MR was carried out. The microdevice performance was evaluated experimentally and numerically by computational fluid dynamics (CFD). The batch process yielded 92% in 480 min using piperidine for equimolar reactant feed, while the pyrrolidine promoted a 100% yield in a 50 min, both using solvent ethanol. Kinetic and thermodynamic parameters of the synthesis using piperidine and pyrrolidine were also obtained from experimental data. In the transposition to flow chemistry, ethanol was also used as solvent and a product yield of 100% (140°C, pyrrolidine) was obtained for a residence time of 20 min, representing a reduction of 24 times in the reaction time. In the numerical simulations by CFD, two mathematical models were elaborated: a transient batch and a steady‐state continuous flow. Both models exhibited good agreement with experimental data. The average relative deviations of TZD conversion and the reaction yields in MRs were, respectively, 0.23% and −7.1% (78°C) and 1.7% and 1.2% (140°C).
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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 | 0.000 | 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 | 0.001 | 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".