A performance-based comparison for the synthesis of Plavix (Clopidogrel) in a microreactor vs. batch reactor: From CuBr2 homogeneous catalysis to heterogeneous catalysis using a Cu-based MOF (VNU-18)
Bibliographic record
Abstract
We investigated Plavix's continuous-flow synthesis through both homogeneous and heterogeneous catalysis utilizing a microreactor. The CuBr2 homogeneous catalyst was used in the former, whereas a Cu-based MOF (i.e., VNU-18 metal-organic framework) heterogeneous catalyst was first synthesized, then characterized, and ultimately utilized in the latter. Each catalytic system's performance was examined concerning factors including feed flowrate, reaction temperature, catalyst loading, residence time, and solvent. Plavix was produced with an optimum yield of 58.2% at 50 °C in 40 min when working under the continuous-flow homogeneous catalysis utilizing DMSO as solvent. Meanwhile, the performances of the batch- and microreactors were examined in the instance of heterogeneous catalysis. According to the findings, the reaction ceased utilizing the microreactor device after 25 min, yielding up to 42.7% product at room temperature using DMF as solvent. However, the product yield of 50.7% was attained in a batch system after 12 h. A comparison between the performance of the flow reactors with that of the batch system reveals that the flow systems are more promising to be the future trend of processing at the industrial scale for the Plavix production than that of the batch in terms of the comparable product yields and lowered reaction time.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".