Microreactor Mixing-Unit Design for Fast Liquid-Liquid Reactions
Bibliographic record
Abstract
Abstract Based on previous work studying complex microreactors, it was desired to further improve the mixing efficiency by varying the mixing unit design for fast liquid-liquid reactions. Different flow regimes were studied, including slug flow, parallel flow, and drop flow. The two-phase hydrolysis of 4-nitrophenyl acetate in sodium hydroxide solution was used to evaluate the overall volumetric mass transfer coefficients (K org a) as a function of the average rate of energy dissipation (ε) for each microreactor design and all flow regimes. The liquid-liquid systems investigated used n-butanol or toluene as the organic phase solvent and a 0.5-M NaOH aqueous solution. The use of surfactant was also investigated with the toluene- water system. All microreactor geometry designs were based on contraction–expansion repeating units with asymmetric obstacles to aid the breakup of slugs and desynchronize the recombination of split streams. The investigated designs were chosen to avoid the formation of the parallel flow regime, contrary to curvature-based mixing-unit designs. The microreactor design can then be optimized to reduce the ε required to reach drop flow, since K org a has been found to be constant at equal ε for a given solvent system in this flow regime, regardless of the reactor selection. Additionally, the “3/7th” scaleup rule was applied and confirmed with the LL-Triangle mixer. It was found that, for low interfacial-tension systems (i.e., n-butanol-water), the onset of drop flow occurred at a lower ε for the LL-Triangle mixer when compared with the Sickle or LL-Rhombus mixers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".