Transport Phenomena in Two-Phase Liquid-Liquid Micro-Reactors
Bibliographic record
Abstract
Micro-reactors offer distinct advantages over batch reactors currently used within the pharmaceutical and fine chemical industries. Their high surface area-to-volume ratios allow for increased heat and mass transfer, which is important for controlling reaction selectivity. In addition, micro-reactors are compatible with continuous processing technology, circumventing the time delays inherent to batch systems. Rapid mixing of reactants within micro-reactors is, however, limited by the inherent difficulty of generating turbulence at reduced geometry scales. Several different passive mixing strategies have been proposed in order to produce eddy-based secondary flows and chaotic mixing. This study examines the effectiveness of these strategies by comparing the energy-density normalized heat and mass transfer coefficients for a selection of industrial micro-reactors. First single, then two-phase liquid-liquid experiments were conducted. Pressure drop measurements were obtained to calculate friction factors and to verify the presence of eddy-based secondary flows. A hot heat exchange fluid and temperature measurements were used to estimate the internal convective heat transfer coefficients within each structure. Volumetric mass transfer coefficients were also determined for the mutual extraction of partially miscible n-butanol and water. Semi-empirical correlations for the reactors’ friction factor and Nusselt number as well as a description of the overall mass transfer coefficient based on energy dissipation are presented.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".