Gas-Liquid Flow and Interphase Mass Transfer in LL Microreactors
Bibliographic record
Abstract
This work investigates the impact of fluid (CO2(g), water) flow rates, channel geometry, and the presence of a surfactant (ethanol) on the resulting gas–liquid flow regime (bubble, slug, annular), pressure drop, and interphase mass transfer coefficient (kla) in the FlowPlateTM LL (liquid-liquid) microreactor, which was originally designed for immiscible liquid systems. The flow regime map generated by the complex mixer geometry is compared to that obtained in straight channels of a similar characteristic length, while the pressure drop is fitted to the separated flows model of Lockhart–Martinelli, and the kla in the bubble flow regime is fitted to a power dissipation model based on isotropic turbulent bubble breakup. The LL-Rhombus configuration yielded higher kla values for an equivalent pressure drop when compared to the LL-Triangle geometry. The Lockhart–Martinelli model provided good pressure drop predictions for the entire range of experimental data (AARE < 8.1%), but the fitting parameters are dependent on the mixing unit geometry and fluid phase properties. The correlation of kla with the energy dissipation rate provided a good fit for the experimental data in the bubble flow regime (AARE < 13.9%). The presented experimental data and correlations further characterize LL microreactors, which are part of a toolbox for fine chemical synthesis involving immiscible fluids for applications involving reactive gas–liquid flows.
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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 | 0.000 | 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".