Enhancement of Interphase Transport in Mini-/Microscale Applications Using Passive Mixing
Bibliographic record
Abstract
Structured mini-/microscale reactors continue to receive attention from both industry and academia due to their low pressure drop, high heat and mass transfer rates, and ease of scale-up relative to conventional reactor technology. Commonly considered for reactions such as hydrogenations, hydrodesulfurization, oxidations, and Fischer–Tropsch synthesis, the performance of these systems is highly dependent on mixing and the interfacial area between phases. While existing literature describes the initial flow patterns generated by a broad range of two-phase contactors, few studies explore the dynamic impacts of downstream passive mixing elements. Experimental and computational methodologies for characterizing two-phase flow pattern transitions, pressure drop, and heat and mass transfer are discussed, with relevant examples for serpentine and Venturi-based passive mixing designs. The efficacies of these two configurations are explored in the context of pressure drop, conditions leading to significant interface renewal, and design considerations for optimizing mass transfer. Challenges associate with the characterization of multiphase flow through these systems are highlighted, and strategies suggested for both experimental and computational analysis of dynamic flow patterns and fluid–fluid interactions.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".