Transport phenomena in microchannels in liquid–liquid extraction ( <scp>LLE</scp> ) systems operating in a slug flow <scp>regime—A</scp> review
Bibliographic record
Abstract
Abstract In the present work, the effects of various parameters (capillary size, shape, flow ratio, presence of additives, and presence of wall film) on both reactive and non‐reactive LLE systems are presented and analyzed. The literature data for a constant liquid flow ratio of 1 ( Q c / Q D = 1) has been correlated to give rise to two empirical correlations relating the Sherwood number (Sh), Reynolds number (Re), capillary number (Ca), and Schmidt number (Sc) have been developed for 0.1 < Re < 10 and 10 < Re < 200. The concept of j‐factor for micro‐channels has been introduced and named as Pandit–Kunzru–Ganguli analogy. Three new dimensionless numbers have been introduced, namely Pandit number (), Ganguli number , and Kunzru number (). The j‐factor is found to be a strong function of Capillary number for 10 < Re < 200 while it is a strong function of surface renewal for 0.1 < Re < 10. Empirical correlations to calculate j ‐factor as a function of Re or Weber number (We) are also presented. Recommendations for future work have been presented based on the review in the present work.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".