Effect of operating factors on liquid–liquid mass transfer and dispersion pattern of sedimentary liquid in a mechanically stirred vessel
Bibliographic record
Abstract
Abstract Liquid–liquid dispersion and mass transfer were investigated in mechanically stirred vessels without baffles by changing operation factors such as an impeller rotation speed, off‐bottom clearance, volumetric liquid ratio, etc. The dispersion regime was categorized into five groups: the sedimentary liquid was kept at the vessel bottom (I), partially elevated without any collision (II), partially dispersed by colliding with the impeller bottom (III), both liquids were partially dispersed by collisions with impeller blades (III’), and the sedimentary liquid was completely dispersed (IV). The dispersion switched to I → II → III → IV with the increasing rotation speed and decreasing off‐bottom clearance. The liquid–liquid mass transfer rate was significantly enhanced with the collision of the sedimentary liquid with the impeller bottom, and subsequently increased with the increasing rotation speed, volumetric liquid ratio, and vessel diameter and with the decreasing off‐bottom clearance. A multiple regression analysis method was applied to determine the mass transfer rates of III and III’.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".