Bibliographic record
Abstract
Abstract Liquid–liquid extraction, also known as solvent extracting, is a well‐established separation technique that depends on the unequal distribution of a solute between two immiscible liquids. The initial feed liquid containing the solute is brought into contact with a solvent that is selected to have a greater affinity for the solute. The partition of the solute can be enhanced by adding a chemical extractant to the solvent; this practice is widespread in the hydrometallurgical and nuclear industries. Most industrial extractors operate continuously with countercurrent flow of the two phases. In mixer–settlers, the phases are contacted as a well‐agitated dispersion of drops, which are then sent to settling tanks for phase disengagement. In extraction columns, the dispersed drops move countercurrently against the flow of the second (continuous) phase. The physics, chemistry, and practice of extraction, with brief descriptions of important industrial extraction processes and equipment, are presented. Research on hydrodynamic aspects of process design, eg, axial mixing, drop dispersion, and coalescence, is reviewed. New extraction techniques, eg, membrane extraction, supercritical exctraction, and two‐phase aqueous extraction, are discussed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.047 |
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".