Thermodynamic Modeling of Multi-Staged Extraction Systems for Chiral Separations through Coupled Analysis of Species Equilibria and Mass Transfer
Bibliographic record
Abstract
A flexible and comprehensive model for predicting and optimizing separations of racemates of amino acids and other chiral metabolites in liquid-liquid multi-staged extraction systems is presented. Enantiomer partition coefficients are computed along the extraction path using multiple chemical equilibria theory and measured equilibrium formation constants for every complex formed in the two phases. The large number of speciation reactions typically occurring in ligand-exchange extraction systems requires the development of a robust numerical algorithm, and we present a method to rapidly and accurately solve the large nonlinear set of governing equations. Model performance is assessed through comparison to data for continuous extraction of various racemates within a series of hollow-fiber membrane modules. For each extraction, a chiral-ligand exchange selector molecule is solubilized in the organic phase flowing countercurrent to the aqueous phase into which the racemate is loaded. Enantiomer eluent profiles predicted at different conditions are in very good agreement with experiment. Through its predictive power, the model provides a useful in silico platform for optimizing these complex separations, and model results demonstrating this capability are presented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".