Application of reactive extraction for the separation of pseudomonic acids: Influencing factors, interfacial mechanism, and process modelling
Bibliographic record
Abstract
Abstract Mupirocin (a mixture of A, B, C, and D pseudomonic acids—four complex, similar structures) is an antibiotic belonging to the monocarboxylic acid class, used to cure infections caused by bacteria (Gram‐positive), especially methicillin‐resistant Staphylococcus aureus (MRSA). Its biotechnological production and separation need constant attention, as improving antibiotics is important in the pharmaceutical industry. This is the first study regarding pseudomonic acids separation through reactive extraction; the experiments were focused on finding a proper combination of extractant and diluent for optimization of the separation yield. The pH influence (4–9) and extractant (TOA and Amberlite LA‐2, 5–20 g/L) concentration dissolved in n‐heptane (green solvent) have been analyzed, obtaining the maximum extraction yield (88.78%) at pH 4, 20 g/L extractant, and 10% octanol added to the organic solvent. The addition of the phase modifier, 1‐octanol, improved the extraction yield by 2.6 times for Pseudomonic acid A due to interactions of formed complexes with the phase modifier that improves its solubility. The experimental results for determining the mechanism of the interfacial reaction showed that, regardless of the pH value and solvent polarity (modified by the addition of 1‐octanol), only one molecule of Pseudomonic acid A and extractant react at the aqueous‐organic interface. In addition, the system was modelled and optimized with a methodology combining artificial neural networks (ANN) and differential evolution.
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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) | 0.000 | 0.001 |
| 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".