Enzymatic Fatty Acid Hydroxylation in a Liquid–Liquid Slug Flow Microreactor
Bibliographic record
Abstract
A fatty acids omega hydroxylation biocatalytic process into an intensified liquid–liquid slug flow microreactor with immobilized or aqueous solution-phase enzyme was proposed and analyzed numerically. Hydroxylation of the tetradecanoic acid by the recombinant P450foxy enzyme produced by an Escherichia coli was chosen as a case study. The liquid–liquid reaction system includes an aqueous continuous liquid phase containing buffer, cofactor, and enzyme (when biotransformation occurs in aqueous phase) and an organic dispersed liquid phase which behaves as a substrate (tetradecanoic acid) reservoir facilitating a constant mass transfer between the organic dispersed and aqueous continuous liquid phases without deactivating the enzyme. The behavior of the intensified microreactor was analyzed through simulation via two-scale, isothermal, unsteady-state models accounting for detailed hydrodynamics, whereupon were tied the thermodynamics and kinetics of fatty acid hydroxylation catalyzed by immobilized or aqueous solution-phase P450foxy enzyme. The effects of key operating parameters as well as the contribution of P450foxy enzyme on the performance of fatty acid hydroxylation process are highlighted. The intensified microreactors with liquid–liquid reaction systems offer a promising option for the fatty acids hydroxylation biocatalytic process because of high specific enzymatic activity as a result of the constant mass transfer of the substrate between the dispersed organic and continuous aqueous liquid phases.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".