The Development of Flow‐through Bio‐Catalyst Microreactors from Silica Micro Structured Fibers for Lipid Transformations
Bibliographic record
Abstract
This study demonstrates the utility of a flow-through enzyme immobilized silica microreactor for lipid transformations. A silica micro structured fiber (MSF) consisting of 168 channels of internal diameter 4-5 μm provided a large surface area for the covalent immobilization of Candida antartica lipase. The specific activity of the immobilized lipase was determined by hydrolysis of p-nitrophenyl butyrate and calculated to be 0.81 U/mg. The catalytic performance of the lipase microreactor was demonstrated by the efficient ethanolysis of canola oil. The parameters affecting the performance of the MSF microreactor, including temperature and reaction flow rate, were investigated. Characterization of the lipid products exiting the microreactor was performed by non-aqueous reversed-phased liquid chromatography (NARP-LC) with evaporative light scattering detector (ELSD) and by comprehensive two-dimensional gas chromatography (GC x GC). Under optimized conditions of 1 μL/min flow rate of 5 mg/mL trioleoylglycerol (TO) in ethanol and 50 °C reaction temperature, 2-monooleoylglycerol was the main product at > 90% reaction yield. The regioselectivity of the Candida antartica lipase immobilized MSF microreactor in the presence of ethanol was found to be comparable to that obtained under conventional conditions. The ability of these reusable flow-through microreactors to regioselectively form monoacylglycerides in high yield from triacylglycerides demonstrate their potential use in small-scale lipid transformations or analytical lipids profiling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".