Enzyme immobilized on the surface geometry pattern of groove‐typed microchannel reactor enhances continuous flow catalysis
Bibliographic record
Abstract
Abstract BACKGROUND Enzymatic catalysis in a microchannel under continuous flow has attracted increasing scientific interest. It allows for surfaces with higher specificities for heat and mass transfer and makes it easier to control reaction parameters. However, its applications are limited by a lesser amount of loading and cycle of use of the immobilized enzyme. A series of groove‐typed channel microreactors with surface geometry patterns were first established for effective immobilization of naringinase, which was used to produce isoquercitrin from rutin via hydrolysis. RESULTS Of the tested groove‐typed channel microreactors with surface geometry patterns, the enzyme adsorbing capacities of all were increased compared to that with smooth surface. The enzyme adsorbing capacity increased 1.32‐fold in the grooved microreactor with triangular pattern. Excellent yields of isoquercitrin production were produced in grooved channel immobilized enzyme microreactors with triangular surfaces. A yield of 91.72 ± 0.65% was obtained in 10 min, the microreactor could be reused 12 times with a residual activity over 56%. Efficient transfer of heat and mass were attained in the grooved microreactors with surface geometry patterns. The Poiseuille number and Nusselt number were increased by 20.61% and 29.63%, respectively, using fluid mechanics analysis in the grooved microreactor with triangular pattern. CONCLUSION The grooved microreactor with geometry pattern surface provided an efficient method for the high production of isoquercitrin, which is promising for enzyme immobilization and catalysis. © 2019 Society of Chemical Industry
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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) | 0.000 | 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) | 0.001 | 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".