Enzyme immobilization on photopatterned temperature‐response poly (N‐isopropylacrylamide) for microfluidic biocatalysis
Bibliographic record
Abstract
Abstract BACKGROUND Microfluidic chips have gained growing attention from the scientific community due to their large surface/interface area and fast mass transfer. However, their application is challenged by the instability of enzymes and time‐consuming catalytic process. Poly (N‐isopropylacrylamide) (PNIPAAm) coupled with photopatterning technology was applied to immobilize an enzyme on the microchannel surface to realize the biotransformation in this study. RESULTS The photopattern‐immobilized naringinase on a microchip achieved a yield of 93.63 ± 1.12% for isoquercitrin production within 5 min. The production of unit time per unit enzyme (g g−1 h−1) increased 2.1‐fold whereas the reaction time decreased to 1/12 of the time required in a batch reactor. The enzyme was absorbed and desorbed at 40 and 25 °C, respectively, and the release efficiency of immobilized naringinase reached 80.57%, indicating that most of the enzymes were replaced with fresh enzymes to proceed each enzymatic reaction. Six cycles of enzymatic hydrolysis reactions were completed successively, maintaining >60% of relative enzymatic activity. CONCLUSION The results indicated that using the immobilized enzyme on the photopatterned PNIPAAm in the enzymatic hydrolysis of rutin was an efficient and simple way to achieve a high yield of isoquercitrin. Thus, this approach represents a convenient and cost‐saving method to produce fine chemicals using microfluidic biocatalysis. © 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".