Immobilized enzyme microreactor system with bamboo-based cellulose nanofibers for efficient biotransformation of phytochemicals
Bibliographic record
Abstract
The enzyme immobilization technique has been widely applied in biotechnology, biomedicine, and environmental remediation. In this research, carboxylated bamboo-based cellulose nanofibers (BCNFs) were obtained by one-step oxidation modification of bamboo fibers using ammonium persulphate. The surface carboxyl groups of the BCNFs were modified by a crosslinking agent and then combined with polyethylene imine (PEI) functionalized magnetic nanoparticles to construct a microreactor system for enzyme loading by the methods of electrostatic self-assembly and physical adsorption. Contrasted with free β -glucosidase, the microreactor possesses higher relative enzyme activity at pH 5.5 and 50 °C, and the storage stability is significantly higher, with >75% relative enzyme activity after storage at 4 °C for 15 d. In addition, the β -glucosidase loaded on the microreactor facilitates its separation from the reaction medium and subsequent reuse. After completing five cycles of use, it retained 76.47% of its initial activity. The biotransformation of geniposide reached 93.10%, and the genipin concentration increased 1.2 folds higher than that in the original plant extract. Therefore, PEI@Fe 3 O 4 @BCNFs microreactor immobilized with β -glucosidase can be successfully used to produce higher activity aglucone such as genipin from geniposide, and it might also have the potential to convert phytochemicals by the immobilized enzyme microreactor system with bamboo-based cellulose nanofibers in the natural production field.
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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 | 0.000 |
| 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 | 0.001 | 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".