Immobilization of alcalase on polydopamine modified magnetic particles
Bibliographic record
Abstract
Enzymes play a crucial role in medicine, industry, and agriculture. Alcalase, a protease, has found wide-ranging applications in both the detergent and food industries. Immobilizing enzymes has gained prominence as a technology to enhance enzyme stability and reusability, and magnetic particles (MP) have emerged as promising carriers for enzyme immobilization due to their magnetic properties and ease of synthesis. In our study, we propose a novel approach that utilizes polydopamine-modified magnetic particles (MPP) as carriers for immobilizing alcalase. The immobilization process entails modifying the magnetic particles with polydopamine and functionalizing them with glutaraldehyde (GA). We conducted experiments to determine the optimal conditions for alcalase immobilization. These conditions were identified as a pH of 7.5, a GA concentration of 0.23 μg/mL, an alcalase concentration of 6.1 mg/mL, and an immobilization time of 4 hours. The immobilized alcalase significantly improved its temperature and pH stability. Furthermore, kinetic studies of the immobilized enzyme were conducted, revealing that while the Michaelis constant (Km) remained unaffected, there was a decrease in the maximum velocity (Vmax). After 14 repeated uses, it retained 78.66% of its relative activity. This innovative strategy not only enhances our understanding of enzyme immobilization techniques but also offers new avenues for leveraging enzymes in a multitude of applications.
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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) | 0.000 | 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 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".