Stability, scalability, and reusability of a volume efficient biocatalytic system constructed on magnetic nanoparticles
Bibliographic record
Abstract
This report investigates for the first time stability, scalability, and reusability characteristics of a protein nano-bioreactor useful for green synthesis of fine chemicals in aqueous medium extracting maximum enzyme efficiency. Enzyme catalysts conjugated with magnetic nanomaterials allow easy product isolation after a reaction involving simple application of a magnetic field. In this study, we examined a biocatalytic system made of peroxidase-like myoglobin (Mb), as a model protein, to covalently conjugate with poly(acrylic acid) functionalized magnetic nanoparticles (MNPs, 100 nm hydrodynamic diameter) to examine the catalytic stability, scalability, and reusability features of this bioconjugate. Application of the conjugate was effective for electrochemical reduction of organic and inorganic peroxides, and for both peroxide-mediated and electrocatalytic oxidation of the protein substrate 2, 2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) with greater turnover rates and product yields than Mb prepared in solution or MNP alone. Mb-attached MNPs displayed extensive catalytic stability even after 4 months of storage compared to Mb present in solution. Five- and ten-fold scale up of MNPs in the bioconjugates resulted in two- and four-fold increases in protein-catalyzed oxidation products, respectively. Nearly 40% of the initial product was present even after four reuses, which is advantageous for synthesizing sufficient products with a minimal investment of precious enzymes. Thus, the results obtained in this study are highly significant in guiding cost-effective development and efficient multiple uses of enzyme catalysts for biocatalytic, electrocatalytic, and biosensing applications via magnetic nanomaterials conjugation.
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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".