Immobilized cold-active enzymes onto magnetic chitosan microparticles as a highly stable and reusable carrier for p-xylene biodegradation
Bibliographic record
Abstract
Stability and reusability properties are the two most important factors that determine an enzyme’s application in industry. To this end, cold-active crude enzymes from a psychrophile (xylene monooxygenase (XMO) and catechol 1,2-dioxygenase (C1,2D) were immobilized on magnetic chitosan microparticles for the first-time using glutaraldehyde as a linker. The potential application of enzyme-loaded magnetic particles to remove and detoxify dissolved p-xylene from water confirmed the synergistic mechanism of degradation for in-situ bioremediation in soil and water. Immobilization was optimized based on four variables, such as magnetic particle (MPs), chitosan, glutaraldehyde, and enzyme concentrations. The immobilized enzymes were characterized by Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscope (SEM). The immobilized enzymes showed improved pH tolerance ranging from 4.0 to 9.0, better temperature stability ranging from 5 to 50, higher storage stability (∼70% activity after 30 days of storage), and more importantly, reusability (∼40% activity after 10 repetitive cycles of usage) compared to their free form. Also, the immobilization of enzymes increased the effectiveness of the enzymatic treatment of p-xylene in soil (10,000 mg/kg) and water (200 mg/L) samples. As a result of the superior catalytic properties of immobilized XMO and C1,2D, they offer great potential for in situ or ex-situ bioremediation of pollutants in soil or water.
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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 | 0.001 | 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".