Recent Developments in the Immobilization of Laccase on Carbonaceous Supports for Environmental Applications - A Critical Review
Bibliographic record
Abstract
Τhe ligninolytic enzyme laccase has proved its potential for environmental applications. However, there is no documented industrial application of free laccase due to low stability, poor reusability, and high costs. Immobilization has been considered as a powerful technique to enhance laccase's industrial potential. In this technology, appropriate support selection for laccase immobilization is a crucial step since the support could broadly affect the properties of the resulting catalyst system. Through the last decades, a large variety of inorganic, organic, and composite materials have been used in laccase immobilization. Among them, carbon-based materials have been explored as a support candidate for immobilization, due to their properties such as high porosity, high surface area, the existence of functional groups, and their highly aromatic structure. Carbon-based materials have also been used in culture media as supports, sources of nutrients, and inducers, for laccase production. This study aims to review the recent trends in laccase production, immobilization techniques, and essential support properties for enzyme immobilization. More specifically, this review analyzes and presents the significant benefits of carbon-based materials for their key role in laccase production and immobilization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".