Enzymatic decolourization of water using loofa sponge as cellular carrier: Immobilization and dye degradation performance
Bibliographic record
Abstract
Abstract In recent years, strong efforts have been made to develop sustainable biocatalytic decolourization processes for dye‐polluted water. In particular, dye‐oxidizing laccase enzymes immobilized on suitable carriers are promising candidates, and can be reused as long as the activity is sufficiently high. In this work, we propose, for the first time, a new methodology to immobilize laccase from Trametes hirsute on a naturally‐grown and decomposable cellular loofa sponge carrier and assess the capability to degrade dye‐polluted water. High immobilization activity is achieved and about 70 % residual activity remains after 8 cycles. Additionally, we determined homogenous and heterogeneous kinetic parameters for free and immobilized enzymes. The results reveal a four times higher Michaelis‐Menten constant of the laccase immobilized on the loofa due to mass transfer and mixing limitations in a packed bed bio‐reactor. The response surface methodology was applied to identify favourable operating conditions. In particular, dye concentration and treatment time determine the decolourization performance, while mixing velocity has only a minor effect. Eventually, the results demonstrated a remarkable dye removal capability with shorter treatment time compared to the previous studies on immobilized laccase reported in the literature.
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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".