Covalent Immobilization of Laccase onto Nanofibrous Membrane for Degradation of Pharmaceutical Residues in Water
Bibliographic record
Abstract
Enzymatic degradation with ligninolytic enzyme, e.g., laccase, is a potential green solution for removal of pharmaceutical compounds that are released into the environment through wastewater effluent. However, the deficiencies of using the enzyme in its free forms, such as reusability and stability, should be addressed before industrial applications. In this study, laccase was immobilized onto tailor-made polyacrylonitrile–biochar composite nanofibrous membrane through covalent bonding, and the parameters of immobilization were optimized. The obtained biocatalyst was utilized for removal of chlortetracycline (CTC), carbamazepine (CBZ), and diclofenac (DCF) at an environmentally relevant concentration in batch mode. These pharmaceutical compounds represented three main categories of pharmaceutical compounds, i.e., antibiotics, antidepressant, and anti-inflammatory. The results showed that the immobilized laccase has improved storage, temperature, and pH stability compared to free laccase. Also, it maintained more than 17% of its initial activity after 10 cycles of ABTS oxidation which indicated improved reusability of the enzyme. Using immobilized laccase for degradation, three pharmaceutical compounds in batch experiments exhibited 72.7%, 63.3%, and 48.6% degradation efficiency for DCF, CTC, and CBZ, respectively, after 8 h of reaction. The decreasing trend of adsorption extent during reaction time for all compounds confirmed the regenerative effect of laccase on adsorption sites of biochar.
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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".