Covalent Immobilization of Laccase onto Nanofibrous Membrane for Degradation of Pharmaceutical Residues in Water
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it