Efficient Degradation of Carbamazepine in Continuous and Batch Modes by Laccase-Photo-Fenton-Intensified Hybrid Treatment
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Despite advances in the removal of pharmaceutical residues from aqueous effluents, carbamazepine (CBZ) remains challenging due to its persistence. The low removal efficiency of conventional wastewater treatments reinforces the need to develop innovative approaches, such as hybrid systems. This study combined photo-Fenton reactions with the enzyme laccase (Lac) to effectively remove CBZ from aqueous solutions in batch and continuous-flow regimes. Lac was immobilized on functionalized magnetite nanoparticles (MNPs) to improve stability and operational efficiency. Investigation of the effects of pH, temperature, UVC radiation, and H 2 O 2 dose on Lac activity revealed promising results. Immobilized Lac retained 77.7% of its initial activity after 60 min of UVC exposure. In contrast, the free enzyme lost its activity within 30 min of exposure. In batch mode, the Lac-MNPs/UVC/H 2 O 2 system with 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) as the inducer degraded 91.9% of CBZ in 15 min of reaction at neutral pH. For continuous operation mode, optimization based on a Central Composite Rotatable Design achieved 91.1% CBZ removal at 10 min space-time, 20:1 H 2 O 2:CBZ molar ratio, and 30 μmol L –1 ABTS. The high removal efficiency in both batch and continuous modes indicates the potential application of the developed hybrid laccase-photo-Fenton treatment for effective CBZ degradation.
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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) | 0.001 | 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) | 0.001 | 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".