Laccase immobilized on reduced graphene oxide sponges for simultaneous adsorption and enzymatic degradation of endocrine disrupting chemicals
Bibliographic record
Abstract
The presence of endocrine disrupting chemicals (EDCs) in water can impart detrimental effects on public health by mimicking the behaviors of natural hormones and their associated receptors in human body. Studies have demonstrated that ligninolytic enzymes such as laccase can degrade various phenolic compounds, including a broad range of EDCs. In this study, the technique of covalent immobilization of laccase through carbodiimide coupling chemistry on highly adsorptive reduced graphene oxide (rGO) sponges was utilized to effectively remove two representative EDCs; namely, bisphenol A (BPA) and triclosan (TCS) from water. The bio-functionalized adsorbent (rGO-LA) showed a significant improvement in removing BPA (87 % after 24 h) compared to pristine rGO sponge, (~40 % after 24 h). The removal efficiency of both adsorbents for TCS was as high as 84 %, with faster kinetics being observed for rGO-LA. Further investigation using gas-chromatography-mass spectroscopy revealed that the bio-functionalization not only improved the removal efficiency of the adsorbent, but also facilitated the adsorption of the metabolites generated during the biodegradation of BPA and TCS. When temperature was increased to 40 °C, the removal efficiency and kinetics of rGO-LA sponges were improved significantly for BPA (83 % removal in 4 h) and TCS (73 % removal after 4 h). The study highlights the synergy of enzymatic degradation and adsorption, with enhanced performance observed at elevated temperatures, offering a promising solution for effective EDCs mitigation in water treatment, while also ensuring comprehensive contaminant removal by adsorbing the generated metabolites.
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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".