Highly selective bio-functionalized graphene-based sponges for adsorption and degradation of polycyclic aromatic hydrocarbon mixtures
Bibliographic record
Abstract
Xenobiotic pollutants such as polycyclic aromatic hydrocarbons (PAHs), originating from the incomplete combustion of organic substances, yield harmful effects on both the environment and public health. Existing research highlights that ligninolytic enzymes, including laccase, exhibit the capability to degrade different PAHs to varying degrees. Enzyme immobilization on a support enhances their suitability for industrial uses, typically leading to improved storage and operational stability. This study aims to enhance the elimination of PAHs such as naphthalene, anthracene, phenanthrene and pyrene and their mixture from water by merging the biocatalytic activity of laccase with the high adsorption capacity of a reduced graphene oxide (rGO) sponge. Our findings revealed that as the molecular weight and hydrophobic properties of PAHs increased, their affinity towards the rGO sponges became more pronounced. Conversely, it was noted that the elimination of naphthalene exhibited remarkable enhancement (achieving 75 % removal after 48 h individually, and 82 % removal in PAH mixtures), demonstrating faster removal kinetics in contrast to other PAHs. This improvement was attributed to the utilization of a bio-functionalized rGO sponge, indicating the notable role of immobilized laccase in the degradation of naphthalene. As observed, certain PAHs in the mixture were more susceptible to oxidation and enzymatic degradation, while those with a higher affinity for adsorption onto the rGO surface demonstrated reduced degradability. This selective mechanism effectively treated specific PAHs based on their structural characteristics, thus enhancing the overall efficiency in removing diverse PAH contaminants in mixtures. The results regarding PAH degradation by-products indicated that laccase primarily converted anthracene into 9,10-anthraquinone, most of which were adsorbed and subsequently eliminated by the rGO sponge acting as the enzyme's support. • Bio-functionalized rGO sponges for removal of emerging contaminants of concern. • Multifunctional sponges by covalent immobilization of laccase on rGO sponges. • High selectivity and efficiency in removal of polycyclic aromatic hydrocarbons. • Simultaneous biodegradation and adsorption of the metabolites and by-products. • All-in-one strategy for mitigating contaminants in simple and complex water matrices.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".