Efficient removal of emulsified oil from water by lipase functionalized bio-catalytic graphene oxide sponges
Bibliographic record
Abstract
Conventional water treatment methods struggle to tackle the oil contamination in the form of resilient emulsified droplets, measuring less than 20 μm in wastewater, due to their robust stability. The absence of economically feasible technologies capable of managing these small oil droplets leads to their prolonged existence in water, causing drastic impact on marine life, ecosystem, and public health. As such, there is an urgent need for advanced technologies capable of efficiently removing emulsified oil droplets with minimal residue. The main reason for emulsion stability is the interaction between surfactants and the oil–water interfaces. In this study, we biologically degraded surfactants to disrupt the interfacial layer between water and oil droplets, ultimately leading to destabilizing the highly stable emulsions. We combined the biocatalytic activity of lipase with the high adsorption capacity of reduced graphene oxide (rGO) to treat highly stable emulsified oil. Lipase activity was enhanced after being immobilized on the hydrophobic rGO sponges, compared to its free form, due to the enzyme structural changes. Our results demonstrate that the immobilized lipase effectively degraded the emulsion stabilizer (Tween 20), while the generated metabolites and combined oil droplets were adsorbed by the highly adsorptive rGO sponges. These synergistic mechanisms resulted in more than 96 % removal of emulsified crude oil.
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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) | 0.000 | 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".