Laccase-Functionalized Hexagonal Boron Nitride-Coated Sponges for the Removal and Degradation of Anthracene
Bibliographic record
Abstract
Superhydrophobic porous materials, for instance, sponges, membranes, and meshes, have attracted great attention due to their ability for adsorption of organic solvents, hydrocarbons, and oils while repelling water. In this work, we report an organic-solvent-free, environmentally benign, and cost-effective preparation method of a bifunctional adsorbent using a melamine sponge coated with few-layer hexagonal boron nitride nanosheets (hBNNs) functionalized with laccase (LA). The hBNNs are used as a building block for immobilizing and stabilizing LA for the removal and degradation of anthracene, a polycyclic aromatic hydrocarbon in crude oil, and to convert them to lighter and less-toxic substances. The physiochemical properties and performance of the sponges for the removal and degradation of anthracene were investigated thoroughly. The highest hydrocarbon degradation of 89% was obtained at an LA concentration of 2.72 mg/mL and pH 7, after 72 h. The degradation increased to 91% after 7 days of functionalized sponge exposure to the medium. Moreover, the functionalized sponges’ reusability studies revealed that the anthracene degradation efficiency was still as high as 54% after the hBNN–polyethylene oxide–LA sponges were repeatedly used 5 times. The strategy proposed for the fabrication of these sponges is facile and easy to scale up, not requiring the use of a complicated process or expensive equipment. These nano-engineered sponges are promising candidates for the separation and degradation of oils and hydrocarbons in oil spill remediation applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".