Antifouling Cellulose Hybrid Biomembrane for Effective Oil/Water Separation
Bibliographic record
Abstract
Oil/water separation has been of great interest worldwide because of the increasingly serious environmental pollution caused by the abundant discharge of industrial wastewater, oil spill accidents, and odors. Here, we describe simple and economical superhydrophobic hybrid membranes for effective oil/water separation. Eco-friendly, antifouling membranes were fabricated for oil/water separation, waste particle filtration, the blocking of thiol-based odor materials, etc., by using a cellulose membrane (CM) filter. The CM was modified from its original superhydrophilic nature into a superhydrophobic surface via a reversible addition-fragmentation chain transfer technique. The block copolymer poly{[3-(trimethoxysilyl)propyl acrylate]-block-myrcene} was synthesized using a "grafting-from" approach on the CM. The surface contact angle that we obtained was >160°, and absorption tests of several organic contaminants (oils and solvents) exhibited superior levels of extractive activity and excellent reusability. These properties rendered this membrane a promising surface for oil/water separation. Interestingly, myrcene blocks thiol (through "-ene-" chemistry) contaminants, thereby bestowing a pleasant odor to polluted water by acting as an antifouling material. We exploited the structural properties of cellulose networks and simple chemical manipulations to fabricate an original material that proved to be effective in separating water from organic and nano/microparticulate contaminants. These characteristics allowed our material to effectively separate water from oily/particulate phases as well as embed antifouling materials for water purification, thus making it an appropriate absorber for chemical processes and environmental protection.
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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) | 0.000 | 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".