Functionalized Tetrapodal ZnO Membranes Exhibiting Superoleophobic and Superhydrophilic Character for Water/Oil Separation Based on Differential Wettability
Bibliographic record
Abstract
Achieving the efficacious and rapid separation of mixed water/oil streams has emerged as a fundamental imperative in order to facilitate extraction of fossil fuels by methods such as cyclic steam stimulation as well as to mitigate the potentially calamitous impact of oil spills in natural aquatic environments. Here, we demonstrate functionalized ZnO nanotetrapodal membranes combining the micrometer-scale texturation of underlying stainless steel meshes with the nanoscale texturation of an enmeshed interconnected porous network of ZnO tetrapods, the conformal adhesion afforded by an amorphous silica layer, and the low surface energy of surface-deposited perfluorinated sulfonate layers. The membranes exhibit pronounced differential wettability and selectively permeate water whilst retaining oil. The multivariate design space of the architectures has been evaluated to determine the mesh size and ZnO loading that yield the highest separation efficiencies. Oil content in recovered water is reduced to <300 ppm while maintaining a flux rate of greater than 325 L/(m 2 ·h). The functioning of the membranes can be understood in terms of the creation of differential Cassie–Baxter nonwetting and Wenzel wetting regimes for oil and water, respectively.
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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".