Selective and Continuous Oil Removal from Oil–Water Mixtures Using a Superhydrophobic and Superoleophilic Stainless Steel Mesh Tube
Bibliographic record
Abstract
The development of continuous oil–water separation processes has applications in the treatment of industrial oily wastewater and effective management of oil spills. In this research, the performance of a superhydrophobic–superoleophilic (SHSO) membrane in oil–water separation is investigated through dynamic tests. We investigate the effects of the total flow rate and oil concentration on the separation efficiency using an as-fabricated SHSO mesh tube. To construct the SHSO membrane, a tubular stainless steel mesh is dip-coated into a solution, containing a long-chain alkyl silane (Dynasylan F8261) and functionalized silica nanoparticles (AEROSIL R812). The as-prepared SHSO mesh tube illustrates a water contact angle of 164° and an oil contact angle of zero for hexane. A maximum oil separation efficiency (SE) of 97% is obtained when the inlet oil–water mixture has the lowest flow rate (5 mL/min) with an oil concentration of 10 vol %, while the minimum oil SE (86%) is achieved for the scenario with the highest total flow rate (e.g., 15 mL/min) and the highest oil concentration (e.g., 50 vol %). The water SE of about 100% in the tests indicates that the water separation is not affected by the total flow rate and oil concentration, due to the superhydrophobic state of the fabricated mesh. The clear color of water and oil output streams also reveals the high SE of both phases in dynamic tests. The outlet oil flux increases from 314 to 790 (L/m 2 ·h) by increasing the oil permeate flow rate from 0.5 to 7.5 (mL/min). The linear behavior of the cumulative amounts of collected oil and water with time demonstrates the high separation performance of a single SHSO mesh, implying no pore blocking during dynamic tests. The significant oil SE (97%) of the fabricated SHSO membrane with robust chemical stability shows its promising potential for industrial-scale oil–water separation applications.
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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".