Fabrication of superhydrophobic-superoleophilic membranes for oil-water separation applications
Bibliographic record
Abstract
The increasing volume of industrial oily wastewater and recent oil spill incidents have negatively affected the ecosystem and human health. Accordingly, it has been a worldwide challenge to separate the water or oil from such oily wastewater effectively. Recently, owing to the low efficiency of conventional techniques, a great interest has been paid in using membranes with engineered wettability especially superhydrophobic-superoleophilic (SHSO) ones, for oil-water separation applications. In this research, the SHSO mesh is fabricated to examine the effectiveness of membrane surface modification for oil-water separation purposes. After cleaning and activating the stainless-steel mesh by piranha solution, two different silanes with short- and long-alkyl functional chains (Dynasylan® Sivo 408 and Dynasylan® F8261, respectively) are used to modify the mesh surface via a dip-coating technique. Functionalized silica micro and nanoparticles with different ratios are tried to evaluate their potential as morphology modifiers. The superhydrophobic and superoleophilic membranes are then characterized by the contact angle, sliding angle, scanning electron microscopy (SEM), energy dispersive X-ray (EDX), Fourier transform infrared spectroscopy (FTIR), Transmission Electron Microscopy (TEM), and X-ray Powder Diffraction (XRD) techniques. Utilization of Dynasylan® F8261 as a coating solution leads to a higher water contact angle (WCA) due to having a longer alkyl functional chain, compared to Dynasylan® Sivo 408. Moreover, the combination of micro (25%) and nano (75%) particles results in the highest WCA (165.8˚), followed by the scenario of only nanoparticles with a WCA of 163.8˚. The coating solution with only nanoparticles is thus proposed as the optimal case, since the microparticles tend to settle, making the solution non-homogeneous based on the SEM results. Analysis of characterization tests confirms that the as-prepared mesh exhibits SHSO properties. The stability analysis is also conducted by submerging the SHSO membranes into solutions of NaCl (1M), H₂SO₄ (0.1M), and NaOH (0.1M). Except for the NaOH solution, this mesh maintains its SHSO properties in the solutions of NaCl and H₂SO₄ over one-month stability assessment. The results of static oil-water separation show a higher separation efficiency for hexane (99%) than canola oil (97%), owing to the lower viscosity of hexane. The dynamic oil-water separation tests are also performed using coated mesh tubes in a cross-flow gravity-based separator. The oil-water mixture is pumped into the tube side for 70 minutes. The oil-water mixture level is adjusted to avoid the breakthrough of the water phase into the SHSO membrane. The effluents of oil and water from the system are directed to the secondary separators to analyze the oil and water separation efficiency. Different oil concentrations (10, 30, and 50 vol%) and total flow rates (5, 10, and 15 mL/min) are examined to evaluate the performance of the SHSO mesh in separating oil from the oil-water mixture. The maximum oil separation efficiency of 97% is obtained from a scenario with 10 vol% oil and 5 mL/min total flow rate. Conversely, the minimum oil separation efficiency (86%) occurs for the case with a 15 mL/min total flow rate and 50 vol% oil. The water separation efficiency is not affected by changing oil-water mixture characteristics, as it reached the maximum level (100%) shortly. The flower-like silica nano-roughness on the SHSO mesh tube by decreasing the pore size from around 80 to 45 μm effectively prohibits the water phase from entering the mesh pores up to 3 cm H₂O column. By increasing the oil permeate flow rates from 0.5 to 7.5 ml/min, the oil permeation flux increases from 314 to 790 (L/m².h). By the time, the production rate of oil and water shows a linear behaviour indicating that the SHSO coated mesh does not experience the fouling phenomenon. Both separated phases are very clear. Therefore, the proposed methodology can have practical applications in oil removal from oily wastewater and oil spill incidents.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".