Fabrication of superhydrophobic-superoleophilic membranes for oil-water separation applications
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».