Design and Machine Learning Prediction of In Situ Grown PDA-Stabilized MOF (UiO-66-NH2) Membrane for Low-Pressure Separation of Emulsified Oily Wastewater.

Significant progress has been made in designing advanced membranes; however, persistent challenges remain due to their reduced permeation rates and a propensity for substantial fouling. These factors continue to pose significant barriers to the effective utilization of membranes in the separation of oil-in-water emulsions. Metal-organic frameworks (MOFs) are considered promising materials for such applications; however, they encounter three key challenges when applied to the separation of oil from water: (a) lack of water stability; (b) difficulty in producing defect-free membranes; and (c) unresolved issue of stabilizing the MOF separating layer on the ceramic membrane (CM) support. In this study, a defect-free hydrolytically stable zirconium-based MOF separating layer was formed through a two-step method: first, by in situ growth of UiO-66-NH2 MOF into the voids of polydopamine (PDA)-functionalized CM during the solvothermal process, and then by facilitating the self-assembly of UiO-66-NH2 with PDA using a pressurized dead-end assembly. A stable MOF separating layer was attained by enriching the ceramic support with amines and hydroxyl groups using PDA, which assisted in the assembly and stabilization of UiO-66-NH2. The PDA-s-UiO-66-NH2-CM membrane displayed air superhydrophilicity and underwater superoleophobicity, demonstrating its oil resistance and high antifouling behavior. The PDA-s-UiO-66-NH2-CM membrane has shown exceptionally high permeability and separation capacity for challenging oil-in-water emulsions. This is attributed to numerous nanochannels from the membrane and its high resistance to oil adhesion. The membranes showed excellent stability over 15 continuous test cycles, which indicates that the developed MOFs separating layers have a low tendency to be clogged by oil droplets during separation. Machine learning-based Gaussian process regression (GPR) models as nonparametric kernel-based probabilistic models were employed to predict the performance efficiency of the PDA-s-UiO-66-NH2-CM membrane in oil-in-water separation. The outcomes were compared with the support vector machine (SVM) and decision tree (DT) algorithm. This efficiency includes various metrics related to its separation accuracy, and the models were developed through feature engineering to identify and utilize the most significant factors affecting the membrane's performance. The results proved the reliability of GPR optimization with the highest prediction accuracy in the validation phase. The average percentage increase of the GPR model compared to the SVM and DT model was 6.11 and 42.94%, respectively.

Thermo-pH-Salt Environmental Terpolymers Influenced by 2-((Dimethylamino)methyl)-4-methylphenyl Acrylate: A Comparative Study for Tuning Phase Separation Temperature.

This study offers a comparison between three different types of thermoresponsive (TR) and thermo-pH-salt (TPR) multiresponsive polymers including homopoly(N-isopropylacrylamide) (PNIPAAm), copolymers with three different monomers, 2-hydroxyethyl methacrylate (HEMA), N,N-dimethylacrylamide (DMAAm), and styrene (S) at three different concentrations (5, 10, and 20 mol %), and a PNIPAAm terpolymer with 5, 10, and 20 mol % 2-((dimethylamino)methyl)-4-methylphenyl acrylate (DMAMCA) and 10 mol % HEMA, DMAAm, and S monomers. All polymers were chemically analyzed with 1H NMR and Fourier transform infrared spectroscopy (FT-IR) as well as gel permeation chromatography (GPC) for the molecular weights and dispersity and differential scanning calorimeter (DSC) for the glass transition temperatures. The cloud point, also known as the phase separation temperature (Cp), was determined for all polymers by a turbidity test using a UV-vis spectrophotometer; a micro-differential scanning calorimeter was used for measuring the cloud point in deionized water. The influence of a tertiary amine cationic group of DMAMCA changed the behavior of TR copolymers into TPR by shifting the cloud point of the TPR to higher values in acidic solutions (lower pH) and to lower values in alkaline solutions. The Cp was measured at different concentrations of Hofmeister kosmotropic and chaotropic anion salt solutions in a range of pH solutions for the terpolymers. It demonstrated the same behavior as mentioned in pH solutions besides the effect of salt ions. By measuring the Tc and Cp of these polymers, we can exploit various applications of stimuli-responsive materials for sensors and biomedical technology.

Highly Permeable Sulfonated Polydopamine Integrated MXene Membranes for Efficient Surfactant-Stabilized Oil-in-Water Separation.

MXene is an incredibly promising two-dimensional material with immense potential to serve as a high-performing separating or barrier layer to develop advanced membranes. Despite the significant progress made in MXene membranes, two major challenges still exist: (i) effectively stacking MXene nanosheets into defect-free membranes and (ii) the high fouling tendency of MXene-based membranes. To address these issues, we employed sulfonated polydopamine (SPD), which simultaneously serves as a binding agent to promote the compact assembling of Ti3C2Tx MXenes (MX) nanosheets and improves the antifouling properties of the resulting sulfonated polydopamine-functionalized MX (SPDMX) membranes. The SPDMX membrane was tested for challenging surfactant-stabilized oil-in-water separation with an impressive efficiency of 98%. Moreover, an ultrahigh permeability of 1620 LMH/bar was also achieved. The sulfonation of PD helps in improving the antifouling characteristics of SPDMX by developing a strong hydration layer and enhancing the oleophobicity of the membrane. The underwater SPDMX membrane appeared superoleophobic with an oil contact angle of 153°, whereas the ceramic membrane exhibited an oil contact angle of 137°. The SPDMX membranes showed an improved flux recovery (31%) compared to the nonsulfonated counterpart. This work highlights the appropriate functionalization of MXene as a promising approach to developing MXene membranes with high permeation flux and better antifouling characteristics for oily wastewater treatment.

Polyethersulfone Membranes Prepared with 3-Aminopropyltriethoxysilane Modified Alumina Nanoparticles for Cu(II) Removal from Water.

In this study, 3-aminopropyltriethoxysilane (APTES) modified γ-alumina nanoparticles were utilized to improve the copper removal efficiency of polyethersulfone (PES) membranes. Alumina nanoparticles were first modified by APTES silane coupling agent before impregnating into PES composite membranes. The PES membranes were fabricated by incorporating three different amounts of modified nanoparticles by a phase inversion process. The prepared membranes were characterized using field emission scanning electron microscopy, Fourier transform infrared, X-ray diffraction, thermogravimetric analysis (TGA), dynamic mechanical analysis, water contact angle, water flux, and porosity measurements. The Cu(II) removal and adsorption capacity of the membranes were also analyzed. The addition of nanoparticles increased the thermal stability, hydrophilicity, total porosity, Brunauer-Emmett-Teller surface area, and glass transition temperature of the membranes. TGA confirmed a suitable uptake of the nanoparticles during the membrane fabrication process. The water permeation of the membranes also increased significantly. Membranes synthesized with 4 wt % nanoparticles showed the highest rejection for copper ions of 87%. Adsorption isotherms were tested using Langmuir and Freundlich models, where the Freundlich isotherm model resulted in the best fitting.