Self-Assembled CMC/UiO-66-NH2 Membrane with Anti-Crude Oil Adhesion Property for Highly Efficient Oil-Water Separation

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.

One-Step Prepared Multifunctional Polyacrylonitrile/MIL-100(Fe) Membrane with High-Density Porous Fibers for Efficient Dye/Oil Wastewater Remediation

With environmental pollution becoming more serious, developing efficient treatment technologies for all kinds of organic wastewater has become the focus of current research. In this work, the coaxial electrospinning technology was used to one-step fabricate a porous and underwater superoleophobic polyacrylonitrile nanofibrous membrane with an Fe-based metal-organic framework (MIL-100(Fe)). Benefiting from the synergistic effect of two jets, the nanofibers are smaller and denser, which prompt the exposure of more nanomaterial additives (MIL-100(Fe)). The BET surface area increased to 202.888 m2/g, and the membranes demonstrated outstanding underwater superoleophobicity. Moreover, compared with traditional blended matrix membranes by the single-axis method, separation of the modifier and membrane matrix material by coaxial methods also maintained excellent mechanical properties, which enhanced Young's modulus 3.4 times (∼1.34 MPa). As a result, facing soluble dyes, the porous C-PAN/MIL-100(Fe) membrane can demonstrate outstanding and fast adsorptive property (the Qm of MB and CR reached 44.71 and 88.74 mg g-1, respectively). For oily emulsion, the hydrophilic and oleophobic nanofibrous reticular surface provided excellent separation performance (flux: 1124.0-1549.3 L m-2 h-1, R > 98%). Moreover, the porous and underwater superoleophobic C-PAN/MIL-100(Fe)-0.5 membrane can synchronously purify the dye/oil mixture emulsions by one-step filtration. Based on the above performance, we believe that the modified nanofibrous membrane prepared by one-step coaxial electrospinning technology can promote more studies of the development of membrane preparation technology in the field of oily wastewater treatment.

Sensitive determination of 4,6-dinitro-o-cresol based on a glassy carbon electrode modified with Zr-UiO-66 metal-organic framework entrapped FMWCNTs

A DNOC electrochemical sensor has been developed by using a composite of Zr-UiO-66 and FMWCNTs on a glassy carbon electrode (GCE) and using the differential pulse voltammetry technique. The synthesized materials were physico-chemically characterized by BET, PXRD, FTIR, TGA, EDX, and FESEM. Cyclic voltammetry showed that DNOC has three oxidation peaks at 0.03 V (RSD: 0.23%), 0.42 V (RSD: 0.21%), and 1.32 V (RSD: 0.32%) and three reduction peaks at - 0.20 V (RSD: 0.15%), - 0.82 V (RSD: 0.26%), and - 1.14 V (RSD: 0.19%) which follow a diffusion-controlled mechanism. Different parameters were optimized using differential pulse voltammetry and good linear ranges were found for the simultaneous detection of the three reduction peaks. For a specific concentration range of 0.1-50 μM, a limit of detection of 0.119 μM based on 3Sb/m was obtained. The interfering effects of five non-phenolic pesticides and five heavy metals were evaluated to highlight the selectivity of the developed sensor. It is the first report of an electrochemical DNOC sensor in which all three oxidation peaks are prominently visible. Ethion and chloropyriphos were found to inhibit the redox process of DNOC on the developed sensor platform Zr-UiO-66/FMWCNT/GCE. The sensor was successfully applied to DNOC determination in spiked potato samples and the results showed a standard deviation of less than 3%. The proposed method is expected to provide a novel platform for the quantitative determination of DNOC pesticides in vegetables.