Designed water channels and sieving effect for heavy metal removal by a novel silica-poly(ionic liquid) nanoparticles TFN membrane

Metal Oxide Nanoparticles Synthesized in Ionic Liquids: Characterization and Photodegradation of Methyl Orange

Dye residues from the textile industry significantly contribute to water pollution, necessitating effective wastewater treatment methods. This study reports the successful synthesis of zinc oxide (ZnO) nanoparticles using various ionic liquids (ILs), [BMIM]-BF4, [BMIM]-PF6, and [BMIM]-Cl, as mediators. The synthesized nanomaterials were characterized using various techniques, including X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and photoluminescence (PL) spectroscopy. Their photocatalytic activity in degrading methyl orange (MO) dye under UV-vis and sunlight irradiation was investigated. The results demonstrated that ILs significantly influenced the structural and optical properties of ZnO, resulting in smaller crystallite sizes, modified morphologies, and reduced band gap energies compared to unmodified ZnO. The ZnO-[BMIM]-BF4 (1%) exhibited superior photocatalytic efficiency, achieving complete MO degradation within 30 min under UV-vis irradiation, attributed to its enhanced light absorption and reduced electron-hole recombination. The ZnO-BMIM-PF6 (1%) demonstrated exceptional stability, maintaining high degradation efficiency over multiple cycles. These findings highlight the potential of IL-mediated synthesis in tailoring ZnO nanomaterials for efficient photocatalytic degradation of organic pollutants, offering a promising approach for wastewater treatment.

Experiments and machine learning-based modeling for haloacetic acids rejection by nanofiltration: Influence of solute properties and operating conditions

Because of potential risks to public health, the presence of haloacetic acids (HAAs) in drinking water is a major concern. Nanofiltration (NF) has shown potential for HAAs rejection, and several factors, namely, membrane properties, solute properties, and operating conditions, have been revealed key roles. However, knowledge of NF separation mechanism by quantifying these factors is limited. This study investigated and modeled NF performance on HAAs rejection. NF performance was experimentally investigated under various transmembrane pressure (TMP), cross-flow velocity (CV), temperature, pH, ionic strength (IS), and HAAs initial feed concentration (C_in). We used machine learning (ML) to understand the mechanism from the perspective of HAAs properties and operating conditions. Multiple linear regression (MLR), support vector machine (SVM), multsilayer perceptron (MLP), extreme gradient boosting (XGBoost), and random forest (RF) models were used. The MLP, XGBoost and RF models achieved significant performance with high R² (0.970, 0.973, and 0.980) and low RMSE (4.71, 4.41, and 3.84). These three models were analyzed using the Shapley Additive explanation (SHAP) to quantify relative contributions of HAAs properties and operating conditions. XGBoost-SHAP produced the most logical results and was the best-performing model for selecting optimal input variables combinations. The results showed that Stokes radius (r_s), logarithmic octanol-water partitioning coefficient (logK_ow), molecular weight (MW), pH, TMP, and temperature are key variables for interpreting NF process. The effects of HAAs properties were ranked as r_s > logK_ow > MW, suggesting significance of size exclusion and hydrophobic interaction. The impact of the operational conditions followed the order pH > TMP > temperature, illustrating that pH was the major influencing operating condition. This study demonstrated significant capacity of ML, which reduced amount of experimental work. In addition, the main operating conditions can be evaluated in terms of their contributions, making ML an efficient tool for risk management and process optimization.