https://cnj.araku.ac.ir/ Colloid & Nanoscience Journal en daily 1 Thu, 01 Jan 2026 00:00:00 +0330 Thu, 01 Jan 2026 00:00:00 +0330 https://cnj.araku.ac.ir/article_733628.html In this study, architecture and training of two artificial neural networks (ANNs) were designed to predict the thermal conductivity (TC) and viscosity properties of nanofluids GO-SiO2 (50:50, Graphene Oxide and Silicon Dioxide)-based nanofluids. The nanofluid samples investigated comprised various weight fractions (0.1–1%) of a 50:50 mass-ratio mixture of graphene oxide (GO) and silicon dioxide (SiO₂) nanoparticles dispersed in the base fluid; their viscosity and thermal conductivity were measured at temperatures ranging from 30 to 60°C. For each ANN developed to predict either nanofluid viscosity or TC, the regression plots corresponding to the training, validation, and test datasets provided a clear demonstration of the network’s optimal performance. Specifically, the mean and maximum relative errors obtained for the test dataset were 0.3425% and 0.8359%, respectively, for viscosity prediction, and 0.2465% and 0.4069%, respectively, for TC prediction. Furthermore, following a sensitivity analysis of the networks, we found that the weight fraction exerts a more pronounced influence on nanofluid viscosity and TC than temperature. Subsequently, a genetic algorithm (GA) applied to the trained ANN models identified the optimal conditions as a weight fraction in the range of 0.1–1% and a temperature of 60°C. https://cnj.araku.ac.ir/article_734918.html AbstractPolyethersulfone (PES)-based nanofiltration membranes were fabricated by incorporating CuFe₂O₄/Ag composite nanoparticles (CNPs) to enhance permeability, hydrophilicity, and antifouling performance. The CuFe₂O₄/Ag CNPs was synthesized via an environmentally benign co-precipitation method using arginine as a natural reducing agent, and ATR-FTIR together with FESEM analyses confirmed its characteristic functional groups, uniform morphology, and homogeneous elemental distribution; similarly, ATR-FTIR and FESEM characterization of the CuFe₂O₄/Ag-incorporated membrane verified the successful embedding of the nanoparticles, the appearance of new functional interactions within the polymer matrix, and a uniformly modified surface morphology. Mixed matrix membranes were prepared by the phase inversion technique at various CNPs loadings (0.001–1.0 wt%), and their physicochemical properties were systematically evaluated. The addition of CuFe₂O₄/Ag CNPs significantly reduced the water contact angle from 70° to 45° and increased membrane porosity from 75% to 89%, improving surface hydrophilicity and pore interconnectivity. The optimized membrane (0.1 wt%) exhibited a 2.5-fold increase in pure-water flux (from 12 to 29.0 L·m⁻²·h⁻¹) and achieved 94% Na₂SO₄ rejection. Moreover, it demonstrated excellent heavy-metal ion removal efficiencies, with 93% and 81% rejection for Cu(II) and Cr(II), respectively. The flux recovery ratio (FRR) markedly improved from 61% for pristine PES to 88% for the modified membrane, indicating enhanced antifouling resistance. These improvements are attributed to the synergistic effects of the hydrophilic CuFe₂O₄/Ag CNPs, which enhanced charge selectivity, pore uniformity. Overall, the CuFe₂O₄/Ag–PES nanocomposite membranes exhibited superior water flux, ion selectivity, and fouling resistance, demonstrating strong potential for sustainable wastewater treatment and desalination applications.