Nanoscale zero-valent iron (nZVI) particles were investigated for the removal of methylene blue (MB) from aqueous solutions and the treatment of textile industry effluents. The nZVI material was characterized by XRD, TEM, EDS, FTIR, and SEM. It was demonstrated that several functional groups such as C–H, C = C, C–C, and C–O contributed to MB reduction. At initial MB concentration of 70 mg/L, the optimum pH was 6, achieving a removal efficiency of 72.1% using an nZVI dosage of 10 g/L, stirring rate of 150 rpm, and temperature of 30 °C within 30 min. The adsorption isotherm was described by the Langmuir model with monolayer coverage of 5.53 mg/g, and the Freundlich equation with multilayer adsorption capacity of 1.59 (mg/g)·(L/mg)1/n. The removal mechanisms of MB included reduction into colorless leuco-MB, precipitation as Fe(II)-MB, adsorption as ZVI-MB or FeOOH-MB, and/or degradation using •OH radicals. The synthesized nZVI particles were applied to reduce various organic and inorganic compounds, as well as heavy metal ions from real textile wastewater samples. The removal efficiencies of COD, BOD, TN, TP, Cu2+, Zn2+, and Pb2+ reached up to 91.9%, 87.5%, 65.2%, 78.1%, 100.0%, 29.6%, and 99.0%, respectively. The treatment cost of 1 m3 of textile wastewater was estimated as 1.66 $USD.

Water stress is considered one of the most environmental hazards that threaten agricultural productivity. Therefore, two field experiments were conducted to investigate the impact of biochar (6 t ha⁻¹ as soil amendment), ZnO NPs (50 mg L⁻¹ as foliar application), and their combination on growth, yield, and water use efficiency (WUE) of rice grown under four irrigation deficit treatments (i.e., irrigation every 3, 6, 9 and 12 d). The irrigation every 3 d was considered as the control in the current study. For this purpose, biochar was prepared through the pyrolysis of corn stalk and rice husk at 350 °C for 3 h, while sonochemical combined with the precipitation method was used to prepare zinc oxide nanoparticles (ZnO NPs) from zinc acetate. The morphological structures of the produced biochar and ZnO NPs were characterized using X-ray diffraction (XRD), N₂ gas adsorption-desorption, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The results exhibited that the combination of biochar alongside ZnO NPs resulted in a positive significant effect on the physiological traits such as chlorophyll content, relative water content, plant height, and leaf area index as well as yield-associated components (i.e., number of panicles m⁻², number of filled grain per panicle, 1000-grain weight), and biological and grain yield ha⁻¹ when rice plants were irrigated every 9 days without a significant difference with those obtained from the control treatment (irrigation every 3 d). In conclusion, the combination of biochar and ZnO NPs could be recommended as an optimal approach to maximize both grain yield ha⁻¹ and WUE of rice.

This study recommends Carbopol/zinc oxide (ZnO) hybrid nanoparticles gel as an efficient antibacterial agent against different bacterial species. To this end, ZnO nanoparticles were synthesized using chemical precipitation derived from a zinc acetate solution with ammonium hydroxide as its precipitating agent under the effect of ultrasonic radiation. The synthesized ZnO nanoparticles were stabilized simultaneously in a freshly prepared Carbopol gel at a pH of 7. The chemical composition, phase identification, particle size and shape, surface charge, pore size distribution, and the BET surface area of the ZnO nanoparticles, as well as the Carbopol/ZnO hybrid Nanoparticles gel, were by XRD, SEM, TEM, AFM, DLS, Zeta potential and BET instruments. The results revealed that the synthesized ZnO nanoparticles were well-dispersed in the Carbopol gel network, and have a wurtzite-crystalline phase of spherical shape. Moreover, the Carbopol/ZnO hybrid nanoparticles gel exhibited a particle size distribution between ~9 and ~93 nm, and a surface area of 54.26 m²/g. The synthesized Carbopol/ZnO hybrid nanoparticles gel underwent an antibacterial sensitivity test against gram-negative K. pneumonia (ATCC 13883), Bacillus subtilis (ATCC 6633), and gram-positive Staphylococcus aureus (ATCC 6538) bacterial strains, and were compared with ampicillin as a reference antibiotic agent. The obtained results demonstrated that the synthesized Carbopol/ZnO hybrid nanoparticles gel exhibited a compatible bioactivity against the different strains of bacteria.

Silica nanoparticles (SiO₂ NPs) are one of the most well-studied inorganic nanoparticles for many applications. They offer the advantages of tunable size, biocompatibility, porous structure, and larger surface area. Thus, in this study, a high yield of SiO₂ NPs was produced via the chemical treatment of rice husk ash by the sol–gel method. Characteristics of the prepared SiO₂ NPs were validated using different characterization techniques. Accordingly, the phase, chemical composition, morphological, and spectroscopic properties of the prepared sample were studied. The average particle size of the SiO₂ NPs was found to be approximately 60–80 nm and the surface area was 78.52 m²/g. The prepared SiO₂ NPs were examined as photocatalysts for the degradation of methyl orange (MO) dye under UV irradiation. It was found that the intensity of the characteristic absorption band of MO decreased gradually with exposure time increasing, which means the successful photodegradation of MO by SiO₂ NPs. Moreover, the antibacterial activity of obtained SiO₂ NPs was investigated by counting the coliform bacteria in the surface water using the most probable number (MPN) index method. The results revealed that the MPN of coliform bacteria untreated and treated by SiO₂ NPs was estimated to be 170 CFU/100 mL and 10 CFU/100 mL, respectively, resulting in bacterial growth inhibition of 94.12%.

Predicting the heavy metals adsorption performance from contaminated water is a major environment-associated topic, demanding information on different machine learning and artificial intelligence techniques. In this research, nano zero-valent aluminum (nZVAl) was tested to eliminate Cu(II) ions from aqueous solutions, modeling and predicting the Cu(II) removal efficiency (R%) using the adsorption factors. The prepared nZVAl was characterized for elemental composition and surface morphology and texture. It was depicted that, at an initial Cu(II) level (C_o) 50 mg/L, nZVAl dose 1.0 g/L, pH 5, mixing speed 150 rpm, and 30 °C, the R% was 53.2 ± 2.4% within 10 min. The adsorption data were well defined by the Langmuir isotherm model (R²: 0.925) and pseudo-second-order (PSO) kinetic model (R²: 0.9957). The best modeling technique used to predict R% was artificial neural network (ANN), followed by support vector regression (SVR) and linear regression (LR). The high accuracy of ANN, with MSE < 10⁻⁵, suggested its applicability to maximize the nZVAl performance for removing Cu(II) from contaminated water at large scale and under different operational conditions.