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This study demonstrates the synthesis, characterization, and modeling of nickel ferrite nanocomposite, NiFe2O4 (NFC) as an adsorbent for the phenol contaminated aqueous environment. The characterization of the prepared NFC was performed with X-ray diffraction spectroscopy (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and vibrating sample magnetometer (VSM) techniques. The optimization and modeling of phenol removal using NFC was done through central composite design (CCD) and effective parameters of CCD were measured as input variables including the amount of NFC, pH, contact time and initial phenol concentration. The predicted results showed that the adsorption process using NFC as adsorbent had the maximum phenol removal (~99%) under predicted optimal conditions (pH = 7.67, NFC dosage = 0.15 g at room temperature), which also corresponded to the experimental values. In addition, a multilayer feed-forward artificial neural network (ANN) model was used to obtain a speculative phenol removal model. The network was trained for six replications after selection of the best neuron number for hidden layer. The value of MSE trained network was found to be 6.01718e-3 along with regression coefficient (R2 = 0.9934) that indicated satisfactory relationship. Isothermal modeling of phenol adsorption onto NFC was performed using well-known Temkin, Freundlich and Langmuir models and it was clear from the higher R2 value of 0.961 that the Langmuir model was significantly followed by experimental data. The maximum Langmuir adsorption capacity was found to be 274.72 mg/g at the optimal conditions. The obtained results prove that NFC could be an effective adsorbent for elimination of phenol contaminant from aqueous environment.

Nitrate ions (NO3−) enter water sources through sewage and can cause (in high concentrations) methemoglobinemia, cancer of the lymphatic and blood systems. Therefore, the level of NO3− in water must be controlled and effectively reduced. In the present work, copper oxide nanoparticles (CuO NPs) were synthesized by chemical deposition method and their physicochemical properties have been determined by SEM, FTIR, XRD techniques. CuO NPs have been used as adsorbents to remove nitrate ions from synthetic effluents. The optimization of nitrate ions removal was performed using response surface modeling (RSM) with parameters (input): initial nitrate concentration (20, 45, 70 mg/L), CuO NPs concentration (0.03, 0.06, 0.08 g/L), pH (3, 7, 11) and reaction time (40, 80, 120 min). The optimum conditions found were 70 mg/L as nanoparticles dose, 0.08 g/L as NO3− concentration, pH=3, and 120 min contact time. The analysis of variance for this model confirmed that the model was fitted successfully (R2=0.9987, R2adj=0.9974). The adsorption fitted to Langmuir isotherm (R2L=0.9822) and second-order kinetics (R2s2=0.9999).

Even though we live in an age of modern technology and innovation, infectious diseases such as Malaria are among the biggest health challenges worldwide. The main drawbacks of conventional Malaria disease chemotherapy are developing multidrug resistance and targeting non-specific parasites, resulting in high dose requirements and subsequently intolerable toxicity. To combat this trend, research in nanotechnology and nanomedicine has begun to develop new biochemicals capable of incorporating drugs, contributing to Malaria disease treatment through diagnosis, control, and targeted delivery. Nanotechnology has emerged as a powerful tool to develop safe, cheap, and highly effective antiMalaria drug therapies. Metal nanoparticles fabricated via green and eco-friendly approaches have shown potential against mosquito-borne diseases. The plant extracts and extract-mediated nanoparticles have demonstrated significant bio-efficacy to combat Malaria vectors and offer a promising strategy for treating Malaria disease. Also, nano-bio sensors potential for diagnosing Malaria disease has become greater than before since they can be affordable, sensitive, and reliable applications that could substitute the conventional detection techniques in the future. Biosensors have improved the scope for Malaria diagnosis with novel biorecognition and transducer components, which allow the construction of affordable biosensor platforms with improved characteristics. Herein, we present recent studies on the plant extract-mediated fabrication of green nanoparticles and their effectiveness in treating Malaria-infected patients.