Unveiling the effect of crystallinity and particle size of biogenic Ag/ZnO nanocomposites on the electrochemical sensing performance of carbaryl detection in agricultural products.

In this study, bio-Ag/ZnO NCs were synthesized via a microwave-assisted biogenic electrochemical method using mangosteen (Garcinia mangostana) peel extract as a biogenic reducing agent for the reduction of Zn2+ and Ag+ ions to form hybrid nanoparticles. The as-synthesized NC samples at three different microwave irradiation temperatures (Z 70, Z 80, Z 90) exhibited a remarkable difference in size and crystallinity that directly impacted their electrocatalytic behaviors as well as electrochemical sensing performance. The obtained results indicate that the Z 90 sample showed the highest electrochemical performance among the investigated samples, which is attributed to the improved particle size distribution and crystal microstructure that enhanced charge transfer and the electroactive surface area. Under the optimal conditions for carbaryl pesticide detection, the proposed nanosensor exhibited a high electrochemical sensitivity of up to 0.303 µA µM-1 cm-2 with a detection limit of LOD ∼0.27 µM for carbaryl pesticide detection in a linear range of 0.25-100 µM. Overall, the present work suggests that bio-Ag/ZnO NCs are a potential candidate for the development of a high-performance electrochemical-based non-enzymatic nanosensor with rapid monitoring, cost-effectiveness, and eco-friendly to detect carbaryl pesticide residues in agricultural products.

Applying response surface methodology to optimize partial nitrification in sequence batch reactor treating salinity wastewater.

In this study, the operation parameters of a partial nitrification process (PN) treating saline wastewater were optimized using the Box-Behnken design via the response surface methodology (BBD-RSM). A novel strategy based on the control of the carbon/nitrogen ratio (C/N), alkalinity/ammonia ratio (K/A), and salinity in three stages was used to achieve PN in a sequence batch reactor. The results demonstrated that a high and stable PN was completed after 50 d with an ammonia removal efficiency (ARE) of 98.37 % and nitrite accumulation rate (NAR) of 85.93 %. Next, BBD-RSM was applied, where ARE and NAR were the responses. The highest responses from the confirmation experiment were 99.9 % ± 0.04 and 95.25 % ± 0.32 when the optimum C/N, K/A, and salinity were identified as 0.84, 2, and 5.5 (g/L), respectively. The results were higher than those for the nonoptimized reactor. The developed regression model adequately forecasts the PN performance under optimal conditions. Therefore, this study provides a promising strategy for controlling the PN process and shows how the BBD-RSM model can improve the PN performance.