Process Optimization and Temperature-Dependent Kinetic Modeling of Reactive Oxygen and Nitrogen Species in Plasma Activated Water during Storage

Abstract The practical utilization of plasma-activated water (PAW) depends not only on the efficient generation of reactive oxygen and nitrogen species (RONS) but also on their preservation during storage. This study integrates process optimization with long-term kinetic modeling to characterize the effects of plasma processing conditions on RONS generation and predict their evolution in PAW during storage. PAW was produced using a helium atmospheric-pressure plasma jet, and the effects of nozzle-to-water surface distance, helium flow rate, discharge voltage, treatment time, and water volume were investigated using a five-factor Box–Behnken experimental design. Plasma processing conditions significantly affected RONS concentrations, pH, and electrical conductivity. The optimized conditions (16 min treatment time, 20 mL water volume, 4.3 mm nozzle-to-water distance, 6.4 kV discharge voltage, and 0.5 SLM helium flow rate) predicted H₂O₂ and NO₃⁻ concentrations of 29.93 and 14.40 mg L⁻¹, respectively, and an electrical conductivity of 386.07 µS cm⁻¹. In contrast, maximum NO₂⁻ production was predicted under different operating conditions, demonstrating a species-specific response to the plasma processing parameters. The long-term stability of PAW was subsequently evaluated for 105 days over a broad storage-temperature range (− 80 to 25°C). RONS losses were strongly dependent on both storage time and temperature. At − 80°C, H₂O₂, NO₂⁻, and NO₃⁻ decreased by less than 10%, 11%, and 4%, respectively, during the first two weeks, whereas after more than 100 days the corresponding losses reached 48%, 31%, and 30%. A kinetic model describing the evolution of individual RONS during storage showed strong agreement between experimental and predicted concentrations. The derived kinetic parameters further demonstrated distinct temperature-dependent behavior under frozen and nonfrozen storage conditions. Overall, the integration of plasma-process optimization with temperature-dependent kinetic modeling provides a quantitative framework for controlling RONS generation and predicting their persistence in PAW during long-term storage.