Abstract / Summary
Low-temperature plasma generates reactive species that play a crucial role in plasma medicine and industry. However, the mechanisms governing their penetration and transport within targets remain incompletely understood. This study investigated the penetration depth resulting from the accumulation of oxidizing species in agarose gel over a broad range of plasma operating parameters and introduced a data-driven framework for quantifying their transport using diffusion-based metrics. The penetration of oxidizing species was quantified using a potassium iodide–starch colorimetric assay in gels exposed to systematically varied applied voltages, pulse frequencies, irradiation times, and post-exposure times. A full factorial design of experiments was employed to generate a comprehensive dataset that was used to train an artificial neural network capable of predicting the penetration depth across the multidimensional plasma parameter space. The predictive model was subsequently used to derive diffusion-based transport metrics that characterized both irradiation and post-exposure species transport. The derived diffusion metrics revealed that species transport during plasma irradiation was substantially greater than post-exposure transport and increased with both frequency and voltage. The proposed methodology offers an alternative strategy for plasma diagnostics and the optimization of operating parameters across various applications.