Due to their production of diverse reactive chemical species, cold atmospheric plasmas have enormous application potential in biomedicine and other fields. But as plasma chemical models often contain dozens of species and hundreds or even thousands of reactions, complex analyses are typically required before producing desired results.
Liu et al. use graph theory, namely vital nodes identification, to simulate complex plasma phenomena and simplify key chemical reactions. The method, which has been applied for various purposes from controlling epidemic outbreaks and preventing power grid failures to discovering drug target candidates, identifies vital nodes and calculates their “importance.”
Using plasma consisting of a mixture of helium with air, which contains 59 species and 866 chemical reactions, the study authors describe a process that helps unravel the main chemical pathways.
“Our simplification process reduced the number of reactions in this representative, chemically complex plasma by a factor of [between] eight to 20 and provided simulation results within a deviation of less than two,” said Liu. “This suggests the method can capture the main chemical profile from a complex plasma while greatly reducing the computational load for simulation.”
While previous studies identified reactions via human experience, plasma chemistry is highly sensitive to discharge conditions, which vary dramatically with different plasmas. This research pioneers a method independent of human experience to simplify plasma chemistry, providing significant insight and visualization of complex network relationships, which is especially significant for the study of plasma biomedicine.
“Reactive species are the key agents in biomedical applications,” said Liu. “So, it is crucial to know the production mechanism of these species, and consequently control the discharge parameters to optimize their production.”
Source: “Simplification of plasma chemistry by means of vital nodes identification,” by Bowen Sun, Dingxin Liu, Yifan Liu, Santu Luo, Mingyan Zhang, Jishen Zhang, Aijun Yang, Xiaohua Wang, and Mingzhe Rong, Journal of Applied Physics (2021). The article can be accessed at https://doi.org/10.1063/5.0063068.
