Rescaling the complex network of low-temperature plasma chemistry through graph-theoretical analysis

Abstract

We propose graph-theoretical analysis for extracting inherent information from complex plasma chemistry and devise a systematic way to rescale the network under the following key criteria: (1) maintain the scale-freeness and self-similarity in the network topology and (2) select the primary species considering its topological centrality. Network analysis of reaction sets clarifies that the scale-freeness emerging from a weak preferential mechanism reflects the uniqueness of plasma-induced chemistry. The effect of chemistry rescaling on the dynamics and chemistry of the He + O2 plasma is quantified through numerical simulations. The present chemical compression dramatically reduces the computational load, whereas the concentration profiles of reactive oxygen species (ROS) remain largely unchanged across a broad range of time, space and oxygen admixture fraction. The proposed analytical approach enables us to exploit the full potential of expansive chemical reaction data and would serve as a guideline for creating chemical reaction models.