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.

Review: Machine learning for advancing low-temperature plasma modeling and simulation

“…Mizui et al 86 extended the analysis in Ref. 85 to transient states by constructing temporal graphs. Their classification method was used to deduce information from silane and methane plasma chemistries.…”

Modeling study on temporal nonlinear behaviors in CO2 dielectric barrier discharges under Martian pressure

“…The CO 2 plasma has a wide variety of particles and complex chemical reactions, to accurately investigate the underpinning physics of the nonlinear behaviors in CO 2 discharges under Martian conditions, the species and chemical reactions in CO 2 discharges are carefully selected according to the graph theory developed by Holmes et al [47] and Murakami and Sakai [48] (CO e , CO v , CO v , CO v , CO v , CO v , CO v ,…”

“…The $${\text{CO}}_{2}$$ plasma has a wide variety of particles and complex chemical reactions, to accurately investigate the underpinning physics of the nonlinear behaviors in $${\text{CO}}_{2}$$ discharges under Martian conditions, the species and chemical reactions in $${\text{CO}}_{2}$$ discharges are carefully selected according to the graph theory developed by Holmes et al [ 47 ] and Murakami and Sakai [ 48 ] , and a $${\text{CO}}_{2}$$ reaction model including 26 species and 278 reactions is obtained. Besides the background species $${\text{CO}}_{2}$$, five neutral particles (O, CO, $${{\rm{O}}}_{2},{{\rm{O}}}_{3}$$, and C), seven charged particles ( e , $${{\rm{O}}}^{-},{{\rm{O}}}_{2}^{-},{\text{CO}}_{3}^{-},{\text{CO}}_{4}^{-},{\text{CO}}_{2}^{+}$$, and $${{\rm{O}}}_{2}^{+}$$), and 13 excited states of $${\text{CO}}_{2}$$ (…”

2022 Review of Data-Driven Plasma Science

“…[661,662]. More recently, graph theory and machine learning are being used to extract information from complex chemistries [663,664]. While an ultimate solution to the problem of plasma-chemical reduction is not yet in sight, these developments bear great promise for the future of the field.…”