Open AccessRescaling the complex network of low-temperature plasma chemistry through graph-theoretical analysis
Author(s) -
Tomoyuki Murakami,
Osamu Sakai
Publication year - 2020
Publication title -
plasma sources science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.9
H-Index - 108
eISSN - 1361-6595
pISSN - 0963-0252
DOI - 10.1088/1361-6595/abbdca
Subject(s) - uniqueness , expansive , graph , centrality , computer science , network analysis , chemical similarity , chemistry , chemical reaction , plasma , statistical physics , chemical physics , theoretical computer science , mathematics , physics , thermodynamics , combinatorics , structural similarity , artificial intelligence , mathematical analysis , biochemistry , compressive strength , quantum mechanics
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 + O 2 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.