Reaction kinetic graphs of chain reactions: Solutions for their rate equations

Mondal, Sukanya; Mandal, Bholanath

doi:10.1016/j.cplett.2021.138977

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…A popular application is to model the synthesis of molecules [5,6]. A further application is to model chemical reactions using graphs (it is within this area that our own research lies), and there are many approaches to doing this, including modelling exclusively the chemical species as nodes [7][8][9][10]. Other approaches separate the nodes into two distinct categories: species and reaction [11][12][13][14] and using edges to connect reactants to a specific reaction node for any given reaction, and then connecting the product nodes to this reaction as demonstrated in Holmes et al [15], for application to plasma chemical reaction engineering.…”

Section: Condition Sequencing-the Staging Of Input Conditions For a D...mentioning

confidence: 99%

Node-of-Influence Network Analysis for Targeted Condition Sequencing in Plasma Chemical Reaction Networks

Holmes

Moody

Zimmerman

2023

Plasma Chem Plasma Process

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It was demonstrated that a plasma chemical reaction system can be represented as a directed bipartite variable-relationship (VR) graph to accurately represent node influence, and that targeted node-influence analysis of this graph can determine improved condition sequencing for a target outcome. A novel graph algorithm (OCARINA) was adapted for VR-graphs to give a measure of the net-influence of incremental increase (NIII) of one variable on another at any depth in the graph. Additionally, two conventional node-influence measures, the Eigenvector Centrality Index (ECI) and Katz Centrality Index (KCI), were also trialled on the VR-graph. The electron energy (ε) node influence was evaluated on a “baseline” continuous sequence of 10 ns 1 eV pulses in a 0D chemical-kinetic simulation using ECI, KCI and OCARINA NIII at three depths. KCI appeared to give meaningful values for ε influence in the whole graph but not on specific nodes, ECI gave no meaningful results. OCARINA (O3 targeted) suggested each successive ε pulse had diminishing influence on O3 formation, though analysis differed for each NIII depth. O3 concentrations in simulations with different numbers of ε pulses decreased with each additional pulse, correlating with the OCARINA analysis. O3, NO and O Species production in simulations of two ε pulses with one or both pulses incrementally changed by 10% from the baseline also largely agreed with the OCARINA results for each species on a baseline simulation of two consecutive 1 eV electron energy pulses. Additionally, it was found the NIII at antecedent depths corresponded to effects in subsequent phases in simulations.

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