Rescaling the complex network of low-temperature plasma chemistry through graph-theoretical analysis
Rescaling the complex network of low-temperature plasma chemistry through graph-theoretical analysis
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DOI：
10.1088/1361-6595/abbdca
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发表时间：
2020-12
影响因子：
3.8
通讯作者：
T. Murakami;O. Sakai
中科院分区：
文献类型：
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作者：
T. Murakami;O. Sakai
我们提出了从复杂等离子体化学中提取固有信息的图论分析，并设计了一种系统的方法来根据以下关键标准重新缩放网络：（1）保持网络拓扑结构的无标度性和自相似性;（2）选择考虑其拓扑中心性的主要物种。反应集的网络分析表明，从一个弱的优先机制出现的无标度反映了等离子体诱导化学的独特性。通过数值模拟的化学重新缩放的影响的动力学和化学的He + O2等离子体的量化。目前的化学压缩显着降低了计算负荷，而活性氧（ROS）的浓度分布在很大程度上保持不变，在很宽的范围内的时间，空间和氧混合分数。所提出的分析方法使我们能够利用膨胀的化学反应数据的全部潜力，并将作为创建化学反应模型的指导方针。
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.