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.

我们提出了图论分析方法，以从复杂的等离子体化学中提取固有信息，并设计了一种系统的方法来根据以下关键标准对网络进行重新缩放：（1）在网络拓扑中保持无标度和自相似性，并且（2）选择考虑到其拓扑中心性的主要物种。对反应集的网络分析表明，弱优先机制产生的无垢反应反映了等离子体诱导化学的独特性。化学结垢对He + O ₂动力学和化学性质的影响通过数值模拟对血浆进行量化。当前的化学压缩显着降低了计算负荷，而活性氧（ROS）的浓度曲线在很宽的时间，空间和氧混合分数范围内仍保持很大的不变。所提出的分析方法使我们能够充分利用广阔的化学反应数据的潜力，并将作为创建化学反应模型的指南。