Knowledge-based probabilistic representations of branching ratios in chemical networks: the case of dissociative recombinations.
Knowledge-based probabilistic representations of branching ratios in chemical networks: the case of dissociative recombinations.
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DOI：
10.1063/1.3479907
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发表时间：
2010-10
期刊：
影响因子：
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通讯作者：
S. Plessis;N. Carrasco;P. Pernot
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文献类型：
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作者：
S. Plessis;N. Carrasco;P. Pernot
关于多原子离子解离重组产物的支化比的实验数据目前是天然或实验室化学等离子体建模者可用的独特信息源。然而，由于测量技术的限制，许多离子的数据并不完整。特别是，氢原子在烃离子碎片之间的重新分配通常是不可行的。其结果是，在化学模型中正确实施解离重组过程很困难，并且许多模型忽略了宝贵的数据。我们提出了一种基于狄利克雷型分布的新颖概率方法，使建模者能够充分考虑可用信息。作为一个应用，我们考虑了土星最大卫星泰坦电离层化学模型中通过解离重组产生自由基的速率。与所有最新模型中默认实现的简单 H 损失机制相比，我们展示了用我们的方法导出的解离重组产物的完整方案如何显着影响这些速率。
Experimental data about branching ratios for the products of dissociative recombination of polyatomic ions are presently the unique information source available to modelers of natural or laboratory chemical plasmas. Yet, because of limitations in the measurement techniques, data for many ions are incomplete. In particular, the repartition of hydrogen atoms among the fragments of hydrocarbons ions is often not available. A consequence is that proper implementation of dissociative recombination processes in chemical models is difficult, and many models ignore invaluable data. We propose a novel probabilistic approach based on Dirichlet-type distributions, enabling modelers to fully account for the available information. As an application, we consider the production rate of radicals through dissociative recombination in an ionospheric chemistry model of Titan, the largest moon of Saturn. We show how the complete scheme of dissociative recombination products derived with our method dramatically affects these rates in comparison with the simplistic H-loss mechanism implemented by default in all recent models.