Out-of-equilibrium electrochemical reaction mechanisms are notoriously difficult to characterize. However, such reactions are critical for a range of technological applications. For instance, in metal-ion batteries, spontaneous electrolyte degradation controls electrode passivation and battery cycle life. Here, to improve our ability to elucidate electrochemical reactivity, we for the first time combine computational chemical reaction network (CRN) analysis based on density functional theory (DFT) and differential electrochemical mass spectroscopy (DEMS) to study gas evolution from a model Mg-ion battery electrolyte─magnesium bistriflimide (Mg(TFSI)₂) dissolved in diglyme (G2). Automated CRN analysis allows for the facile interpretation of DEMS data, revealing H₂O, C₂H₄, and CH₃OH as major products of G2 decomposition. These findings are further explained by identifying elementary mechanisms using DFT. While TFSI^– is reactive at Mg electrodes, we find that it does not meaningfully contribute to gas evolution. The combined theoretical–experimental approach developed here provides a means to effectively predict electrolyte decomposition products and pathways when initially unknown.

中文翻译：

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化学反应网络解释镁离子电池中的气体释放机制

众所周知，非平衡电化学反应机制难以表征。然而，此类反应对于一系列技术应用至关重要。例如，在金属离子电池中，自发的电解质降解控制电极钝化和电池循环寿命。在这里，为了提高我们阐明电化学反应性的能力，我们首次结合基于密度泛函理论 (DFT) 和微分电化学质谱 (DEMS) 的计算化学反应网络 (CRN) 分析来研究 Mg- 模型的气体演化。离子电池电解液─溶解在二甘醇二甲醚(G2)中的双三氟亚胺镁(Mg(TFSI) _{2 )。}自动 CRN 分析允许轻松解释 DEMS 数据，揭示 H ₂ O、C ₂H ₄和CH ₃ OH是G2分解的主要产物。这些发现通过使用 DFT 识别基本机制得到进一步解释。虽然 TFSI ^–在 Mg 电极上具有反应性，但我们发现它对气体逸出没有有意义的贡献。这里开发的理论-实验相结合的方法提供了一种在最初未知时有效预测电解质分解产物和途径的方法。