傅里叶变换红外光谱
电解质
化学
锂(药物)
分析化学(期刊)
感应耦合等离子体
离子色谱法
溶剂
离子
红外光谱学
红外线的
色谱法
电极
化学工程
等离子体
有机化学
物理化学
内分泌学
工程类
物理
光学
医学
量子力学
作者
Leah D. Ellis,Samuel Buteau,Samuel G. Hames,Lauren Thompson,David S. Hall,Jeff R. Dahn
摘要
A new method is introduced for determining unknown concentrations of major components in typical lithium-ion battery electrolytes. The method is quick, cheap, and accurate. Machine learning techniques are used to match features of the Fourier transform infrared (FTIR) spectrum of an unknown electrolyte to the same features of a database of FTIR spectra with known compositions. With this method, LiPF 6 concentrations can be determined with similar accuracy and precision as an inductively coupled plasma optical emission spectrometry (ICP-OES) method. The ratios of organic carbonate solvent species can be determined with more rapidity than gas chromatography (GC). This FTIR method is faster and less expensive than GC and ICP-OES, and has the added benefit of being able to determine LiPF 6 concentration and solvent fractions simultaneously. Application of this tool can facilitate electrolyte analysis of aged lithium-ion cells, and will help elucidate mechanisms for cell degradation.
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