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Characterization of Lithium Ion Battery Materials with Valence Electron Energy-Loss Spectroscopy

机译:锂离子电池材料的表征具有价损耗光谱法的锂离子电池材料

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摘要

Cutting-edge research on materials for lithium ion batteries regularly focuses on nanoscale and atomic-scale phenomena. Electron energy-loss spectroscopy (EELS) is one of the most powerful ways of characterizing composition and aspects of the electronic structure of battery materials, particularly lithium and the transition metal mixed oxides found in the electrodes. However, the characteristic EELS signal from battery materials is challenging to analyze when there is strong overlap of spectral features, poor signal-to-background ratios, or thicker and uneven sample areas. A potential alternative or complementary approach comes from utilizing the valence EELS features (<20 eV loss) of battery materials. For example, the valence EELS features in LiCoO2 maintain higher jump ratios than the Li–K edge, most notably when spectra are collected with minimal acquisition times or from thick sample regions. EELS maps of these valence features give comparable results to the Li–K edge EELS maps of LiCoO2. With some spectral processing, the valence EELS maps more accurately highlight the morphology and distribution of LiCoO2 than the Li–K edge maps, especially in thicker sample regions. This approach is beneficial for cases where sample thickness or beam sensitivity limit EELS analysis, and could be used to minimize electron dosage and sample damage or contamination.
机译:锂离子电池材料的尖端研究经常侧重于纳米级和原子级现象。电子能量损耗光谱(EEL)是表征电池材料电子结构的组成和方面的最强大方式之一,特别是锂电片和电极中的过渡金属混合氧化物。然而,当频谱特征较强重叠,信号到背景比率较差或更厚的样本区域时,来自电池材料的特征鳗鱼信号是具有挑战性的。潜在的替代或互补方法利用了电池材料的价鳗特征(<20EV损耗)。例如,LiCoO2中的价鳗特征在于Li-k边缘维持更高的跳跃比,最值得注意的是,当通过最小的获取时间或厚的样品区域收集光谱时。这些价值的鳗鱼地图为LiCoO2的Li-K边缘鳗鱼映射提供了可比的结果。通过一些光谱处理,该价鳗更加精确地突出了LiCoO2的形态和分布而不是Li-K边缘图,尤其是较厚的样本区域。这种方法有利于样品厚度或光束敏感性限制鳗鱼分析的情况,并且可用于最小化电子剂量和样品损坏或污染。

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