Castro Fernando C, Dravid Vinayak P
1Department of Materials Science and Engineering,Northwestern University,2220 Campus Drive, Cook Hall, Room 1137, Evanston,IL 60208,USA.
Microsc Microanal. 2018 Jun;24(3):214-220. doi: 10.1017/S1431927618000302. Epub 2018 Jun 7.
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.
锂离子电池材料的前沿研究经常聚焦于纳米尺度和原子尺度的现象。电子能量损失谱(EELS)是表征电池材料组成和电子结构方面,特别是电极中锂和过渡金属混合氧化物的最有力方法之一。然而,当光谱特征强烈重叠、信背比不佳或样品区域较厚且不均匀时,来自电池材料的特征EELS信号很难分析。一种潜在的替代或补充方法是利用电池材料的价带EELS特征(损失小于20 eV)。例如,LiCoO₂中的价带EELS特征比Li-K边保持更高的跃变比,尤其是在以最短采集时间或从厚样品区域采集光谱时。这些价带特征的EELS图谱与LiCoO₂的Li-K边EELS图谱给出了可比的结果。经过一些光谱处理后,价带EELS图谱比Li-K边图谱更准确地突出了LiCoO₂的形态和分布,特别是在较厚的样品区域。这种方法对于样品厚度或束流灵敏度限制EELS分析的情况很有帮助,并且可用于最小化电子剂量以及样品损伤或污染。