在锂离子电池清洁度检测需求的驱动下，基于SEM-EDS 功能、用于锂离子电池清洁度检测的自动分析工具—— AZtecBattery 应运而生。

清洁能源技术是世界各国关注的热点，主要的发达国家都在大力推动电动汽车的发展，以期取代燃油车。电动汽车的发展离不开动力电池技术的进步，正极材料是动力电池的研究重点。寻找合适的成分体系一直是产业界和学术界的重要研究课题，相关的技术沿着降钴增镍、提高容量、稳定性和循环性能的方向发展。另外，有研究表明， 多晶正极材料的稳定性及循环性能不如单晶正极材料 [1]。国内多家企业将单晶正极材料作为自己的拳头产品，但多晶正极材料成本较低，仍然属于市场主流产品。优化结构以提高性能是多晶正极材料研究的重要方向。

电解液是锂离子电池的重要构成要素，通常包括了有机溶剂、锂盐电解质和添加剂（图1）。电解液的作用从本质上讲是为锂离子提供自由脱嵌的环境，实现电池正负极之间的电流传导。由于有机溶剂、电解质和添加剂种类繁多，不同组成和配比的电解液在热耐受性、化学稳定性、离子电导率和电极相容性等方面可能存在显著差异，极大影响了电池的性能、寿命、安全性以及适用范围。因此，准确全面地表征电解液，了解并控制其作用特性，是锂电池理论研究和应用开发不可或缺的重要环节。

Summary Impurities and contaminants in the material used in the production of Li-ion batteries can have catastrophic impacts on the finished products. As such, monitoring of the quality and cleanliness of materials throughout the production process is essential if contaminants are to be found and their sources controlled. This monitoring must start with the raw materials produced at the mine and continue through to the final battery grade powders. This application note demonstrates a scanning electron microscopy (SEM) based solution for automatic detection and identification of impurities in battery raw material powders. By taking samples at different steps of the production process it is possible to identify where contaminants are introduced; thereby allowing sources to be identified and solutions developed.

Lithium Ion batteries are found in most mobile electronic devices (e.g. laptop computers, phones etc).

An important part of the research and development of thin-film solar cells is the characterisation of microstructural and compositional properties of the functional layers. For this purpose, energy-dispersive X-ray spectrometry (EDS) and electron backscatter diffraction (EBSD) represent techniques which exhibit spatial resolutions on the nanometer scale but can be, at the same time, applied on large areas of several square millimeters. The application of EDS and EBSD is demonstrated on this example of thin-film solar cells with Cu(In,Ga)Se2 absorber layers. While EDS provides elemental distributions even in layers with a nominal thickness of 30-50 nm, EBSD gives not only information of average grain sizes, local orientations and grain boundaries. Moreover, strain distributions within individual grains can be calculated by the evaluation of EBSD patterns recorded on individual grains.