A macro-nano-atomic–scale high-throughput approach for material research
Yiwei Ju, Shuai Li, Xiaofei Yuan, Lei Cui, A. Godfrey, Yunjie Yan, Zhiying Cheng, Xiaoyan Zhong, Jing Zhu
Abstract
Understanding the properties of materials requires structural characterization over large areas and different scales to link microstructure with performance. Here, we demonstrate a single-beam high-throughput scanning electron microscope allowing the collection of both secondary electron and backscattered electron signals over large areas. Combined with machine learning, a high efficiency in material research is achieved, illustrated here by a multiscale investigation of carbides in a second-generation nickel-base single-crystal superalloy. The resulting terabyte-sized panoramic atlas data, combined with conventional electron microscopy, enable a simultaneous multiscale analysis of carbide evolution during creep regarding specific type, location, composition, size, shape, and relationship with the matrix, providing sample-scale quantitative statistical data and giving a precise insight into the effect of carbides in the superalloy in a way not previously possible.