High throughput crystal structure and composition mapping of crystalline nanoprecipitates in alloys by transmission Kikuchi diffraction and analytical electron microscopy

电子衍射 高分辨率透射电子显微镜 透射电子显微镜 材料科学 纳米晶材料 扫描透射电子显微镜 选区衍射 结晶学 反射高能电子衍射 电子背散射衍射 能量过滤透射电子显微镜 相(物质) 衍射 分析化学(期刊) 纳米技术 化学 光学 微观结构 冶金 物理 有机化学 色谱法
作者
Arunodaya Bhattacharya,Chad M. Parish,J. Henry,Yutai Katoh
出处
期刊:Ultramicroscopy [Elsevier BV]
卷期号:202: 33-43 被引量:18
标识
DOI:10.1016/j.ultramic.2019.03.015
摘要

Statistically significant crystal structure and composition identification of nanocrystalline features such as nanoparticles/nanoprecipitates in materials chemistry and alloy designing using electron microscopy remains a grand challenge. In this paper, we reveal that differing crystallographic phases of nanoprecipitates in alloys can be mapped with unprecedented statistics using transmission Kikuchi diffraction (TKD), on typical carbon-based electron-transparent samples. Using a case of multiphase, multicomponent nanoprecipitates extracted from an improved version of 9% chromium Eurofer-97 reduced-activation ferritic-martensitic steel we show that TKD successfully identified more than thousand M23C6, MX, M7C3, and M2X (M=Fe, Cr, W, V, Ta; X = C, N) nanoprecipitates in a single scan, something that is currently unachievable using a transmission electron microscope (TEM) without incorporating a precision electron diffraction (PED) system. Precipitates as small as ∼20-25 nm were successfully phase identified by TKD. We verified the TKD phase identification using high-resolution transmission electron microscopy (HRTEM) and convergent beam electron diffraction (CBED) pattern analysis of a few precipitates that were identified by TKD on same sample. TKD study was combined with state-of-art analytical scanning transmission electron microscopy (STEM)-energy dispersive X-ray (EDX) spectroscopy and multivariate statistical analysis (MVSA) which provided the complete crystal structure and distinct chemistries of the precipitates in the steel in a high throughput automated way. This technique should be applicable to characterizing any multiphase crystalline nanoparticles or nanomaterials. The results highlight that combining phase identification by TKD with analytical STEM and modern data analytics may open new pathways in big data material characterization at nanoscale that may be highly beneficial for characterizing existing materials and in designing new materials.
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