Bhattacharya Arunodaya, Parish Chad M, Henry Jean, Katoh Yutai
Materials Science & Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA.
CEA, DEN-Service de Recherches Métallurgiques Appliquées, Laboratoire d'Analyse Microstructurale des Matériaux, Université Paris-Saclay F-91191, Gif-sur-Yvette, France.
Ultramicroscopy. 2019 Jul;202:33-43. doi: 10.1016/j.ultramic.2019.03.015. Epub 2019 Mar 28.
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 MC, MX, MC and MX (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.
在材料化学和合金设计中,利用电子显微镜对纳米晶体特征(如纳米颗粒/纳米析出物)进行具有统计学意义的晶体结构和成分鉴定仍然是一个巨大的挑战。在本文中,我们揭示了在典型的碳基电子透明样品上,使用透射菊池衍射(TKD)可以以前所未有的统计方式绘制合金中纳米析出物的不同晶体相。以从改进版的9%铬欧洲铁素体-97低活化铁素体-马氏体钢中提取的多相、多组分纳米析出物为例,我们表明TKD在单次扫描中成功识别了超过一千个MC、MX、MC和MX(M = Fe、Cr、W、V、Ta;X = C、N)纳米析出物,这是目前在不配备精密电子衍射(PED)系统的透射电子显微镜(TEM)下无法实现的。TKD成功地对小至约20 - 25纳米的析出物进行了相鉴定。我们使用高分辨率透射电子显微镜(HRTEM)和对同一样品上由TKD识别的一些析出物的会聚束电子衍射(CBED)图案分析,验证了TKD的相鉴定。TKD研究与先进的分析扫描透射电子显微镜(STEM)-能量色散X射线(EDX)光谱和多元统计分析(MVSA)相结合,以高通量自动化方式提供了钢中析出物的完整晶体结构和独特化学组成。该技术应适用于表征任何多相晶体纳米颗粒或纳米材料。结果突出表明,将TKD相鉴定与分析STEM和现代数据分析相结合,可能会在纳米尺度的大数据材料表征中开辟新途径,这对于表征现有材料和设计新材料可能非常有益。