Identifying multi-stage nanocrystal growth using in situ TEM video data

ABSTRACT

The in situ transmission electron microscopy technique is receiving considerable attention in material science research, as its in situ nature makes possible discoveries that ex situ instruments are unable to make and provides the capability of directly observing nanocrystal growth processes. As incresing amounts of dynamic transmission electron microscopy (TEM) video data become available, one of the bottlenecks appears to be the lack of automated, quantitative, and dynamic analytic tools that can process the video data efficiently. The current processing is largely manual in nature and thus laborious, with existing tools focusing primarily on static TEM images. The absence of automated processing of TEM videos does not come as a surprise, as the growth of nanocrystals is highly stochastic and goes through multiple stages. We introduce a method in this article that is suitable for analyzing the in situ TEM videos in an automated and effective way. The method learns and tracks the normalized particle size distribution and identifies the phase-change points delineating the stages in nanocrystal growth. Using the outcome of the change-point detection process, we propose a hybrid multi-stage growth model and test it on an in situ TEM video, made available in 2009 by Science.

KEYWORDS:

• Change-point detection
• dynamic TEM data
• hybrid particle growth model
• in-situ TEM
• normalized particle size distribution
• time-varying probability density function

Additional information

Notes on contributors

Yanjun Qian

Yanjun Qian received a B.S. (2009) and M.S. (2012) in Automation from Tsinghua University, China. He is currently a Ph.D. candidate in Industrial & Systems Engineering at Texas A&M University. His research interests are in system informatics and quality and reliability engineering, image and video processing, and machine learning. He is a student member of INFORMS.

Jianhua Z. Huang

Jianhua Z. Huang received a B.S. in Probability and Statistics from Beijing University of China (1989); an M.S. in Probability and Statistics from Beijing University of China (1992); and a Ph.D. in Statistics from University of California at Berkeley (1997). He is currently a Professor of Statistics at Texas A&M University. His research interests are in statistical machine learning and applied statistics. He is a fellow of ASA and IMS and an elected member of ISI.

Yu Ding

Yu Ding received a B.S. in Precision Engineering from the University of Science & Technology of China (1993); an M.S. in Precision Instruments from Tsinghua University, China (1996); an M.S. in Mechanical Engineering from Penn State University (1998); and a Ph.D. in Mechanical Engineering from the University of Michigan (2001). He is currently the Mike and Sugar Barnes Professor of Industrial & Systems Engineering and Professor of Electrical & Computer Engineering at Texas A&M University. His research interests are in system informatics and quality and reliability engineering. He is a fellow of IISE, a fellow of ASME, a senior member of IEEE, and a member of INFORMS.