Data Science Methods for Analyzing Nanomaterial Images and Videos
Abstract
A large amount of nanomaterial characterization data has been routinely collected by
using electron microscopes and stored in image or video formats. A bottleneck in making
effective use of the image/video data is the lack of the development of sophisticated
data science methods capable of unlocking valuable material pertinent information buried
in the raw data. To address this problem, the research of this dissertation begins with
understanding the physical mechanisms behind the concerned process to determine why
the generic methods fall short. Afterwards, it designs and improves image processing
and statistical modeling tools to address the practical challenges. Specifically, this dissertation
consists of two main tasks: extracting useful information from images or videos
of nanomaterials captured by electron microscopes, and designing analytical methods for
modeling/monitoring the dynamic growth of nanoparticles. In the first task, a two-pipeline
framework is proposed to fuse two kinds of image information for nanoscale object detection
that can accurately identify and measure nanoparticles in transmission electron
microscope (TEM) images of high noise and low contrast. To handle the second task of
analyzing nanoparticle growth, this dissertation develops dynamic nonparametric models
for time-varying probability density functions (PDFs) estimation. Unlike simple statistics,
a PDF contains fuller information about the nanoscale objects of interests. Characterizing
the dynamic changes of the PDF as the nanoparticles grow into different sizes and
morph into different shapes, the proposed nonparametric methods are capable of analyzing
an in situ TEM video to delineate growth stages in a retrospective analysis, or tracking
the nanoparticle growth process in a prospective analysis. The resulting analytic methods
have applications in areas beyond the nanoparticle growth process such as the image-based
process control tasks in additive manufacturing.
Subject
In situ TEM videonanoparticle growth process
change point detection
time-varying probability density functions estimation
Citation
Qian, Yanjun (2018). Data Science Methods for Analyzing Nanomaterial Images and Videos. Doctoral dissertation, Texas A & M University. Available electronically from https : / /hdl .handle .net /1969 .1 /173896.