Abstract
The morphology of nanoparticles governs their properties for a range of important applications. Thus, the ability to statistically correlate this key particle performance parameter is paramount in achieving accurate control of nanoparticle properties. Among several effective techniques for morphological characterization of nanoparticles, transmission electron microscopy (TEM) can provide a direct, accurate characterization of the details of nanoparticle structures and morphology at atomic resolution. However, manually analyzing a large number of TEM images is laborious. In this work, we demonstrate an efficient, robust and highly automated unsupervised machine learning method for the metrology of nanoparticle systems based on TEM images. Our method not only can achieve statistically significant analysis, but it is also robust against variable image quality, imaging modalities, and particle dispersions. The ability to efficiently gain statistically significant particle metrology is critical in advancing precise particle synthesis and accurate property control.
Highlights
Nanoparticles are widely used in applications such as bio-medicine [1,2,3,4], sensors [5,6], nanoscale thermal metrology [7], energy [8], environmental protection [9], optics [10,11,12,13]and electronics [14,15]
Examples include the enhancement of the catalytic activities of nanoparticles by controlling their shape [16,17]; the particle size- and shape-dependent drug molecule absorption in drug delivery [18]; the strong size/shape correlation with their optical properties [19,20]; and control of the size and dispersion of nanoparticles to optimize their photoacoustic effect in the field of bioimaging [21,22,23]
The nanoparticle transmission electron microscopy (TEM) samples were imaged using JEOL F200 (Akishima, Tokyo, Japan), operated at 200 kV, using either the bright-field TEM (BF-TEM) mode or the annular-dark field scanning-TEM (ADF-STEM) mode
Summary
Nanoparticles are widely used in applications such as bio-medicine [1,2,3,4], sensors [5,6], nanoscale thermal metrology [7], energy [8], environmental protection [9], optics [10,11,12,13]. Published studies are showing that unsupervised ML methods can efficiently analyze the size and shape information of the nanoparticles; they are limited to nanoparticles with well distinguished shapes with strong image contrast (such as gold nano-rods) and good dispersions (isolated particles) [39,40]. The method first uses computer vision algorithms to pre-process images and obtain particle morphology information, followed by a hierarchical-clustering based unsupervised ML algorithm to classify particle shapes and output statistical particle morphology analysis results This method is highly automated and it is applicable to convex nanoparticle shapes (this limitation will become clear in Section 2.4), dispersion density and particle composition (which determines the image contrast). This method fills a gap in the particle shape classification in the common large-number particle analysis software
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