A multistage, semi-automated procedure for analyzing the morphology of nanoparticles

Abstract

This paper presents a multi-stage, semi-automated procedure that can expedite the morphology analysis of nanoparticles. Material scientists have long conjectured that morphology of nanoparticles has a profound impact on the properties of the hosting material but a bottleneck is the lack of a reliable and automated morphology analysis of the particles based on their image measurements. This paper attempts to ll in this critical void. One particular challenge in nano morphology analysis is how to analyze the overlapped nanoparticles, a problem not well addressed by the existing methods but eectively tackled by our proposed method. Our proposed method entails multiple stages of operations, executed sequentially, and is considered semi-automated due to the inclusion of a semi-supervised clustering step. Our method was applied to several images of gold nanoparticles, producing the needed statistical characterization of their morphology.

A complex network approach for nanoparticle agglomeration analysis in nanoscale images

“…Notice that the curve could be smoother had we used more images; this is because with a larger number of values (one per image), the expected average value would dominate the plot, avoiding spikes. However, that would demand a very large number of images, which were not available and which is not usual in the corresponding literature [15,30,31,27].…”

“…In the work of Park et al [30], the authors propose a semiautomatic method to perform shape analysis over the particles. In the work of Park et al [30], the authors used six images of Transmission Electron Microscopy (TEM) to characterize the shape of gold nanoparticles by representing boundary corners into a parametric curve. Although the authors created a rotation invariant approach, the reconstruction depends on the corners that the algorithm detects.…”

Segmentation of Fe<inf>3</inf>O<inf>4</inf> nano particles in TEM images

“…Bu sistem sınırlı sayıda GEM görüntüsünde test edilmiştir ve kullanılan model karmaşıktır. [5] aşamalı bir şekildeönce nanoparçacık imgelerindeki kenarları bulmuş daha sonra dogrusal olmayan izdüşüm yöntemi ile sınırlardanöznitelikler çıkartıp yarı-otomatik bir şekilde bunlarıönceden tanımlı sınıflara ayırmıştır. Başka bir çalışmada ise nihai erozyon metodu ile birleşik parçacıklar ayrıldıktan sonra referans şekillere göre bir kontur geliştirilerek nanoparçacıklar bulunmuştur [6].…”

Texture analysis using complex system models: fractal dimension, swarm systems and non-linear diffusion

“…Fisker et al (FISKER et al, 2000) developed an automatic method to estimate the particle size distribution based on a deformable ellipse model applied to ferromagnetic (aFe 1− x -C x ) and hematite (a -Fe 2 O 3 ) nanoparticles. In the work of Park et al (PARK et al, 2012), the authors propose a semi-automatic method to shape analysis of particles. In the work (PARK et al, 2012), Park et al used six images of Transmission Electron Microscopy (TEM) to characterize the shape of gold nanoparticles by representing boundary corners into a parametric curve.…”

“…In the work of Park et al (PARK et al, 2012), the authors propose a semi-automatic method to shape analysis of particles. In the work (PARK et al, 2012), Park et al used six images of Transmission Electron Microscopy (TEM) to characterize the shape of gold nanoparticles by representing boundary corners into a parametric curve. Although the authors created a rotation invariant approach, the reconstruction depends on the corners that the algorithm detects.…”