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We report sol-gel process of Pb5Ge3O11 (PGO) as well as the microstructure and physical properties of ferroelectric PGO films for memory applications. The PGO sol was prepared from lead acetate hydrate, germanium isopropoxide, and di(ethylene glycol) ethyl ether. The reactions taking place during the sol-gel process were examined in detail, Diethanolamine (DEA) was added to help maintain the desired species ratio and prevent germanium oxide precipitation. The preferred orientation of the PGO thin films was well controlled by the heating and reflux procedures in the sol-gel preparation process. Additionally, to examine the impact of post-deposition processing, selected samples were oxygen annealed at temperatures ranging from 450-650°C. The samples were characterized with X-ray diffraction (XRD), non-contact (planview) atomic force microscopy (NC-AFM). The resulting data indicate that the microstructure and physical properties of PGO films depend strongly on the precursor preparation as well as the post deposition annealing temperature.
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Curve fitting of extended x-ray absorption fine structure (EXAFS) spectra, transmission electron microscopy (TEM) imaging, and Scherrer analysis of x-ray diffraction (XRD) are compared as methods for determining the mean crystallite size in polydisperse samples of platinum nanoparticles. By applying the techniques to mixtures of pure samples, it is found that EXAFS correctly determines the relative mean sizes of these polydisperse samples, while XRD tends to be weighted more toward the largest crystallites in the sample. Results for TEM are not clear cut, due to polycrystallinity and aggregation, but are consistent with the other results. © 2005 American Institute of Physics.
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Nanoparticles, particles with a diameter of 1-100 nanometers (nm), are of interest in many applications including device fabrication, quantum computing, and sensing because their size may give them properties that are very different from bulk materials. Further advancement of nanotechnology cannot be obtained without an increased understanding of nanoparticle properties such as size (diameter) and size distribution frequently evaluated using transmission electron microscopy (TEM). In the past, these parameters have been obtained from digitized TEM images by manually measuring and counting many of these nanoparticles, a task that is highly subjective and labor intensive. More recently, computer imaging particle analysis has emerged as an objective alternative by counting and measuring objects in a binary image. This paper will describe the procedures used to preprocess a set of gray scale TEM images so that they could be correctly thresholded into binary images. This allows for a more accurate assessment of the size and frequency (size distribution) of nanoparticles. Several preprocessing methods including pseudo flat field correction and rolling ball background correction were investigated with the rolling ball algorithm yielding the best results. Examples of particle analysis will be presented for different types of materials and different magnifications. In addition, a method based on the results of particle analysis for identifying and removing small noise particles will be discussed. This filtering technique is based on identifying the location of small particles in the binary image and removing them without affecting the size of other larger particles.
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Thresholding is an image processing procedure used to convert an image consisting of gray level pixels into a black and white binary image. One application of thresholding is particle analysis. Once foreground objects are separated from the background, a quantitative analysis that characterizes the number, size and shape of particles is obtained which can then be used to evaluate a series of nanoparticle samples. Numerous thresholding techniques exist differing primarily in how they deal with variations in noise, illumination and contrast. In this paper, several popular thresholding algorithms are qualitatively and quantitatively evaluated on transmission electron microscopy (TEM) and atomic force microscopy (AFM) images. Initially, six thresholding algorithms were investigated: Otsu, Riddler-Calvard, Kittler, Entropy, Tsai and Maximum Likelihood. The Riddler-Calvard algorithm was not included in the quantitative analysis because it did not produce acceptable qualitative results for the images in the series. Two quantitative measures were used to evaluate these algorithms. One is based on comparing object area the other on diameter before and after thresholding. For AFM images the Kittler algorithm yielded the best results followed by the Entropy and Maximum Likelihood techniques. The Tsai algorithm yielded the top results for TEM images followed by the Entropy and Kittler methods.
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Nanoparticles, particles with a diameter of 1-100 nanometers (nm), are of interest in many applications including device fabrication, quantum computing, and sensing because their decreased size may give rise to certain properties that are very different from those exhibited by bulk materials. Further advancement of nanotechnology cannot be realized without an increased understanding of nanoparticle properties such as size (diameter) and size distribution. Frequently, these parameters are evaluated using numerous imaging modalities including transmission electron microscopy (TEM) and atomic force microscopy (AFM). In the past, these parameters have been obtained from digitized images by manually measuring and counting many of these nanoparticles, a task that is highly subjective and labor intensive. Recently, computer imaging particle analysis routines that count and measure objects in a binary image1 have emerged as an objective and rapid alternative to manual techniques. In this paper a procedure is described that can be used to preprocess a set of gray scale images so that they are correctly thresholded into binary images prior to a particle analysis ultimately resulting in a more accurate assessment of the size and frequency (size distribution) of nanoparticles. Particle analysis was performed on two types of calibration samples imaged using AFM and TEM. Additionally, results of particle analysis can be used for identifying and removing small noise particles from the image. This filtering technique is based on identifying the location of small particles in the binary image, assessing their size, and removing them without affecting the size of other larger particles.
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