Comparison of particle size distributions of “monodisperse” particles from 0.8 to 3.5 μ in diameter using a Coulter Counter and electron microscopy

1968 ◽  
Vol 223 (2) ◽  
pp. 160-166 ◽  
Author(s):  
W. D. Cooper ◽  
G. D. Parfitt
Keyword(s):  
Electron Microscopy ◽  
Particle Size ◽  
Coulter Counter ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Monodisperse Particles

scholarly journals Particle size distributions by transmission electron microscopy: an interlaboratory comparison case study

Metrologia ◽  
2013 ◽  
Vol 50 (6) ◽  
pp. 663-678 ◽  
Author(s):  
Stephen B Rice ◽  
Christopher Chan ◽  
Scott C Brown ◽  
Peter Eschbach ◽  
Li Han ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Transmission Electron Microscopy ◽  
Particle Size ◽  
Interlaboratory Comparison ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Transmission Electron

“Heavy” Particle Dispersion Measurements With Mono- and Polydisperse Particle Size Distributions

1993 ◽  
Vol 115 (3) ◽  
pp. 523-526 ◽  
Author(s):  
J. R. Ferguson ◽  
D. E. Stock
Keyword(s):  
Particle Size ◽  
Standard Deviation ◽  
Size Distribution ◽  
Heavy Particle ◽  
Particle Dispersion ◽  
Turbulent Dispersion ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Monodisperse Particles ◽  
Polydisperse Particle

A method is presented to estimate the effects of a polydisperse particle size distribution on the measured turbulent dispersion of particles. In addition, the analysis provides a means to estimate the standard deviation of the size distribution for which a class of particles may be considered monodisperse. If monodisperse particles are unavailable because of practical considerations (e.g., the required standard deviation of particle size is too small to obtain a sufficient quantity) then the method provides a means to correct the data of near monodisperse size distributions to reflect the dispersion of monodisperse particles.

Agglomeration of High Calcium Fly Ash for Utilization II. Binding Mechanisms

1989 ◽  
Vol 178 ◽  
Author(s):  
Carol L. Kilgour ◽  
Kenneth L Bergeson ◽  
Scott Schlorholtz
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Particle Size ◽  
Power Plants ◽  
Layer Structure ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Fly Ashes ◽  
High Calcium ◽  
Scanning Electron

AbstractFly ashes from the Lansing and Ottumwa power plants in Iowa were agglomerated by means of a continuous pan agglomerator, a continuous auger and a batch turbine agglomerator. In order to compare agglomeration mechanisms the following parameters were determined: (a) particle size distributions of the untreated fly ashes; (b) particle size distributions of the agglomerated fly ashes; (c) pore size distribution of agglomerates; (d) crystalline hydration products by X-ray diffraction; and (e) morphological characterization by scanning electron microscopy.In the batch system coalescence mechanisms were favoured. The agglomerates were fairly irregular in shape and had a rough surface texture. As residence time in the system increased breakage of agglomerates occurred, reducing the average agglomerate size. In the continuous systems layering of the fine feed particles onto established agglomerates was the predominant growth mechanism. The agglomerates were smooth and spherical. The layer structure was observed by scanning electron microscopy. Agglomerates of widely varying size, strength, and pore matrix can be produced in both systems. It is envisaged that while agglomerates could be produced with characteristics essential for their proposed end use by either method, continuous pan agglomeration would be the most versatile system to utilize.

scholarly journals Workflow towards automated segmentation of agglomerated, non-spherical particles from electron microscopy images using artificial neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bastian Rühle ◽  
Julian Frederic Krumrey ◽  
Vasile-Dan Hodoroaba
Keyword(s):  
Electron Microscopy ◽  
Neural Networks ◽  
Particle Size ◽  
Artificial Neural Networks ◽  
Generative Adversarial Networks ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Adversarial Networks ◽  
Artificial Neural ◽  
Microscopy Images

AbstractWe present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from scanning electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. The whole process only requires about 15 min of hands-on time by a user and can typically be finished within less than 12 h when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. We compare the segmentation masks obtained with our suggested workflow qualitatively and quantitatively to state-of-the-art methods using various metrics. Finally, we used the segmentation masks for automatically extracting particle size distributions from the SEM images of TiO2 particles, which were in excellent agreement with particle size distributions obtained manually but could be obtained in a fraction of the time.

Sign in / Sign up

Export Citation Format

Share Document