Particulate pollution case studies which illustrate uses of individual particle analysis by scanning electron microscopy

1989 ◽  
Vol 11 (3-4) ◽  
pp. 157-162 ◽  
Author(s):  
Ian Heasman ◽  
John Watt
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Case Studies ◽  
Individual Particle ◽  
Particle Analysis ◽  
Particulate Pollution ◽  
Individual Particle Analysis ◽  
Scanning Electron

Cluster Analysis of Automated Fine-Particle Analysis Using Scanning Electron Microscopy

1997 ◽  
Vol 3 (S2) ◽  
pp. 1071-1072
Author(s):  
C.A. O’Keefe ◽  
J.P. Hurley
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Cluster Analysis ◽  
Chemical Composition ◽  
Fine Particle ◽  
Submicron Particles ◽  
Particle Analysis ◽  
Solvent Ratio ◽  
Individual Particles ◽  
Scanning Electron

Because of analytical advances, submicron particles as small as 0.2 μm can be characterized for chemical composition, size, and shape using scanning electron microscopy (SEM). Once these characteristics are determined, cluster analysis can be used to group the individual particles into categories based on size, shape, and chemical composition.Submicron particle analysis is important when characterizing the ash to provide information to help solve ash-related problems in coal combustion and gasification systems. Since a combustion system has an excess of oxygen available, the resulting ash is typically easier to characterize than ash from a gasification system. In a gasification system, the lack of oxygen results in additional categories high in Cl and P.Adequate dispersion of submicron particles for proper analysis of individual particles is required because of the analysis volume of the SEM beam. Therefore, an aliquot of an aqueous solution with a set sample-to-solvent ratio is drawn while being sonicated. A few drops of solution are placed on a vitreous carbon substrate, allowing for particle dispersion on the surface of a smooth substrate. Next, the particles are analyzed by the fine-particle technique (FPT).

MSWI Fly Ash Particle Analysis by Scanning Electron Microscopy-Energy Dispersive X-ray Spectroscopy

2004 ◽  
Vol 38 (24) ◽  
pp. 6669-6675 ◽  
Author(s):  
S. Gilardoni ◽  
P. Fermo ◽  
F. Cariati ◽  
V. Gianelle ◽  
D. Pitea ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Fly Ash ◽  
Energy Dispersive ◽  
Particle Analysis ◽  
Mswi Fly Ash ◽  
X Ray ◽  
Scanning Electron

scholarly journals Analysis of Electron Transparent Beam-Sensitive Samples Using Scanning Electron Microscopy Coupled With Energy-Dispersive X-ray Spectroscopy

2020 ◽  
Vol 26 (3) ◽  
pp. 373-386 ◽  
Author(s):  
Anders Brostrøm ◽  
Kirsten Inga Kling ◽  
Karin Sørig Hougaard ◽  
Kristian Mølhave
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Feature Detection ◽  
Energy Dispersive ◽  
Detection Methods ◽  
Acquisition Time ◽  
Particle Analysis ◽  
X Ray ◽  
Advantages And Disadvantages ◽  
Scanning Electron

AbstractScanning electron microscopy, coupled with energy-dispersive X-ray spectroscopy (EDS), is a powerful tool used in many scientific fields. It can provide nanoscale images, allowing size and morphology measurements, as well as provide information on the spatial distribution of elements in a sample. This study compares the capabilities of a traditional EDS detector with a recently developed annular EDS detector when analyzing electron transparent and beam-sensitive NaCl particles on a TEM grid. The optimal settings for single particle analysis are identified in order to minimize beam damage and optimize sample throughput via the choice of acceleration voltage, EDS acquisition time, and quantification model. Here, a linear combination of two models is used to bridge results for particle sizes, which are neither bulk nor sufficiently thin to assume electron transparent. Additionally, we show that the increased count rate obtainable with the annular detector enables mapping as a viable analysis strategy compared with feature detection methods, which only scan segmented regions. Finally, we discuss advantages and disadvantages of the two analysis strategies.

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