Protective clothing against solid airborne particles including radioactive contamination

2018 ◽  
Keyword(s):  
Radioactive Contamination ◽  
Protective Clothing ◽  
Airborne Particles

Decontamination of protective clothing against radioactive contamination

2014 ◽  
Vol 162 (1-2) ◽  
pp. 144-147
Author(s):  
I. Vo ahlikova ◽  
P. Otahal
Keyword(s):  
Radioactive Contamination ◽  
Protective Clothing

Radioactive Contamination Incidents Involving Protective Clothing

1998 ◽  
Vol 74 (1) ◽  
pp. 99-107 ◽  
Author(s):  
Richard A. Reichelt ◽  
Marc E. Clay ◽  
A. Jeffery Eichorst
Keyword(s):  
Radioactive Contamination ◽  
Protective Clothing

scholarly journals Radioactive contamination incidents involving protective clothing

1996 ◽  
Author(s):  
R. Reichelt ◽  
M. Clay ◽  
J. Eichorst
Keyword(s):  
Radioactive Contamination ◽  
Protective Clothing

Identifying groups of airborne particles by ordination

1994 ◽  
Vol 52 ◽  
pp. 856-857
Author(s):  
M. Shlepr ◽  
C. M. Vicroy
Keyword(s):  
Elemental Analysis ◽  
Energy Dispersive Spectroscopy ◽  
Manufacturing Process ◽  
Spatial Representation ◽  
Airborne Particles ◽  
Common Source ◽  
Data Set ◽  
Microelectronics Industry ◽  
Induced Changes ◽  
Source Materials

The microelectronics industry is heavily tasked with minimizing contaminates at all steps of the manufacturing process. Particles are generated by physical and/or chemical fragmentation from a mothersource. The tools and macrovolumes of chemicals used for processing, the environment surrounding the process, and the circuits themselves are all potential particle sources. A first step in eliminating these contaminants is to identify their source. Elemental analysis of the particles often proves useful toward this goal, and energy dispersive spectroscopy (EDS) is a commonly used technique. However, the large variety of source materials and process induced changes in the particles often make it difficult to discern if the particles are from a common source.Ordination is commonly used in ecology to understand community relationships. This technique usespair-wise measures of similarity. Separation of the data set is based on discrimination functions. Theend product is a spatial representation of the data with the distance between points equaling the degree of dissimilarity.

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