A method for assessing nanomaterial dispersion quality based on principal component analysis of particle size distribution data

Particuology ◽  
2015 ◽  
Vol 22 ◽  
pp. 30-38 ◽  
Author(s):  
R. Tantra ◽  
C. Oksel ◽  
K.N. Robinson ◽  
A. Sikora ◽  
X.Z. Wang ◽  
...  
2021 ◽  
Vol 7 (8) ◽  
pp. 127
Author(s):  
Giuseppe Capobianco ◽  
Giorgia Agresti ◽  
Giuseppe Bonifazi ◽  
Silvia Serranti ◽  
Claudia Pelosi

This paper reports the results of particle size analysis and colour measurements concerning yellow powders, synthesised in our laboratories according to ancient recipes aiming at producing pigments for paintings, ceramics, and glasses. These pigments are based on lead and antimony as chemical elements, that, combined in different proportions and fired at different temperatures, times, and with various additives, gave materials of yellow colours, changing in hues and particle size. Artificial yellow pigments, based on lead and antimony, have been widely studied, but no specific investigation on particle size distribution and its correlation to colour hue has been performed before. In order to evaluate the particle size distribution, segmentation of sample data has been performed using the MATLAB software environment. The extracted parameters were examined by principal component analysis (PCA) in order to detect differences and analogies between samples on the base of those parameters. Principal component analysis was also applied to colour data acquired by a reflectance spectrophotometer in the visible range according to the CIELAB colour space. Within the two examined groups, i.e., yellows containing NaCl and those containing K-tartrate, differences have been found between samples and also between different areas of the same powder indicating the inhomogeneity of the synthesised pigments. On the other hand, colour data showed homogeneity within each yellow sample and clear differences between the different powders. The comparison of results demonstrates the potentiality of the particle segmentation and analysis in the study of morphology and distribution of pigment powders produced artificially, allowing the characterisation of the lead and antimony-based pigments through micro-image analysis and colour measurements combined with a multivariate approach.


2007 ◽  
Vol 7 (3) ◽  
pp. 887-897 ◽  
Author(s):  
T. W. Chan ◽  
M. Mozurkewich

Abstract. Absolute principal component analysis can be applied, with suitable modifications, to atmospheric aerosol size distribution measurements. This method quickly and conveniently reduces the dimensionality of a data set. The resulting representation of the data is much simpler, but preserves virtually all the information present in the original measurements. Here we demonstrate how to combine the simplified size distribution data with trace gas measurements and meteorological data to determine the origins of the measured particulate matter using absolute principal component analysis. We have applied the analysis to four different sets of field measurements that were conducted at three sites in southern Ontario. Several common factors were observed at all the sites; these were identified as photochemically produced secondary aerosol particles, regional pollutants (including accumulation mode aerosol particles), and trace gas variations associated with boundary layer dynamics. Each site also exhibited a factor associated specifically with that site: local industrial emissions in Hamilton (urban site), processed nucleation mode particles at Simcoe (polluted rural site), and transported fine particles at Egbert (downwind from Toronto).


2007 ◽  
Vol 7 (3) ◽  
pp. 875-886 ◽  
Author(s):  
T. W. Chan ◽  
M. Mozurkewich

Abstract. Principal component analysis provides a fast and robust method to reduce the data dimensionality of an aerosol size distribution data set. Here we describe a methodology for applying principal component analysis to aerosol size distribution measurements. We illustrate the method by applying it to data obtained during five field studies. Most variations in the sub-micrometer aerosol size distribution over periods of weeks can be described using 5 components. Using 6 to 8 components preserves virtually all the information in the original data. A key aspect of our approach is the introduction of a new method to weight the data; this preserves the orthogonality of the components while taking the measurement uncertainties into account. We also describe a new method for identifying the approximate number of aerosol components needed to represent the measurement quantitatively. Applying Varimax rotation to the resultant components decomposes a distribution into independent monomodal distributions. Normalizing the components provides physical meaning to the component scores. The method is relatively simple, computationally fast, and numerically robust. The resulting data simplification provides an efficient method of representing complex data sets and should greatly assist in the analysis of size distribution data.


2006 ◽  
Vol 6 (5) ◽  
pp. 10463-10492
Author(s):  
T. W. Chan ◽  
M. Mozurkewich

Abstract. Principal component analysis provides a fast and robust method to reduce the data dimensionality of an aerosol size distribution data set. Here we describe a methodology for applying principal component analysis to aerosol size distribution measurements. We illustrate the method by applying it to data obtained during five field studies. Most variations in the sub-micrometer aerosol size distribution over periods of weeks can be described using 5 components. Using 6 to 8 components preserves virtually all the information in the original data. A key aspect of our approach is the introduction of a new method to weight the data; this preserves the orthogonality of the components while taking the measurement uncertainties into account. We also describe a new method for identifying the approximate number of aerosol components needed to represent the measurement quantitatively. Applying Varimax rotation to the resultant components decomposes a distribution into independent monomodal distributions. Normalizing the components provides physical meaning to the component scores. The method is relatively simply, computationally fast, and numerically robust. The resulting data simplification provides an efficient method of representing complex data sets and should greatly assist in the analysis of size distribution data.


1972 ◽  
Vol 94 (2) ◽  
pp. 117-123
Author(s):  
W. Downs ◽  
S. S. Strom

A basic parameter needed for judicious design, selection, and regulation of industrial gas cleanup equipment is particle size distribution of gas-borne emissions. Despite the importance of this parameter, a cloud of uncertainty about the validity of present methods used for particle size distribution measurements has produced a technological gap. This paper describes an apparatus and method which is capable of bridging this gap. Particle size distribution data obtained, particularly in the near micron and submicron range, can provide means for determining grade efficiencies of dust collectors, expected opacity of stack emissions, and dispersion characteristics of plumes. The apparatus described was developed for manual sampling and size distribution determination of aerosols within industrial flues and stacks. The entire apparatus is contained within a sampling probe. The principal component is a cylindrical cascade impactor. Design of the probe eliminates most of the technical difficulties associated with previous attempts at implementing cascade impactors to this task. This probe has been used successfully for sampling gases up to 450 F.


2006 ◽  
Vol 6 (5) ◽  
pp. 10493-10522 ◽  
Author(s):  
T. W. Chan ◽  
M. Mozurkewich

Abstract. Absolute principal component analysis can be applied, with suitable modifications, to atmospheric aerosol size distribution measurements. This method quickly and conveniently reduces the dimensionality of a data set. The resulting representation of the data is much simpler, but preserves virtually all the information present in the original measurements. Here we demonstrate how to combine the simplified size distribution data with trace gas measurements and meteorological data to determine the origins of the measured particulate matter using absolute principal component analysis. We have applied the analysis to four different sets of field measurements that were conducted at three sites in southern Ontario. Several common factors were observed at all the sites; these were identified as photochemically produced secondary aerosol particles, regional pollutants (including accumulation mode aerosol particles), and trace gas variations associated with boundary layer dynamics. Each site also exhibited a factor associated specifically with that site: local industrial emissions in Hamilton (urban site), processed nucleation mode particles at Simcoe (polluted rural site), and transported fine particles at Egbert (downwind from Toronto).


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