Classification Strategies for Regulatory Nanodefinitions - Safety of Nanomaterials along Their Lifecycle - page 48

1. Ensemble - Particles over a range of sizes are
measured all at once. (e.g., laser diffraction, acoustic
attenuation, DLS)

Most popular,
most error

2. Classifying - Particles are separated into
size classes for measurement. (e.g., disc centrifuge, sieve analysis,
fractionation + ensemble measurements, field flow fractionation,
analytical ultracentrifuge)

3. Counting - Particles are counted one at a time.
(e.g., Microscopy, coulter counting/scanning occlusion
particle counting, single particle optical sizing, particle tracking)

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More tedious,
most “accurate”

4. Classified counting - Particles are
separated into size classes then counted
one at a time. (e.g., Classification + microscopy,
electrospray ionization dynamic mobility analysis)

FIGURE 3.1 Systematization of methods to assess the size distribution of particulate mate-
rials in number metrics. (From Brown, S.C. et al., Environ Health Perspect , 121, 1282-1291,
2013.)

external diameter to irregularly shaped primary particles (Linsinger et al. 2012) and
the counting of small agglomerates of nonseparated primary particles. Allen (1997a,
140ff) gives a comprehensive overview of the sample preparation techniques for
TEM. The current emerging techniques for TEM preparation include electrospray
ionization, ultrasonic dispersion, or ultrasonic evaporation (Lenggoro et al. 2006; Li
et al. 2011; Pease et al. 2010; Taurozzi et al. 2010). For indispersible materials (e.g.,
those consisting completely of aggregates, or those designed for matrices incompati-
ble with TEM), scanning electron microscopy (SEM) is applicable but often requires
a multiple of time for image evaluation (compare ISO 13322-1). Currently, there are
only exploratory methods to determine the distribution of the thickness of platelets;
AFM (e.g., through ASTM E2859-11) could be an option, but has not been validated
(Baalousha and Lead 2013). Further method development needs for electron micros-
copy are discussed by Brown et al. (2013).
The specific case of a rod-shaped iron oxide hydrate with the color index Pigment
Yellow 42 highlights the difficulties in identifying the “boundary” of individual
rods (Figure 3.2a). However, the same TEM grid displays also larger agglomerates
(Figure 3.2b), which easily attain nonidentifiable structure or simply lose trans-
parency for the electron beam. Hence these larger agglomerates must be excluded
from automated evaluation, possibly introducing a sampling bias. Selecting only the
small agglomerates, a semiautomated image analysis of 282 primary particles was
performed, resulting in the distribution shown in Figure 3.2c. In order to enable
comparisons to other methods (see the following), in this case the longer axis of the