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Size and shape distributions of carbon black aggregates by transmission electron microscopy

Authors
Grulke, Eric A.Rice, Stephen B.Xiong, JinChengYamamoto, KazuhiroYoon, Tae HyunThomson, KevinSaffaripour, MeghdadSmallwood, Greg J.Lambert, Joshua W.Stromberg, Arnold J.Macy, RyanBriot, Nicholas J.Qian, Dali
Issue Date
Apr-2018
Publisher
Pergamon Press Ltd.
Citation
Carbon, v.130, pp 822 - 833
Pages
12
Indexed
SCI
SCIE
SCOPUS
Journal Title
Carbon
Volume
130
Start Page
822
End Page
833
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/150248
DOI
10.1016/j.carbon.2018.01.030
ISSN
0008-6223
1873-3891
Abstract
Carbon black aggregate size and shape affects its performance in many applications. In this interlaboratory comparison, an industry reference carbon black, SRB8, was analysed with a protocol based on ASTM D3849-14a, a method for morphological characterization of carbon black aggregates using electron microscopy. Multiple descriptor types (size, elongation, ruggedness, plus those of ASTM D3849-14a) were assessed for repeatability, reproducibility, and measurement uncertainties. Carbon black aggregates have been characterized using descriptor correlations: two important such correlations are affinity coefficients and fractal exponents. SRB8 aggregates appear to be self-affine, i.e., their width and length descriptors scale anisotropically. ASTM D3849-14a derived descriptors have low interlaboratory reproducibilities and high measurement uncertainties. When these descriptors are used for projected area-based fractal analysis, the estimated fractal exponents do not have realistic values. However, the use of an average nodule diameter generated self-consistent values of fractal exponents with measurement uncertainties of about 9%. Carbon black aggregates can be categorized using shape descriptors into the categories: spheroidal, ellipsoidal, branched, and linear. These shape categories contribute nonuniformly to descriptor values across their data ranges and lead to multimodal distributions. These findings illustrate the importance of assessing data quality and measurement uncertainty for particle size and shape distributions.
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