Size and shape distributions of carbon black aggregates by transmission electron microscopy. (April 2018)

• Record Type: Journal Article
• Authors: Grulke, Eric A.; Rice, Stephen B.; Xiong, JinCheng; Yamamoto, Kazuhiro; Yoon, Tae Hyun; Thomson, Kevin; Saffaripour, Meghdad; Smallwood, Greg J.; Lambert, Joshua W.; Stromberg, Arnold J.; Macy, Ryan; Briot, Nicholas J.; Qian, Dali
• Is Part Of: Carbon. Volume 130(2018)
• Page Start: 822
• Page End: 833
• Publication Date: 2018-04
• Subjects: Carbon -- Periodicals; Carbone -- Périodiques; Koolstof; Toepassingen; Electronic journals; 546.681
• DOI: 10.1016/j.carbon.2018.01.030
• Languages: English
• ISSNs: 0008-6223

Abstract

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 non-uniformly 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. Graphical abstract: