4.8 Article

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

期刊

CARBON
卷 130, 期 -, 页码 822-833

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.carbon.2018.01.030

关键词

-

资金

  1. Ministry of Economy, Trade, and Industry (METI) of Japan
  2. University of Kentucky's College of Arts and Sciences

向作者/读者索取更多资源

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. (c) 2018 Elsevier Ltd. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据