Open AccessA synchrotron‐based local computed tomography combined with data‐constrained modelling approach for quantitative analysis of anthracite coal microstructure
Author(s) -
Chen Wen Hao,
Yang Sam Y. S.,
Xiao Ti Qiao,
Mayo Sherry C.,
Wang Yu Dan,
Wang Hai Peng
Publication year - 2014
Publication title -
journal of synchrotron radiation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.172
H-Index - 99
ISSN - 1600-5775
DOI - 10.1107/s1600577514002793
Subject(s) - anthracite , characterization (materials science) , coal , sample (material) , porosity , mineralogy , transformation (genetics) , materials science , tomography , synchrotron , image resolution , geology , biological system , computer science , chemistry , artificial intelligence , optics , nanotechnology , physics , composite material , gene , biochemistry , organic chemistry , chromatography , biology
Quantifying three‐dimensional spatial distributions of pores and material compositions in samples is a key materials characterization challenge, particularly in samples where compositions are distributed across a range of length scales, and where such compositions have similar X‐ray absorption properties, such as in coal. Consequently, obtaining detailed information within sub‐regions of a multi‐length‐scale sample by conventional approaches may not provide the resolution and level of detail one might desire. Herein, an approach for quantitative high‐definition determination of material compositions from X‐ray local computed tomography combined with a data‐constrained modelling method is proposed. The approach is capable of dramatically improving the spatial resolution and enabling finer details within a region of interest of a sample larger than the field of view to be revealed than by using conventional techniques. A coal sample containing distributions of porosity and several mineral compositions is employed to demonstrate the approach. The optimal experimental parameters are pre‐analyzed. The quantitative results demonstrated that the approach can reveal significantly finer details of compositional distributions in the sample region of interest. The elevated spatial resolution is crucial for coal‐bed methane reservoir evaluation and understanding the transformation of the minerals during coal processing. The method is generic and can be applied for three‐dimensional compositional characterization of other materials.