Study of complex structure using mixed data and complex modeling

The new complex materials have wide applications in next generation technologies in industrial fields such as electronics, energy production, environment engineering, etc. Understanding their structure is the key in keeping developing new materials and improving their performance. As they are more and more complex in different length scale, new methods that utilize information from different sources and be able to provide complex structural information are on the horizon of this new era. In this thesis, we developed new methods that process the mixed data and provide the extra information that people are interested in. First one is extending the computed tomography technique with other analysis method including texture analysis and Pair Distribution Function (PDF) method. The new methods enable us to study the coupling of desired structural properties, such as texture and local local structure of nano-particles, at meso-scale. For example, by applying the texture-CT analysis on the LiCoO₂ coin cell, we found the texture of LiCoO₂ particles was quite inhomogeneous. By combining PDF and CT method, we successfully studied the catalyst reaction and the participle size distribution in industrial catalyst. Second one is a new method of obtaining reliable anomalous differential Pair Distribution Function (adPDF) by using diffraction data sets in wide energy range and an ad-hoc algorithm that perform the data correction automatically. The new method was demonstrated using both simulated data and real experimental data.