Speaker
Description
Reliable extraction of microstructural information from X-ray diffraction (XRD) data requires accurate separation of instrumental broadening from the intrinsic sample response. In this work, we present an iterative forward-convolution method optimized using a genetic algorithm (GA) to recover intrinsic diffraction line profiles from measurements acquired in transmission geometry with a two-dimensional detector. In contrast to conventional single-peak fitting methods, the proposed approach performs a global optimization over the complete measured $2\theta$ range, improving the stability and robustness of the inverse problem while simultaneously accounting for the instrumental resolution function. The methodology is demonstrated using synchrotron XRD data collected over a wide range of sample-to-detector distances. The proposed analysis substantially reduces systematic variations in the extracted crystallite size caused by changes in the experimental geometry, yielding consistent microstructural parameters that are in good agreement with transmission electron microscopy. The presented framework provides a robust and broadly applicable strategy for quantitative XRD line-profile analysis and instrumental broadening correction in diffraction experiments.
Acknowledgment
The authors express their gratitude to Dr. V. Girman for help with TEM observations. Parts of this researchwere carried out at the light source PETRA III (beamline P21.2) at DESY, a member of the Helmholtz Association HGF. This study was funded by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under the project No. 09I03-03-V03-00034.
