PDFgetX3: A rapid and highly automatable program for processing powder diffraction data into total scattering pair distribution functions

Abstract

PDFgetX3 is a new software application for converting X-ray powder diffraction data to atomic pair distribution function (PDF). PDFgetX3 has been designed for ease of use, speed and automated operation. The software can readily process hundreds of X-ray patterns within few seconds and is thus useful for high-throughput PDF studies, that measure numerous datasets as a function of time, temperature or other environment parameters. In comparison to the preceding programs, PDFgetX3 requires fewer inputs, less user experience and can be readily adopted by novice users. The live-plotting interactive feature allows to assess the effects of calculation parameters and select their optimum values. PDFgetX3 uses an ad-hoc data correction method, where the slowly-changing structure independent signal is filtered out to obtain coherent X-ray intensities that contain structure information. The outputs from PDFgetX3 have been verified by processing experimental PDFs from inorganic, organic and nanosized samples and comparing them to their counterparts from previous established software. In spite of different algorithm, the obtained PDFs were nearly identical and yielded highly similar results when used in structure refinement. PDFgetX3 is written in Python language and features well documented, reusable codebase. The software can be used either as standalone application or as a library of PDF-processing functions that can be called on from other Python scripts. The software is free for open academic research, but requires paid license for commercial use.

21 pages, 5 figures, submission to the Journal of Applied Crystallography

References in corpus (2)

• Quantitative size-dependent structure and strain determination of CdSe nanoparticles using atomic pair distribution function analysis
• Relationship between the atomic pair distribution function and small angle scattering: implications for modeling of nanoparticles

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