Simple ePDF: A Pair Distribution Function Method Based on Electron Diffraction Patterns to Reveal the Local Structure...

Understanding the local atomic structure of amorphous and nanocrystalline materials is crucial for advancing their use in various fields, including structural materials and pharmaceuticals. Prior research has shown that these materials often exhibit structural similarities to crystalline counterparts. However, a key gap remains in the availability of accessible tools for analyzing their local structure. Existing methods rely on complex software environments, which limits their usability. This paper introduces a new approach that simplifies the process. The study builds on established diffraction techniques but introduces a novel computational method. It addresses the need for standalone software that does not require specialized environments. The focus is on improving accessibility for researchers without advanced computational resources. The authors aim to bridge the gap between theoretical models and practical analysis tools. This work provides a solution that supports both qualitative and quantitative structural analysis.

The study aims to develop a user-friendly software tool for analyzing the local structure of amorphous and nanocrystalline materials. The primary goal is to extract pair distribution function (PDF) data from electron diffraction patterns obtained via TEM. The authors seek to make this process more accessible by eliminating the need for specialized software environments. They also aim to provide additional functionalities such as correlation coefficient calculations and fingerprinting. The study addresses the challenge of quantifying structural similarity in disordered materials. The authors propose integrating both experimental and simulated PDF data for comparison. Their approach includes polynomial corrections to improve data accuracy. The ultimate goal is to enable researchers to analyze local structures with minimal computational overhead.

The software is designed to run on Windows-based PCs and processes electron diffraction data from TEM. It extracts PDF data using a polynomial correction to reduce systematic deviations in scattering. The program calculates neighbor distances and coordination numbers. It also enforces or measures number density for quantification. Pearson's correlation coefficient is used to assess structural similarity. Fingerprinting is included to identify unique structural features. The software simulates PDFs from crystalline models stored in CIF or str files. These simulated PDFs are used in libraries for comparison with experimental data. The program supports multiple least-square fitting for estimating mixture fractions. It is designed to be standalone, avoiding reliance on external software environments.

The program successfully extracts PDF data from electron diffraction patterns of inorganic samples. Polynomial corrections effectively reduce systematic deviations in scattering data. Neighbor distances and coordination numbers are accurately calculated. Number density is either measured or enforced to support quantification. Pearson's correlation coefficient provides a reliable measure of structural similarity. Fingerprinting identifies unique structural characteristics in disordered materials. Simulated PDFs from crystalline models match experimental data well. Multiple least-square fitting estimates mixture fractions with reasonable accuracy. The software's standalone nature simplifies its use compared to previous tools. The authors demonstrate its effectiveness with examples from inorganic materials.

The study concludes that the new software provides a practical solution for analyzing the local structure of amorphous and nanocrystalline materials. The authors state that the program's standalone design is a key advantage over existing tools. They note that polynomial corrections improve data accuracy. The program's ability to calculate correlation coefficients and fingerprints is highlighted. Simulated PDFs from crystalline models support structural comparisons. Mixture fraction estimation through least-square fitting is feasible. The software's accessibility is emphasized as a novel contribution. The authors suggest that this tool enhances the analysis of disordered materials without requiring specialized environments.