关键词: small angle x-ray scattering, data analysis software, particle size distribution, inverse problem

Abstract: Small angle x-ray scattering (SAXS) is an advanced technique for characterizing the particle size distribution (PSD) of nanoparticles. However, the ill-posed nature of inverse problems in SAXS data analysis often reduces the accuracy of conventional methods. This article proposes a user-friendly software for PSD analysis, GranuSAS, which employs an algorithm that integrates truncated singular value decomposition (TSVD) with the Chahine method. This approach employs TSVD for data preprocessing, generating a set of initial solutions with noise suppression. A high-quality initial solution is subsequently selected via the $L$-curve method. This selected candidate solution is then iteratively refined by the Chahine algorithm, enforcing constraints such as non-negativity and improving physical interpretability. Most importantly, GranuSAS employs a parallel architecture that simultaneously yields inversion results from multiple shape models and, by evaluating the accuracy of each model's reconstructed scattering curve, offers a suggestion for model selection in material systems. To systematically validate the accuracy and efficiency of the software, verification was performed using both simulated and experimental datasets. The results demonstrate that the proposed software delivers both satisfactory accuracy and reliable computational efficiency. It provides an easy-to-use and reliable tool for researchers in materials science, helping them fully exploit the potential of SAXS in nanoparticle characterization.

Key words: small angle x-ray scattering, data analysis software, particle size distribution, inverse problem