A strategy grounded on the central-moment-expansion approach is proposed for addressing the resolution discrepancy inherent in cross-sectional measurements obtained via small-angle scattering experiments.

A strategy based on the orthonormal basis expansion approach is proposed for extracting the density profile of soft-matter solutions from their small-angle scattering signatures.

A deep learning approach has been developed for analyzing small-angle scattering data, effectively addressing the potential inversion problem in colloids. The method is validated using both simulation results and experimental spectra of charged silica suspensions and is shown to outperform existing approaches in accuracy and efficiency. This study highlights the potential of deep learning in soft-matter research.