Sparse X-ray spectro-tomography for high-sensitivity three-dimensional chemical imaging at the nanoscale

Abstract

Achieving three-dimensional (3D) chemical imaging with scanning x-ray microscopy is often hindered by the prohibitively long acquisition time. In this work, we develop a method that drastically reduces the data requirement by two orders of magnitude, thereby significantly decreasing acquisition time and radiation dose. This is accomplished through sparse and stochastic energy sampling combined with a joint spectral-tomographic reconstruction algorithm that performs the spectrum fitting with prior knowledge of the known reference spectra and 3D reconstruction in a single step. Using this approach, we reveal the reduced oxidation state of Co on the surface of a cycled lithium-ion battery particle and its correlation with the depletion of Mn in 3D. This technique enables comprehensive 3D material characterization with high sensitivity and multimodality, opening opportunities in various scientific and technological fields where volumetric chemical information at the nanoscale is essential. The study introduces a sparse spectro‑tomography method that cuts data needs in scanning mode by about two orders of magnitude, enabling high-sensitivity, low‑dose 3D chemical imaging. It uncovers surface Co reduction and Mn loss in battery particles, revealing key degradation pathways.