Compressed sensing for rapid tabletop X-ray absorption spectroscopy

Abstract

X-ray Absorption Fine Structure (XAFS) spectroscopy is an analytical technique of chemical insight. Improvements to this technique are continuously carried out for reduction of the acquisition time and increasing the resolution, especially for studying operando processes. The accurate reconstruction of XAFS signals requires that the collected data are noise-free to extract elemental structure information in post-processing. A novel approach to rapid sampling (encoded measurement) is validated conceptually. The technique relies on sparse signal recovery, i.e. using compressed sensing to reconstruct under-sampled signals. The proof of concept is demonstrated by using a python code to reconstruct under-sampled XAFS spectra. A case study of transition metal samples and their oxides is considered for this purpose. The results demonstrate a robust recovery of chemical information from a 30% sampled signal for the near-edge region analysis and from a 45% sampled signal for extended-edge region analysis. The application of this procedure is important for demonstrating the feasibility of tabletop operation using laboratory X-ray sources which are not continuously tunable.