Robust retrieval of material chemical states in X-ray microspectroscopy
This work addresses a major obstacle in materials science research by providing a robust method for chemical state analysis in complex samples.
The authors tackled the problem of reliably retrieving chemical states from X-ray microspectroscopy data, which is hindered by noise and spectral variability, by proposing a novel unmixing framework that accurately identifies chemical states even under low signal-to-noise ratios and overlapping features.
X-ray microspectroscopic techniques are essential for studying morphological and chemical changes in materials, providing high-resolution structural and spectroscopic information. However, its practical data analysis for reliably retrieving the chemical states remains a major obstacle to accelerating the fundamental understanding of materials in many research fields. In this work, we propose a novel data formulation model for X-ray microspectroscopy and develop a dedicated unmixing framework to solve this problem, which is robust to noise and spectral variability. Moreover, this framework is not limited to the analysis of two-state material chemistry, making it an effective alternative to conventional and widely-used methods. In addition, an alternative directional multiplier method with provable convergence is applied to obtain the solution efficiently. Our framework can accurately identify and characterize chemical states in complex and heterogeneous samples, even under challenging conditions such as low signal-to-noise ratios and overlapping spectral features. Extensive experimental results on simulated and real datasets demonstrate its effectiveness and reliability.