Robust retrieval of material chemical states in X-ray microspectroscopy

TL;DR

This paper introduces a robust unmixing framework for X-ray microspectroscopy that accurately identifies chemical states in complex samples, even with noise and spectral variability, advancing material analysis capabilities.

Contribution

The work presents a novel data formulation model and unmixing framework that is noise-robust, applicable to multiple chemical states, and efficiently solved with a provably convergent method.

Findings

Accurately identifies chemical states in complex samples.

Effective under low signal-to-noise ratios.

Demonstrates high reliability on simulated and real data.

Abstract

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.