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arxiv 2405.14649 v1 pith:VDKU3JMN submitted 2024-05-23 cond-mat.mtrl-sci cond-mat.mes-hallphysics.data-anphysics.ins-det

Leveraging Machine Learning for Advanced Nanoscale X-ray Analysis: Unmixing Multicomponent Signals and Enhancing Chemical Quantification

classification cond-mat.mtrl-sci cond-mat.mes-hallphysics.data-anphysics.ins-det
keywords psnmfanalysischemicalnoisex-raydatasetselectronleveraging
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Energy dispersive X-ray (EDX) spectroscopy in the transmission electron microscope is a key tool for nanomaterials analysis, providing a direct link between spatial and chemical information. However, using it for precisely determining chemical compositions presents challenges of noisy data from low X-ray yields and mixed signals from phases that overlap along the electron beam trajectory. Here, we introduce a novel method, non-negative matrix factorisation based pan-sharpening (PSNMF), to address these limitations. Leveraging the Poisson nature of EDX spectral noise and binning operations, PSNMF retrieves high quality phase spectral and spatial signatures via consecutive factorisations. After validating PSNMF with synthetic datasets of different noise levels, we illustrate its effectiveness on two distinct experimental cases: a nano-mineralogical lamella, and supported catalytic nanoparticles. Not only does PSNMF obtain accurate phase signatures, datasets reconstructed from the outputs have demonstrably lower noise and better fidelity than from the benchmark denoising method of principle component analysis.

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