Abstract
A filter based on the Hankel-Lanczos singular value decomposition (HLSVD) technique is presented and applied for the first time to X-ray diffraction (XRD) data. Synthetic and real powder XRD intensity profiles of nanocrystals are used to study the filter performances with different noise levels. Results show the robustness of the HLSVD filter and its capability to extract easily and effciently the useful crystallographic information. These characteristics make the filter an interesting and user-friendly tool for processing of XRD data.
Highlights
In many applications of X-ray diffraction (XRD) techniques to the study of crystal properties, a key step in the data processing chain is an effective and adaptive noise filtering [1,2,3,4]
We describe an application of the HankelLanczos singular value decomposition (HLSVD) algorithm to filter XRD intensity data
Noisy synthetic XRD patterns were generated corresponding to nanocrystalline samples of increasing size from 2 to 4 nm, and Poisson-distributed noise with increasing noise-to-signal ratio (NSR) from 2% to 10%
Summary
In many applications of X-ray diffraction (XRD) techniques to the study of crystal properties, a key step in the data processing chain is an effective and adaptive noise filtering [1,2,3,4]. A criterion is presented to facilitate the separation of noise from the useful crystallographic signal It is completely user-independent since it is based on a numerical method. Synthetic XRD datasets are generated by computing the X-ray scattered intensity from nanocrystalline samples of different sizes and properties by using an analytic expression (see (6)). Numerical tests on real XRD data of Au nanocrystalline samples of different sizes and properties show the robustness of the proposed filter and its capability to extract and efficiently the useful crystallographic information. These characteristics make this filter an interesting and user-friendly tool for the interactive processing of XRD data.
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