X-ray Analysis

Latest News


Cancer detection and screening as a treatment for malignant cells with a biopsy or testing caused by carcinogens and genetics with a cancerous cell as an immunotherapy symbol | Image Credit: © freshidea - stock.adobe.com

Machine learning and synchrotron radiation-based micro X-ray fluorescence imaging show promise for early cancer diagnostics by identifying trace biometals as potential cancer biomarkers. The research demonstrates the feasibility of using machine learning algorithms to analyze the spatial distribution of biometals and classify cancer pathogenesis stages, offering potential advancements in non-invasive cancer detection.

Harishchandra Singh, Graham King and associates have employed high energy synchrotron X-ray diffraction (HE-SXRD) experiments and an analytical model in order to predict the yield strength of cerium-modified super duplex stainless steel (SDSS) subjected to various cold- and cryo-deformation. Spectroscopy recently had the opportunity to discuss the experiments and the findings with Singh and King.

In X-ray fluorescence (XRF) analysis, physical traceability chains are used to quantify the absolute elemental content in a sample. The physical traceability chain relies on absolute knowledge of the X-ray spectral distribution used for the excitation of the instrument and is currently used at synchrotron radiation facilities. Here, we discuss the transfer of the physical traceability chain to laboratory-based X-ray sources, which are often polychromatic, with the view to generate wider application of quantitative XRF analysis.

In the agrifood sector, soil sampling and analysis is a prerequisite for accurate fertilizer management and to monitor the accumulation of heavy metals in soils. In this study, energy dispersive X-ray fluorescence (EDXRF) was used to analyze soils with variable textures (clay and sandy) and the percent recovery of elements was compared, as a measure of accuracy.

L. Robert Baker is an associate professor at The Ohio State University in the Department of Chemistry & Biochemistry. His research focuses on X-ray spectroscopy, nonlinear and time-resolved spectroscopy, the chemistry of surfaces and interface science, and energy conversion and catalysis—work that may lead to better solar energy conversion materials. He is the winner of the 2021 Emerging Leader in Atomic Spectroscopy Award, which is presented by Spectroscopy magazine. This annual award, begun in 2017, recognizes the achievements and aspirations of a talented young atomic spectroscopist, selected by an independent scientific committee.