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1. Unraveling Various Sources of Particulates Matter in Air‐Dust Samples Collected from North India Through Elemental Mapping and Concentration Correlation Using Micro‐Particle‐Induced X‐Ray Emission Spectroscopy

   https://doi.org/10.1002/smll.202409264  

   Saini, Darshpreet Kaur; Byers, Todd A; Verma, Shivcharan; Sharma, Mohin; Parashar, Mritunjaya; Bowen, Charles T; Mohanty, Biraja; Glass, Gary A; Rout, Bibhudutta (February 2025, Small)

   Particulate matter (PM) found in the air is one of the major sources of pollution and air‐borne diseases. Therefore, it is imperative to examine the elemental concentration distribution of the PM to identify the pollutant sources. In this study, it has demonstrated the capabilities of micro‐particle‐induced X‐ray emission (micro‐PIXE) spectroscopy in quantitative analysis of air samples collected from the Old Delhi outdoor market and indoor locations in the Panjab University hostel in the winter months. A 2‐million electronvolts energetic scanning proton micro‐beam (diameter ≈1 µm²) is used in micro‐PIXE experiments generating high‐resolution elemental maps of different regions of interest (ROI). Micro‐PIXE along with the GeoPIXE analysis provides a non‐destructive, standard‐less, and ng/mg level‐sensitive tool for the investigation of elemental distributions and highlighting pixels, which correlates to specific concentration ratios between elements at ROIs, thereby enabling a comprehensive understanding of the source of each elemental particulate. Si, Ca, and K detected in indoor PM suggest the source to soil erosion and crop burning, while high S levels in outdoor PM are primarily associated with coal power plants. Additionally, Sc, Ti, Cr, Mn, and Zn are found in outdoor samples, while indoor locations also contained trace amounts of V, Co, and Cu. 

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2. Prototype magnetic deflector system for light element particle induced X-ray emission analysis

   https://doi.org/10.1016/j.nimb.2025.165837  

   Byers, Todd A; Saini, Darshpreet Kaur; Bowen, Charles T; Rout, Bibhudutta; Glass, Gary A (October 2025, Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms)

   Radović, Iva Bogdanović; Lorenz, Katharina; Wang, Yongqiang; Yasuda, Kazuhiro (Ed.)

   The improvements made to ultra-thin windows for X-ray detectors in recent years have allowed for the detection of elements as light as lithium. However, their use with particle induced X-ray emission (PIXE) spectroscopy typically requires the addition of an absorber thick enough to prevent backscattered ions from reaching the detector. This also prevents lower energy (< 1 keV) X-rays from reaching the detector. By using a magnetic field to deflect backscattered ions away, the absorber can be eliminated, allowing for the detection of ultra-low energy X-rays. At the Ion Beam Laboratory of the University of North Texas, a prototype PIXE system using a magnetic deflector has been developed to allow for the detection and measurement of X-rays from light elements using a silicon drift X-ray detector with an ultra-thin window. With an average magnetic flux density of 0.88 T along the center, backscattered protons of an energy up to 1.22 MeV were successfully deflected away from the X-ray detector. Light element PIXE was performed with a 1 MeV proton beam on manganese oxide, sodium chloride and a Hibiscus rosa-sinensis leaf. Elements of 5 ≤ Z ≤ 30 were successfully detected. 

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3. Evaluation of quantitative synchrotron radiation micro-X-ray fluorescence in rice grain

   https://doi.org/10.1107/S1600577523000747  

   Limmer, Matt A.; Webb, Samuel M.; Seyfferth, Angelia L. (March 2023, Journal of Synchrotron Radiation)

   Concentrations of nutrients and contaminants in rice grain affect human health, specifically through the localization and chemical form of elements. Methods to spatially quantify the concentration and speciation of elements are needed to protect human health and characterize elemental homeostasis in plants. Here, an evaluation was carried out using quantitative synchrotron radiation microprobe X-ray fluorescence (SR-µXRF) imaging by comparing average rice grain concentrations of As, Cu, K, Mn, P, S and Zn measured with rice grain concentrations from acid digestion and ICP-MS analysis for 50 grain samples. Better agreement was found between the two methods for high-Zelements. Regression fits between the two methods allowed quantitative concentration maps of the measured elements. These maps revealed that most elements were concentrated in the bran, although S and Zn permeated into the endosperm. Arsenic was highest in the ovular vascular trace (OVT), with concentrations approaching 100 mg kg⁻¹in the OVT of a grain from a rice plant grown in As-contaminated soil. Quantitative SR-µXRF is a useful approach for comparison across multiple studies but requires careful consideration of sample preparation and beamline characteristics. 

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4. Minimum detectable levels of biologically relevant elements in P8 filter paper standard using PIXE spectroscopy

   https://doi.org/10.1016/j.nimb.2024.165544  

   Bowen, Charles T; Byers, Todd A; Nook, Cory; Kaur_Saini, Darshpreet; Rout, Bibhudutta; Glass, Gary A (December 2024, Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms)

   Bogdanović; Iva; Lorenz, Katharina (Ed.)

   PIXE analysis was conducted on p8 fisher brand filter paper samples soaked in elemental standard solutions to determine the minimum detectable levels of Al, Si, P, S, Cl, K, Ca, Cr, Fe, Ni, Cu, and Se. All samples were analyzed with beam parameters of 2 µC incident charge, and beam current of less than 2 nA at 2 MeV beam energy. Minimum detectable levels were obtained by analyzing the x-ray spectrum in the GeoPIXE analysis package, and the data for each element would be averaged over all collected spectra. The minimum detectable level in parts per million was found to be on average 9.59 for Al, 4.6 for Si, 3.23 for P, 2.27 for S, 1.82 for Cl, 1.15 for K, 0.88 for Ca, 0.51 for Cr, 0.07 for Mn, 0.54 for Fe, 1.59 for Ni, 2.0 for Zn, 1.55 for Cu, and 6.5 for Se. Minimal deviation from the averaged values was observed, except in cases where samples contained high concentrations of elements with overlapping X-ray energies. 

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5. XRFast a new software package for processing of MA-XRF datasets using machine learning

   https://doi.org/10.1039/d2ja00114d  

   Vermeulen, Marc; McGeachy, Alicia; Xu, Bingjie; Chopp, Henry; Katsaggelos, Aggelos; Meyers, Rebecca; Alfeld, Matthias; Walton, Marc (October 2022, Journal of Analytical Atomic Spectrometry)

   X-ray fluorescence (XRF) spectroscopy is a common technique in the field of heritage science. However, data processing and data interpretation remain a challenge as they are time consuming and often require a priori knowledge of the composition of the materials present in the analyzed objects. For this reason, we developed an open-source, unsupervised dictionary learning algorithm reducing the complexity of large datasets containing 10s of thousands of spectra and identifying patterns. The algorithm runs in Julia, a programming language that allows for faster data processing compared to Python and R. This approach quickly reduces the number of variables and creates correlated elemental maps, characteristic for pigments containing various elements or for pigment mixtures. This alternative approach creates an overcomplete dictionary which is learned from the input data itself, therefore reducing the a priori user knowledge. The feasibility of this method was first confirmed by applying it to a mock-up board containing various known pigment mixtures. The algorithm was then applied to a macro XRF (MA-XRF) data set obtained on an 18th century Mexican painting, and positively identified smalt (pigment characterized by the co-occurrence of cobalt, arsenic, bismuth, nickel, and potassium), mixtures of vermilion and lead white, and two complex conservation materials/interventions. Moreover, the algorithm identified correlated elements that were not identified using the traditional elemental maps approach without image processing. This approach proved very useful as it yielded the same conclusions as the traditional elemental maps approach followed by elemental maps comparison but with a much faster data processing time. Furthermore, no image processing or user manipulation was required to understand elemental correlation. This open-source, open-access, and thus freely available code running in a platform allowing faster processing and larger data sets represents a useful resource to understand better the pigments and mixtures used in historical paintings and their possible various conservation campaigns. 

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