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Reference electrodes which demonstrate long-term potential stability are essential for many continuous monitoring applications and are commonly based on Ag‖AgCl electrodes; however, these electrodes are susceptible to poisoning from aqueous sulphide species which are commonly present in wastewater and natural groundwater. This work presents a sulphide resistant solid-state reference electrode (SSRE) based on a composite material using suspended KCl electrolyte and sacrificial AgCl in a cross-linked polyvinyl acetate polymer matrix. Sulphidation of the sacrificial AgCl produces a stable Ag2S precipitate and prevents further ingress of the poisoning sulphide species through the composite material. A novel SSRE using this material is compared to a control SSRE without suspended AgCl and a typical liquid filled reference electrode. These three reference electrodes are studied using electrochemical impedance spectroscopy (EIS), and their application is also studied in potentiometric pH sensing and cyclic voltammetry (CV). The long-term sulphide resistance of the two SSREs is also studied with potentiometry, and cross-sections of these electrodes were examined using micro X-ray fluorescence (μXRF). Both SSREs demonstrated higher impedance than the liquid reference electrode but were similar to other SSREs reported in the literature. This impedance did not result a meaningful difference in potentiometric pH sensing or CV experiments done using typical scan rates. The KCl/AgCl SSRE exhibited remarkable sulphide resistance, with all samples demonstrating a stable potential without maintenance after ca. 120 days of continuous immersion in 1 g L−1 Na2S solution, whereas KCl SSRE samples all demonstrated significant drift before this time. μXRF sulphur maps revealed that suspended AgCl prevented sulphide ingress, thus protecting the embedded Ag‖AgCl electrode. This work presents a reference electrode that could enable long-term monitoring in challenging sulphide solutions, and also highlights a novel approach for preventing reference electrode poisoning which could be more widely explored. [ABSTRACT FROM AUTHOR]

Bulk chemical composition analysis is a primary and pivotal analytical task for extraterrestrial geological samples (e.g., lunar soil, asteroids, or meteoroids). However, the analysis of mg-level consumption of extraterrestrial geological samples poses a significant challenge to existing analytical techniques. In this study, a new method was developed for trace element determination in bulk extraterrestrial geological samples prepared as lithium borate glasses, following customized procedures for X-ray fluorescence (XRF) analysis with a 10 mg sample by femtosecond LA-ICP-MS. All thirty-two trace elements in the borate glass samples were evenly distributed with homogeneity indices (H) < Hcrit. For the borate glass samples, Si was more suitable as an internal standard for Co, Ni, Cu, and Zn, whereas Al was better for the others. The femtosecond laser greatly reduces the influence of matrix effects, making calibration with non-matrix-matched external standards (NIST 612 and 614) accurate. Although the mass ratio of the flux and sample was as high as 35 : 1, the analytical results of 32 trace elements in six silicate rock reference materials (covering mafic to felsic rock types) using the developed technique were consistent with the reference values within 10% for most elements, and analytical precision (RSD) was also within 10% for most elements. Finally, one lunar basalt (NWA14526) and one shergottite (NWA13190) were assayed using the proposed method, and a comparison with the results of solution nebulization (SN)-ICP-MS confirmed the reliability of the method. The proposed method, combined with XRF, achieved the rapid, simultaneous, and accurate determination of major and trace elements in the same small sample aliquot (10 mg), which is highly suitable for the analysis of extraterrestrial geological samples. [ABSTRACT FROM AUTHOR]

The various analytical indices of coal are important criteria for evaluating the quality of commercial coal. Coals of different qualities exhibit different physical and chemical characteristics in their utilization. In the case of multiple coal types, the spectral characteristics of different coals may overlap within certain wavelength ranges, or be affected by interference or noise from other coal types, leading to low accuracy in coal quality prediction. Rapid and accurate coal quality testing is of great significance for improving industrial production efficiency and enhancing corporate profitability. This study employs near-infrared spectroscopy (NIRS) and X-ray fluorescence spectroscopy (XRF) combined techniques to explore the accuracy and feasibility of predicting coal quality based on coal type classification models. In terms of classification algorithms, coal samples are identified and classified using Support Vector Machine (SVM) based on fusion spectra. Regarding the modeling approach, Partial Least Squares (PLS) is utilized to establish both an overall model for all coal samples and individual classification models corresponding to each coal type. The results show that the precision, accuracy, recall, and F1 score of this classification algorithm reached 96.49%, 97.50%, 95.83%, and 96.41%, respectively. The determination coefficients (R2) for the classification model's predictions of ash, volatile matter, and sulfur in coal quality indicators reached 0.992, which represents improvements of 1.85%, 5.31%, and 10.10% over the overall model. The root mean square errors of prediction (RMSEP) for these indicators were 0.062, 0.080, and 0.008, showing reductions of 0.24%, 0.68%, and 0.05% compared to the overall model. It indicates that the method of first identifying the coal type and then predicting coal quality indicators using the corresponding classification model can significantly improve the accuracy of coal quality detection in complex coal type scenarios. [ABSTRACT FROM AUTHOR]

Imaging with multiple modalities can maximise the information gained from the analysis of a single sample. probes for optical fluorescence and X-ray fluorescence microscopy based on brominated 4-amino-1,8-naphthalimide and BODIPY scaffolds have been successfully designed and synthesised. Herein we show that these prototype probes, based on each of these scaffolds, can be imaged in two different cancer cell lines, and that the respective optical fluorescence and X-ray fluorescence signals are well correlated in these images. [ABSTRACT FROM AUTHOR]

The article reviews recent advancements in X-ray fluorescence (XRF) spectrometry, focusing on improvements in 3D chemical imaging and the development of specialized instruments. Topics include the progress in XRF spectrometry computed tomography, advancements in confocal XRF techniques, and the expanding applications of these methods in material science and cultural heritage.

Analyzing and rapidly screening the phenomenon of soil heavy metal exceedance remains a challenge for the fusion technology of X-ray fluorescence (XRF) and visible near infrared spectroscopy (visNIR). To address this, a new XRF-visNIR fusion method based on Gramian Angular Summation (GAS) transformation and Principal Component Analysis Network (PCANet) feature extraction is proposed. This method transforms XRF and visNIR data into two-dimensional images through GAS conversion, followed by feature extraction using PCANet. This reduces data dimensions and extracts important information about soil heavy metals. In the experimental phase, a large number of soil samples were collected from the Hongfeng Lake area and tested for spectral information using XRF-visNIR. By constructing and training a deep learning network, soil heavy metal pollution was classified and assessed. The results show that this method has achieved significant results in the analysis of soil heavy metal exceedance. The optimized GASF_PCANet_CNN can rapidly and accurately identify seven kinds of heavy metal pollution exceedance (Pb, Cd, As, Cr, Cu, Zn, and Ni). Deployed on an embedded platform, it can achieve quick feedback of screening results, with an average accuracy, average recall rate, average precision, and average F1 score of 95.07%, 95.90%, 95.17%, and 95.53%, respectively. The XRF-visNIR soil heavy metal analysis method based on GAS transformation and PCANet proposed in this study provides an efficient and reliable analytical means for monitoring soil heavy metal exceedance, actively promoting soil pollution management and environmental protection work. [ABSTRACT FROM AUTHOR]

Graphite demand is increasing rapidly due to the popularity of electric vehicles (EVs) and mobile devices. Lithium-ion batteries (LIBs) are the power source of EVs and mobile devices and the anodes of LIBs are mostly made of graphite, which is currently produced in an unsustainable and costly manner. For the first time, battery-grade biographite is produced from a renewable and sustainable precursor, biomass-derived pyrolysis oil, using iron as the graphitizing catalyst. Considering battery-grade graphite production at scale, pyrolysis oil offers several advantages as feedstock over biomass in terms of higher carbon density, low inorganics, easy handling, and low transportation cost. Catalytic graphitization of pyrolysis oil can be induced at low temperature (∼1100 °C). Herein, catalytic graphitization using iron powder was carried out under relatively moderate conditions (1500 °C) to reduce costs and environmental impact as compared to incumbent technologies. After graphitization, the residual catalyst was removed by refluxing the biographite + iron solid mixture with hydrochloric acid. The efficiency of iron removal was studied using the X-ray fluorescence (XRF) technique. The structural and morphological properties of biographites were assessed via X-ray diffraction (XRD), Raman spectroscopy, and high-resolution transmission electron microscopy (HR-TEM). The in situ XRD experiments provided a mechanistic understanding of the iron-catalyzed graphitization process. In addition, the effect of pyrolysis oil aging on the biographite crystalline structure was elucidated. The optimal biographite sample demonstrated impressive electrochemical performance with a reversible capacity of 350 mA h g−1, an initial coulombic efficiency of 90.2%, and minimal capacity loss over 100 cycles when applied as an LIB anode, thereby making pyrolysis oil-derived biographite potentially competitive with commercially available battery-grade graphite. [ABSTRACT FROM AUTHOR]

The need for elemental analysis in quality control of food, pharmaceuticals and cosmetics, taking into account human safety, has become an essential requirement for any product destinated for commercialization and targets in particular potentially toxic elements. The main objective is to obtain reliable data on elemental content that enables end-users to make informed decisions and to address problems effectively. To align with the principles of green and sustainable analytical chemistry, recent efforts have focused on incorporating new, low-cost screening and quantitative analytical tools such as total reflection X-ray fluorescence spectrometry (TXRF). This paper provides an overview of recent TXRF applications in the elemental analysis of food, pharmaceuticals, and cosmetic samples. The state-of-the-art procedures for sample preparation with details on the sample type and amount, dilution steps, mixing agents, analysed elements, and addition of internal calibration standards are presented together with analytical parameters such as limits of detection and quantification. The current challenges for applying TXRF to each of these research fields are discussed. [ABSTRACT FROM AUTHOR]

A simple and rapid method using an energy dispersive X-ray fluorescence (EDXRF) spectrometer is developed for facilitating the analysis of aqueous as well as organic process stream solutions from a zirconium extraction facility for the quantification of zirconium oxide (ZrO2) and hafnium oxide (HfO2). Except for dilution, not much sample preparation is involved in the analysis and accordingly the experimental parameters are optimized. Calibrations are performed separately using a set of synthetic standard solutions of zirconium and hafnium prepared in nitric acid medium for aqueous samples and in TBP + kerosene medium for organic samples. Studies are carried out to examine the effect of free acidity, sample matrix, and sample volume on determination of Zr and Hf and optimization of these parameters has been carried out accordingly for regular analysis. Zirconium oxide (ZrO2) and hafnium oxide (HfO2) are calculated from the results for reporting purposes. The results from the EDXRF method are compared with those of a gravimetric (phthalic acid precipitation) method with respect to zirconium and ICP-AES method with respect to Hf. On comparison, the results are found to be in good agreement. Detection limits for the developed method are found to be 0.06 g L−1 for ZrO2 and 0.03 g L−1 for HfO2. The developed method is employed for the determination of ZrO2 up to 200 g L−1 and HfO2 up to 5 g L−1 in various process stream solutions from a zirconium extraction facility. A RSD of less than 3% has been achieved for ZrO2 and less than 5% has been achieved for HfO2 in this method. [ABSTRACT FROM AUTHOR]

This study aimed to understand the distribution of lithium and other elements in the Beauvoir granite, which is a granite enriched in lithium (Li), tin (Sn), niobium (Nb) and tantalum (Ta), and develop a reliable method for lithium quantification. These granite samples underwent various mineral processing techniques to produce a large dataset with different lithium concentrations and different mineral matrices. Thin sections were analysed using optical microscopy and micro X-ray fluorescence imaging to determine the distribution of potassium (K), rubidium (Rb), and cesium (Cs) in different mineral phases. Both energy dispersive X-ray fluorescence (ED-XRF) and laser induced breakdown spectroscopy (LIBS) measurements were conducted using handheld instruments on the same samples. The different resulting spectra were used to develop a predictive model for lithium content according to the whole rock estimates. Principal component analysis (PCA) and biplot analysis were used to identify correlations between elemental composition and lithium content; multiple linear regression was employed to establish a regression model for lithium quantification. Residual analysis and various statistical metrics, including R2, root mean squared error, and mean absolute percentage error, were used to assess the accuracy and performance of the models. In summary, this study highlights that for high lithium grade samples, ED-XRF can be preferred over LIBS as it requires less time for both sample preparation and analysis. However, this lithium assay is indirect, whereas for low lithium grade samples (<0.8 wt% Li2O), LIBS should be preferred to ensure a robust lithium assay method. [ABSTRACT FROM AUTHOR]

In X-ray fluorescence (XRF) analysis, which has been used to analyze elements in various samples, it is important to decrease the background (BG) intensity. Generally, BG signals are reduced by inserting metal foils of various types and thicknesses between the X-ray tube and sample as primary X-ray filters. In this study, we developed a unique gas filter-XRF (GF-XRF) instrument to easily reduce the BG effect by changing the pressure of the gas to ensure that the absorption edge of the gas element is slightly lower than the energy of the XRF peak of the target element. The advantage of using a gas is that the gas pressure can be altered easily. To evaluate the performance of this instrument, Ti and Zr were selected as target elements, and Ar and Kr were selected as the filtering gases. When the XRF spectra of the Ti sample were recorded using the Ar gas filter, as the Ar gas pressure increased, the background signal in the energy region of the Ti Kα peak decreased, resulting in an increase in the signal-to-noise ratio (SNR) of that peak. When the Kr gas filter was used, both the SNR and the minimum detection limit of Zr improved. These results demonstrate that the unique GF-XRF instrument is useful for high-sensitivity analyses. [ABSTRACT FROM AUTHOR]

Rhenium(I) tricarbonyl complexes are widely studied for their cell imaging properties and anti-cancer and anti-microbial activities, but the complexes with S-donor ligands remain relatively unexplored. A series of six fac-[Re(NN)(CO)3(SR)] complexes, where (NN) is 2,2′-bipyridyl (bipy) or 1,10-phenanthroline (phen), and RSH is a series of thiocarboxylic acid methyl esters, have been synthesized and characterized. Cellular uptake and anti-proliferative activities of these complexes in human breast cancer cell lines (MDA-MB-231 and MCF-7) were generally lower than those of the previously described fac-[Re(NN)(CO)3(OH2)]+ complexes; however, one of the complexes, fac-[Re(CO)3(phen)(SC(Ph)CH2C(O)OMe)] (3b), was active (IC50 ∼ 10 μM at 72 h treatment) in thiol-depleted MDA-MB-231 cells. Moreover, unlike fac-[Re(CO)3(phen)(OH2)]+, this complex did not lose activity in the presence of extracellular glutathione. Taken together these properties show promise for further development of 3b and its analogues as potential anti-cancer drugs for co-treatment with thiol-depleting agents. Conversely, the stable and non-toxic complex, fac-[Re(bipy)(CO)3(SC(Me)C(O)OMe)] (1a), predominantly localized in the lysosomes of MDA-MB-231 cells, as shown by live cell confocal microscopy (λex = 405 nm, λem = 470–570 nm). It is strongly localized in a subset of lysosomes (25 μM Re, 4 h treatment), as shown by co-localization with a Lysotracker dye. Longer treatment times with 1a (25 μM Re for 48 h) resulted in partial migration of the probe into the mitochondria, as shown by co-localization with a Mitotracker dye. These properties make complex 1a an attractive target for further development as an organelle probe for multimodal imaging, including phosphorescence, carbonyl tag for vibrational spectroscopy, and Re tag for X-ray fluorescence microscopy. [ABSTRACT FROM AUTHOR]

Coffee, one of the top ten most globally traded commodities, can be grown in different agricultural regions presenting peculiar characteristics. Thus, the determination of inorganic nutrients in ground-roasted coffee has nutritional relevance and allows authentication as well as tracking of the origin of the product. Analytical methods routinely used to determine inorganic nutrients in food are based on time-consuming steps that involve acid digestion followed by elemental quantification. In this study, analytical strategies for the direct determination of inorganic macronutrients (P, K, Ca, and Mg) in commercial samples of ground-roasted coffee were evaluated by portable X-ray fluorescence spectrometry (pXRF) using two commercial spectrometers. Two calibration approaches were tested: (i) using plant-based certified reference materials (CRMs); (ii) matrix-matched calibration, using samples of ground-roasted coffee as well as the corresponding solid residues resulting from infusion, the spent coffee grounds (SCG). The analyte mass fractions in these samples were determined by the reference method (decomposition using HNO3 and H2O2 followed by ICP OES measurements). In the latter method, calibration models were built using reference values and analytical signals obtained by pXRF. Pressed pellets from the samples (CRMs, ground-roasted coffee, and SCG) were prepared. The samples were irradiated for 50 s using both spectrometers under two experimental conditions: X-ray tube voltage of 15 kV and 50 kV. Calibration models with excellent linearity were obtained (r ≥ 0.90) for all analytes. The proposed matrix-matched calibration method was considered the most accurate approach for elemental determination (mainly P and K) in samples of ground-roasted coffee as well as in the spent coffee grounds, an alternative agricultural fertilizer. [ABSTRACT FROM AUTHOR]

Metal–organic frameworks (MOF) are a subclass of porous framework materials that have been used for a wide variety of applications in sensing, catalysis, and remediation. Among these myriad applications is their remarkable ability to capture substances in a variety of environments ranging from benign to extreme. Among the most common and problematic substances found throughout the world's oceans and water supplies is [UO2]2+, a common mobile ion of uranium, which is found both naturally and as a result of anthropogenic activities, leading to problematic environmental contamination. While some MOFs possess high capability for the uptake of [UO2]2+, many more of the thousands of MOFs and their modifications that have been produced over the years have yet to be studied for their ability to uptake [UO2]2+. However, studying the thousands of MOFs and their modifications presents an incredibly difficult task. As such, a way to narrow down the numbers seems imperative. Herein, we evaluate the binding behaviors as well as identify the specific binding sites of [UO2]2+ incorporated into six different Zr MOFs to elucidate specific features that improve [UO2]2+ uptake. In doing so, we also present a method for the determination and verification of these binding sites by Anomalous wide-angle X-ray scattering, X-ray fluorescence, and X-ray absorption spectroscopy. This research not only presents a way for future research into the uptake of [UO2]2+ into MOFs to be conducted but also a means to evaluate MOFs more generally for the uptake of other compounds to be applied for environmental remediation and improvement of ecosystems globally. [ABSTRACT FROM AUTHOR]

Works of art, especially paintings, often involve successive layers of materials of diverse compositions and properties. The current study explores the potentialities of the combination of femtosecond Laser Induced Breakdown Spectroscopy (fs-LIBS) and Macro X-ray Fluorescence (MA-XRF) for the study of paintings. In this framework, two mock-up samples that were manufactured by using traditional post-byzantine painting methods and numerous inorganic and organic pigments was thoroughly investigated. The selected pigments were either applied as single paint layers on top of a calcium carbonate substrate or as a combination of two and/or three successive paint layers over the substrate. The authors attempt to disclose the layered structure of the paints by inspecting some carefully chosen LIBS spectral features (markers) as a function of the laser shot number. The various paint layers are identified by applying less than ten laser shots while the diameter of the induced craters is limited to under 100 μm. Additionally, it is shown that through the proper treatment and evaluation of complementary MA-XRF data, one may overcome some of the fs-LIBS limitations and thoroughly investigate a painting's stratigraphy. Given the micro-invasive character of fs-LIBS and the non-invasive character of MA-XRF, combining the selected analytical methods is a promising tool for painting investigation. [ABSTRACT FROM AUTHOR]

Multimodal imaging and spectroscopy like concurrent scanning transmission X-ray microscopy (STXM) and X-ray fluorescence (XRF) are highly desirable as they allow retrieving complementary information. This paper reports on the design, development, integration and field testing of a novel in situ atomic force microscopy (AFM) instrument for operation under high vacuum in a synchrotron soft X-ray microscopy STXM–XRF end-station. A combination of μXRF and AFM is demonstrated for the first time in the soft X-ray regime, with an outlook for the full XRF–STXM–AFM combination. [ABSTRACT FROM AUTHOR]

An accurate and effective analytical method to measure the concentration of heavy metal elements (HMEs) in soil is of great significance for combating soil pollution, repairing ecosystems, and guiding agricultural cropland. Nondestructive, rapid, and in situ measurements make X-ray fluorescence spectroscopy (XRF) a popular tool for analyzing HMEs in soil. However, due to complex matrix effects and spectral line interference, the analytical accuracy is limited. In this study, an effective deep learning method is proposed to accurately determine the concentration of HMEs in soil by combining with XRF. Firstly, soil spectra are acquired based on a handheld energy-dispersive X-ray fluorescence spectrometer (ED-XRF). Secondly, depending on the spectral continuity, cross-space correlation, and local correlation of XRF, a feature mining coordination (FMC) module is proposed. The FMC module is composed of a global spectral attention (GSA) module and a local multiscale feature extraction (LMSFE) module, and it can simultaneously pay overall attention to the spectrogram and local feature modeling. Finally, a deep spectral prediction network (DSPFormer) is proposed based on the FMC module to achieve an accurate estimation of the concentration of five HMEs (Ti, Mn, Cu, Zn, and Pb). The effectiveness of the method is demonstrated by comparing it with other advanced soil analysis algorithms. The coefficients of determination of DSPFormer for five HMEs (Ti, Mn, Cu, Zn, and Pb) are 0.9559, 0.9627, 0.9658, 0.9584, and 0.9664, respectively. The results indicate that DSPFormer effectively mitigates the matrix effect and spectral line interference present in XRF. In summary, the deep learning method based on self-attention and convolutional neural networks (CNNs) provides new theoretical guidance for soil HME analysis. [ABSTRACT FROM AUTHOR]

Wavelength dispersive X-ray fluorescence (WD-XRF) spectroscopy is a widely used instrumental spectroscopy method for a variety of samples and it requires no or a minimal of sample preparation. Compared to using the more conventional energy dispersive XRF spectrometers, WD-XRF spectroscopy has the advantage of a highly improved energy resolution, making this technique suitable for the detection of characteristic X-rays separated by only a few eV. WD-XRF is established both in industry for routine analyses and in the academic world for high-end research and development of the method. WD-XRF spectroscopy can be probed with a laboratory X-ray source and if higher X-ray-beam intensities, monochromatic and polarized beams are required a synchrotron X-ray radiation source can be used. In (synchrotron) radiation facilities all over the world interesting new setups are characterized and used. This review paper will give an overview of the important characteristic properties, such as energy resolution, sensitivity, and the reachable energy range of the different types of WD-XRF setups available and reported in the last 20 years, based on laboratory X-ray sources, radiation facility sources or availability as a commercial instrument. The commercially available instruments are listed by the manufacturer and type of instrument, and for each instrument at least one example of an application is shown. [ABSTRACT FROM AUTHOR]

In this work, nanogels based on polyetheramine–epoxide (PPO–DPEG) were developed, and two potential envisions for the nanocarriers were explored, for the first time, as controlled drug delivery systems and as stimulants for seed germination in agriculture. These nanogels can help reduce the use of hazardous chemicals and expensive instruments, with several advantages: (i) an easy route for their production; (ii) catalyst-free synthesis at low temperature; (iii) water as a unique solvent, which provides advantages in terms of sustainability and cost-effectiveness. First, this study provides a particular drug-polymer design for the sustained release of the antibiotic ciprofloxacin. Second, seed germination assays demonstrated that the nanogels could be used as novel materials to promote the germination and growth (root and shoot) of cucumber seeds. In this sense, the root length of cucumber seeds was improved by 39% compared to the negative control (water). The obtained polymeric gels were in depth characterized by various physicochemical techniques such as dynamic light scattering (DLS) and zeta potential, thermogravimetric analysis (TGA), transmission electron microscopy (TEM), ultra-small angle X-ray scattering (USAXS), and X-ray fluorescence spectroscopy (μ-XRF). The cytotoxicity of PPO–DPEG gels was evaluated by XTT assays, confirming good biocompatibility after exposure to GM07492A cell line (normal human lung fibroblasts). The possibility to use these nanogels for agricultural science is novel, providing good carrier opportunities for embedded micronutrients (and mass production) for the development of an innovative and highly versatile platform for sustainable delivery systems in plants. [ABSTRACT FROM AUTHOR]

The document is a contents list for the Journal of Analytical Atomic Spectrometry. It provides information about the articles and papers included in the journal's latest issue. Topics covered include advances in environmental analysis, managing transition metal interferences, stability of iron single atoms in graphene structures, and various other topics related to atomic spectrometry. The journal is published by The Royal Society of Chemistry, a leading chemistry community. [Extracted from the article]

Total reflection X-ray fluorescence (TXRF) analysis is used to determine the concentrations of trace elements in liquid samples. Solutions are generally added dropwise onto a flat substrate and dried. When liquids with low elemental levels are analyzed, a hydrophobic substrate is employed to prepare a small dome-shaped residue. In the analyses of high-level solutions (high elemental concentration solution), the quantitative accuracy and measurement sensitivity deteriorate because X-ray fluorescence (XRF) is absorbed inside the thick residue. By preparing a thin residue, it is possible to reduce this absorption effect. In our previous study, atmospheric pressure plasma jet (APPJ) treatment was used to prepare a superhydrophilic substrate. The time that super-hydrophilicity is maintained is not yet known and determining this value is important for performing TXRF analysis. Therefore, we measured the contact angles of the droplets and performed X-ray photoelectron spectroscopy of the substrate surfaces to determine the relationships between the experimental values and the elapsed time. The optimal APPJ-treated area has not yet been determined and is one of the most important factors for high-sensitivity analysis. In this study, we controlled the APPJ-treated area by preparing four polytetrafluoroethylene (PTFE) masks with different hole diameters and obtained recoveries, relative standard deviations, and minimum detection limits (MDLs) to identify the optimal APPJ-treated area. A PTFE mask with a diameter of 4 mm generated the best values, particularly for low-Z elements. By controlling the diameter of the dried residue to 4 mm, the MDL value in aluminum was improved by a factor of 1.7. [ABSTRACT FROM AUTHOR]

Chemical Society Reviews is a journal published by The Royal Society of Chemistry that focuses on the chemical sciences. The journal publishes articles on various topics of interest in the field, including biodegradable alternatives to polymers, asymmetric electrocatalysis, synthesizing biomaterials in living organisms, and visible light-responsive materials. The articles aim to be accessible, succinct, and reader-friendly, promoting international and multidisciplinary awareness and cooperation. The journal also provides information for authors on how to submit material for publication. [Extracted from the article]

Presented here is the first demonstration of supervised discretization to ‘declutter’ multivariate classification data in chemical sensor applications. The performance of multivariate classification models is often limited by the non-informative chemical variance within each target class; decluttering methods seek to reduce within-class variance while retaining between-class variance. Supervised discretization is shown to declutter classes in a manner that is superior to the state-of-the-art External Parameter Orthogonalization (EPO) by constructing a more parsimonious model with fewer parameters to optimize and is, consequently, less susceptible to overfitting and information loss. The comparison of supervised discretization and EPO is performed on three classification applications: X-ray fluorescence spectra of pine ash where the pine was grown in three distinct soil types, laser induced breakdown spectroscopy of colored artisanal glasses, and laser induced breakdown spectroscopy of exotic hardwood species. [ABSTRACT FROM AUTHOR]

Major and trace elements in polymetallic minerals are crucial subjects of study in ore geology. However, efficient and convenient methods have yet to be developed for processing high-density and hydrophobic sulfide ores, like copper–nickel sulfide ore, which is a strategic metal resource. In this paper, a novel method for the multielement analysis of copper-nickel sulfide ore using total reflection X-ray fluorescence spectrometry (TXRF) combined with slurry sampling was developed. This approach is based on the preparation of a suspension in a liquid at a certain viscosity without sample digestion. The effects of the sample amount and particle size on the analysis results were evaluated. The method validation was achieved through these processes: comparison with inductively coupled plasma atomic emission spectrometry (ICP-AES) results; analysis of certified reference materials, including copper-nickel cobalt ore and cobalt-rich nodule. The dispersants of ethylene glycol and 1% Triton X-100 applied in sulfide ore were also evaluated. For most elements, the relative error and relative standard deviation were under 10% in ethylene glycol. By contrast, suspensions with 1% Triton X-100 showed poorer reproducibility, particluarly in samples with sulfide-rich content, requiring further enhancement. For high-specific gravity sulfide ores, a dispersant of ethylene glycol is recommended to enhance the result of accuracy and reproducibility. [ABSTRACT FROM AUTHOR]

The insufficient spectral resolution of the energy-dispersive total-reflection X-ray fluorescence spectrometer (TXRF) makes the quantitative determination of U and Th with the use of Lα lines a challenging task, especially in the case of analysis of natural solid samples with low concentrations of these elements. In this study, we used the PLS regression and the spectrum deconvolution to overcome the peak overlap problem (Th Lα overlaps with Fe Kα + Fe Kα and Rb Kα; U Lα overlaps with Rb Kα and Sr Kα). Samples of rocks, ashes, and ores with different amounts of interfering elements (Rb, Sr, and Fe) were prepared as suspensions for the direct TXRF determination of U and Th. We applied the PLS regression coupled to the special design of experiment (DoE) based on the low-correlated concentrations of U, Th, and Rb in the calibration set of samples. To validate our combination of the DoE, preprocessing procedure and PLS models, we used the independent test sets of the Rb-rich and Rb-depleted samples. Our study revealed that in the presence of considerable line overlapping the deconvolution leads to a significant systematic error (30–60%) within the low concentration range (10–30 ppm), while the PLS regression reduces the systematic error by 2–3 times. Additionally, we demonstrated that the borderline of the semi-quantitative and quantitative determination for the PLS regression is 3 and 10 times lower, respectively. We found that the high RSD of the Th concentrations found by the deconvolution reflects not a poor repeatability of the TXRF measurements, but a poor stability of the deconvolution. The application of the Rb-depleted test set helped us to demonstrate that the PLS regression does not improve the accuracy in the absence of strong line overlapping. In this study, we conclude that the best strategy for the determination of U in the Rb-depleted samples is the implementation of the deconvolution, while the determination of Th and U in the Rb-rich samples is best carried out with the PLS regression. [ABSTRACT FROM AUTHOR]

Obtaining accurate characteristic spectra and the net peak area is crucial in X-ray fluorescence (XRF) quantitative analysis. To improve the calculation accuracy of the net peak area of the characteristic X-ray spectrum, carrying out the baseline calibration is necessary before resolving the spectrum. First, this article proposes the use of an enhanced generative adversarial network (EGAN) depth network model for baseline calibration of XRF spectra. This method directly takes a clean spectrum after deducting the background as the optimal target for EGAN deep network training, thus eliminating the traditional cumbersome baseline fitting process. It can directly and quickly obtain the XRF spectrum after baseline calibration, and the entire process requires neither fitting the background nor knowing the background data. Further, to improve the generalization ability of the EGAN model, we introduce transfer learning in the model training process. We use the existing alloy sample spectrogram data to pretrain the EGAN model generator and then migrate the pretrained XRF-EGAN generator model to the baseline calibration task of 57 national standard soil sample spectra collected. During the model training process, a cross-validation method is used to train and test the model's effectiveness. Finally, experiments are conducted on simulated spectra and actual XRF spectra to verify the accuracy and adaptability of the proposed method. Instrument calibration curves for peak counting and element concentration are established to verify the effectiveness of baseline calibration. The coefficient of determination R2 of the calibration curves for elements Cu, Zn, Mn and Cr is increased to 0.998, 0.988, 0.922 and 0.991, respectively. The results indicate that our proposed method can effectively estimate the pure spectrum after deducting the background. In addition, this method can also be applied to the baseline correction of other similar spectral signals. [ABSTRACT FROM AUTHOR]

Accurately and timely determining the calorific value of coal is essential for optimizing the washing and processing procedures in coal preparation plants and achieving maximum economic benefits. In this study, a rapid coal calorific value analyzer was developed by combining near-infrared spectroscopy (NIRS) and X-ray fluorescence spectroscopy (XRF), and industrial testing and performance evaluation were conducted at a coal preparation plant. This article focuses on the structure, operational process, analysis model, and industrial testing of a rapid NIRS-XRF coal calorific value analyzer. This analyzer consists of six parts: NIRS module, XRF module, sample delivery module, control module, hydrogen production module, and operation software. We proposed a holistic-segmented model quantitative analysis algorithm based on partial least squares regression (PLSR). The analyzer was used to measure the calorific values of four types of product coals, namely, clean coal, middling coal, slime and gangue, from the daily production of the Duanshi coal preparation plant, and compared with the assay results. The test results showed that the root mean square error of prediction (RMSEP), the average absolute error (AAE) and the average relative error (ARE) of the prediction of the coal calorific value by using the holistic-segmented model decreased from 0.65 MJ kg−1, 0.55 MJ kg−1 and 4.71% of the traditional holistic model to an average of 0.33 MJ kg−1, 0.28 MJ kg−1 and 2.71%, respectively, a decrease of nearly double. The average standard deviation (SD) also decreased from 0.29 MJ kg−1 to 0.09 MJ kg−1, which met the Chinese national standard requirement of less than 0.12 MJ kg−1. Our proposed model significantly improved the accuracy and repeatability of the measurements. Furthermore, the measurement results of this analyzer showed good consistency with traditional chemical analysis results, indicating its potential for widespread application in industries such as coal mining, washing, power generation, and coking, among others. Therefore, the rapid NIRS-XRF coal calorific value analyzer provides a new intelligent means for the clean and efficient utilization of coal. [ABSTRACT FROM AUTHOR]

In vivo X-ray fluorescence spectrometry is a technique used to quantify trace elements in bone tissue by using an external γ-ray source to irradiate a section of the body. The current system for bone metal quantification is capable of detecting metals such as strontium in human bone by using an 125I radionuclide excitation source with photon detection using a Si(Li) detection system. The historical choice of phantom material, plaster of Paris, has introduced numerous complications in terms of its suitability for the calibration of systems of bone metal quantification. A chemically pure hydroxyapatite phantom material of known composition and stoichiometry has been developed for the purpose of calibrating systems of bone metal quantification as a replacement for plaster of Paris while also removing the required coherent conversion (correction) factor from the protocol. Hydroxyapatite, however, is not necessarily stable over time and thus this study assessed common calcium phosphate glasses prepared with metal oxides as potential candidate calibrators. Monte Carlo simulations were carried out using the bench-marked EGS5 code to evaluate if calcium phosphate glasses can be used in the same fashion as hydroxyapatite. Selected glasses were assessed for their differential cross-sections for coherent scattering as well as the implications on the correction factor. The calcium phosphate glasses did not show much agreement with hydroxyapatite when comparing the slopes of the normalized calibration curves nor when directly calculating the correction factor from the ratios of the differential coherent scattering cross-sections for bone mineral to the glass phantoms. This simulation study thus concludes that the removal of the correction factor from the calibration protocol requires the use of a hydroxyapatite phantom and that conventional calcium phosphate glasses, although desirable, cannot be used reliably as calibrators. [ABSTRACT FROM AUTHOR]

Monochromatic excitation X-ray fluorescence (ME-XRF) spectrometry is an emerging technique for elemental analysis with many potential forensic applications. In this study, a method for rapid and efficient quantification of As, Hg, Tl, and Pb in biological tissues using ME-XRF was established and applied to actual cases. The instrument's operating conditions were optimized, and the optimum method for sample preparation was determined. According to the calibration curves of each element, a new model for the detection of biological tissues was developed based on the fundamental parameter (FP) method. The LOD values of As, Hg, Tl, and Pb were 0.04, 0.04, 0.06, and 0.07 μg g−1, respectively. Through systematic evaluation and validation, the feasibility of using ME-XRF in forensic toxicology analysis was confirmed. [ABSTRACT FROM AUTHOR]

Characterization of microscopic details of the fabric of mudstones and shales (i.e., structure and composition) is important to understand their storage and transport properties. Current characterization methods struggle to probe reliably multiple scales of interest (e.g., pore and fracture) and measure properties at the finest resolution under representative in situ conditions. Micro X-ray fluorescence (μXRF) is a high-performance imaging technique that produces elemental images at sub-10 μm spatial resolution and could offer insight into a diversity of shale properties, such as mineral composition, porosity, and in situ pressure gradients. This study designed and carried out a porosity mapping protocol using model and real-rock microfluidic devices and contrast fluids. Etched silicon micromodels with real-rock pore network patterns served as ideal models to establish a proof of concept. Measurements were performed on a novel μXRF microscope not powered by synchrotron radiation. We registered the μXRF datasets with the binary rock masks used for micromodel fabrication and applied segmentation algorithms to compare porosities. We assessed expected advantages and limitations through a sensitivity analysis and beam study. μXRF is an important new imaging technique for microfluidic applications. [ABSTRACT FROM AUTHOR]

Even though measurements in cryogenic conditions are the preferred choice for single-cell and plant analyses to reduce sample preparation artefacts, preserve the integrity of samples and prevent loss of some chemical elements, cryo-fixation of human or animal soft tissue for X-ray fluorescence analysis still remains challenging. Here we report, compare and discuss analyses of bovine ovarian tissues performed in cryogenic or room temperature conditions, the latter both chemically fixed or freeze-dried. [ABSTRACT FROM AUTHOR]

The utilisation of SR sources has significantly enhanced the analytical capabilities of XRF spectrometry techniques. With the latest generation of facilities, SR-XRF spectrometry achieves remarkably high nm-scale resolution with excellent LODs at ppb levels. A noteworthy trend is the increasing use of SR-XRF spectrometry together with other X-ray spectroscopic and imaging techniques. This provides complementary information on elemental speciation as well as structural and morphological characteristics of samples. Sub-μm SR-XRF spectrometry has been extensively applied in diverse fields such as environmental and planetary studies, biomedical research, materials science and cultural heritage investigations. Methods for handling the huge datasets produced by macroXRF spectrometry have become essential for processing and classifying the element distributions collected from the analysis of paintings. Machine-learning-based correlations of element maps have been developed for the automatic identification of patterns as an alternative method of processing macroXRF spectrometry data from cultural heritage samples. The microanalytical capabilities of TXRF spectrometry have led to a steep increase in applications to biomedical problems with successful analyses of minute amounts of samples (ca. 20 mg) of, e.g., blood, placenta and heart tissue. The suspension-assisted preparation of theses samples and of mineralogical materials was improved in many studies by extending the common internal standard calibration with uni- and multivariate approaches. The development of a scan-free grazing-exit XRF spectrometer improved accuracy in the analysis of periodic surface structures. The degree of protonation of different thiol- or hydroxyl-bearing organic monolayers was successfully determined using grazing-incidence XRF and TXRF spectrometries in combination with other techniques. [ABSTRACT FROM AUTHOR]

A highly sensitive analytical method for elemental quantification of U in (Th1−xUx)O2 mixed oxide (MOX) fuel pellets is extremely necessary for nuclear fuel quality control. It will be an added advantage if the analytical method is direct and non-destructive. Presently we have demonstrated a direct non-destructive methodology for (Th1−xUx)O2 MOX fuel pellets by the X-ray fluorescence technique using U/Th M lines as the analytical line instead of the well resolved U/Th Lα1 lines. U/Th M lines were selected as they could be excited using low-energy excitation. In the present study, we have used seven (Th1−xUx)O2 MOX fuel pellets with varying U/Th concentrations. All the MOX pellets were prepared via the sol–gel micro-sphere pelletization (SGMP) process. All the pellets were presented for μ-XRF measurements. Each pellet was measured at 10 different spots to construct an input data set. Analytical parameters like relative error and precision obtained from the classical FP-based method utilizing U/Th M lines are 22.4% and 4.9%, respectively. To improve the same parameters, we employed a classical chemometric method like partial least square regression (PLSR). It produced the above-mentioned analytical parameters ∼3.0%. Furthermore, an optimized ANN-based modeling methodology generated a relative error and precision for the U determination in the test sample of 3.1% and 4.9%. The comparative study suggests that both the ANN-based methodology and PLSR outperform the classical FP-based methodology for the analytical quantification of U/Th in MOX by employing U/Th M lines as the analytical line. [ABSTRACT FROM AUTHOR]

A reliable X-ray fluorescence (XRF) analysis can be achieved by using accurate knowledge of the spectral distribution of the excitation source. Since most commonly employed X-ray sources emit broadband polychromatic radiation, it is essential to fully characterize the source in order to calculate its spectrum under varying operational settings. By using the calculable synchrotron radiation from a bending magnet, this work shows that a reference-free XRF approach can be implemented for polychromatic excitation conditions. It is further demonstrated that this physically traceable approach is capable of quantifying thin layers as well as stratified materials. The same principles may readily be transferred to similar polychromatic X-ray sources, such as XRF instruments based on an X-ray tube. [ABSTRACT FROM AUTHOR]

Soil is an important source of potentially toxic metal intake for humans through the food chain. Accurate determination of elemental concentrations in soil is of great significance to safeguard human health, thereby requiring reliable detection techniques. This work aimed to propose a new quantitative analysis method that combines pre-processing and concentration prediction. A total of 59 soil samples were collected and subjected to laboratory analyses and energy dispersive X-ray fluorescence (ED-XRF) scans. Firstly, a novel pre-processing method based on iterative adaptive window empirical wavelet transform and Gaussian convolution (IAWEWT-GC) was proposed, which can identify and remove spectral background. Secondly, the instrument calibration curves of peak counts and elemental concentrations were established and the pre-treatment effect was verified by comparison with other common methods. The instrument calibration curves' coefficient of determination (R2) was enhanced to 0.988, 0.962, and 0.980 for Cr, Mn, and Cu, respectively. Finally, to accurately detect the elemental concentration, a hierarchical deep neural network (HDNN) was designed for estimating potentially toxic metals, enabling quantitative regression of multiple elements simultaneously. The proposed method was evaluated and compared with conventional algorithms (DNN, BPNN, and PLSR). The HDNN model achieved the highest R2 of 0.943, 0.983, and 0.979 for Cr, Mn, and Cu, respectively, with the lowest RMSEP of 5.74, 84.45, and 29.17, and lowest MAE of 4.51, 78.51, and 16.35, indicating its ability to enable simple, rapid, and effective quantitative analysis of multiple elements. Overall, our proposed method provides a new option for the elemental analysis of samples rich in potentially toxic metals. [ABSTRACT FROM AUTHOR]

Precision fertilization is crucial for sustainable and efficient world farming. However, traditional chemical mineral analyses are costly and time-consuming, which limits the amount of data that can be collected and the frequency of updates to fertilization programs. Hence, we set to restructure mineral diagnostics in agriculture. First, we developed a unique experimental setup and produced ∼2000 leaf samples at a wide concentration range of macronutrients (0.5–10 mg P per g, 1–50 mg K per g, or 2–40 mg Ca per g) and trace elements (0.05–0.4 mg Mn per g, 0.05–0.4 mg Fe per g, or 0.005–0.06 mg Zn per g) in multiple crops. We scanned the foliar samples by X-ray fluorescence and determined that emissions at the 1–10 keV range resonate with macronutrients and trace elements in plants. While initial correlations for trace elements were unsatisfactory for trace elements (R2 < 0.7), we applied a range of statistical tools to support chemical spectroscopy for the mineral matrices in leaves. The chemometric investigation developed into crop-specific predictive models that fitted chemical analyses of mineral concentrations by R2s of ∼0.9 and an RMSE of 5% of the concentration range. We also developed a single model for all the crops but concluded that it often critically reduced the accuracy (a fourfold increase in RMSE) of the mineral analyses. Nonetheless, the XRF-PLSR approach is rapid and significantly more cost effective than traditional laboratory essays. Thus, developing models for numerous crops is feasible, and XRF tools could integrate mineral analyses in decision support tools for farming. [ABSTRACT FROM AUTHOR]

In this study, the hyphenation of gas chromatography with ICP tandem mass spectrometry (GC-ICP-MS/MS) for the analysis of volatile organochlorides in complex hydrocarbon samples is described. H2 was used as a reaction gas in the MS/MS collision reaction cell in order to bring selectivity and sensitivity towards chlorine detection and the gas flows feeding the plasma were carefully optimized to limit the interferences due to coeluting hydrocarbons. The response towards organochlorides proved to be linear, equimolar against elemental chlorine on a large dynamic range with quantification limits in the range of 30–100 μgCl L−1 per peak. This compound independent calibration (CIC) method was successfully applied to the analysis of light petroleum samples as well as plastic pyrolysis oil light cuts with a good agreement with other total elemental chlorine analysis techniques such as X-ray fluorescence, thus attesting the strength of this technique for the untargeted analysis of organochlorides in complex hydrocarbon volatile samples. [ABSTRACT FROM AUTHOR]

BiTa7O19:Er3+/Yb3+/Sb phosphors were successfully synthesized by high temperature solid sintering. X-ray diffraction (XRD), fluorescence spectrometry and X-ray photoelectron spectroscopy (XPS), were used to analyze the phase structure, upconversion luminescence (UCL) features and Sb valence state, respectively. The results suggest that polyvalent Sb with Sb3+ and Sb5+ can replace the Ta5+ sites in a BiTa7O19 host to form a pure phase. Compared with BiTa7O19:0.1Er3+/0.4Yb3+, polyvalent Sb doping further improves UCL intensity by 1.2 times under 980 nm laser stimulation with a powder density of 44.59 W cm−2. This is due to the adjustment of the local lattice structure of BiTa7O19 by the polyvalent Sb. The maximum absolute sensitivity (SA) and relative sensitivity (SR) can be estimated from the UCL variable-temperature spectra as 0.0098 K−1 at 356 K and 0.0078 K−1 at 303 K using the luminescence intensity ratio (LIR) approach. The outcomes show that host local lattice adjustment using polyvalent elements is an effective way to improve luminescence intensity, and it is possible to use BiTa7O19:Er3+/Yb3+/Sb as a temperature sensor. [ABSTRACT FROM AUTHOR]

X-ray fluorescence spectrometry (XRF) is a technique that allows determining non-destructively the composition of elements within a sample. Focussing the excitation X-ray beam to a small spot that is moved in the x–y-direction relative to the sample adds lateral information. Such a two-dimensional micro-X-ray fluorescence (2D µ-XRF) spectrometer for desktop use is commercially available providing a resolution down to approximately 10 µm. With a µ-XRF spectrometer, it is inexpensive to take many scans of the same sample. With super-resolution methods, these can potentially be combined into a higher-resolution image. As a prerequisite, the misalignments of multiple scans (shifts and rotation) in the subpixel range have to be detected. We present a method for image registration of multiple images based on expander graphs that provides adjustable tradeoffs between registration quality and running time. We evaluate the algorithms on artificial and real µ-XRF data and we argue that our findings show that subpixel information is present in real µ-XRF data. This is a necessary condition for the applicability of multi-image super-resolution techniques to µ-XRF data in future work. [ABSTRACT FROM AUTHOR]

Using radiometrically calibrated instrumentation of the Physikalisch-Technische Bundesanstalt, the L-shell fluorescence yields and Coster–Kronig factors of tantalum (including the uncertainty budget) were experimentally determined based on transmission and X-ray fluorescence experiments. The determined fluorescence yields (ωL3 = 0.247(12), ωL2 = 0.278(15), ωL1 = 0.157(12)) were independently validated through XRR-GIXRF experiments. Both the Coster–Kronig factors (f23 = 0.123(84), f13 = 0.328(152), f12 = 0.14(11)) as well as the fluorescence yields are in good agreement with the most established databases in the field of X-ray fluorescence. [ABSTRACT FROM AUTHOR]

X-ray fluorescence spectroscopy (XRF) plays an important role for elemental analysis in a wide range of scientific fields, especially in cultural heritage. XRF imaging, which uses a raster scan to acquire spectra pixel-wise across artworks, provides the opportunity for spatial analysis of pigment distributions based on their elemental composition. However, conventional XRF-based pigment identification relies on time-consuming elemental mapping facilitated by the interpretation of measured spectra by experts. To reduce the reliance on manual work, recent studies have applied machine learning techniques to cluster similar XRF spectra in data analysis and to identify the most likely pigments. Nevertheless, it is still challenging to implement automatic pigment identification strategies to directly tackle the complex structure of real paintings, e.g. pigment mixtures and layered pigments. In addition, pigment identification based on XRF on a pixel-by-pixel basis remains an obstacle due to the high noise level. Therefore, we developed a deep-learning based pigment identification framework to fully automate the process. In particular, this method offers high sensitivity to the underlying pigments and to the pigments present in low concentrations, therefore enabling robust mapping of pigments based on single-pixel XRF spectra. As case studies, we applied our framework to lab-prepared mock-up paintings and two 19th-century paintings: Paul Gauguin's Poεave;mes Barbares (1896) that contains layered pigments with an underlying painting, and Paul Cezanne's The Bathers (1899–1904). The pigment identification results demonstrated that our model achieved comparable results to the analysis by elemental mapping, suggesting the generalizability and stability of our model. [ABSTRACT FROM AUTHOR]

Total reflection X-ray fluorescence (TXRF) analysis is conducted to determine trace elements in a sample solution, which is dropped onto a substrate and dried. Therefore, the form of the residue affects the quantitative results. The absorption of X-ray fluorescence (XRF) follows the Lambert–Beer law; the absorption effect of XRF in a thick residue (dotted-type residue) is stronger than that in a thin residue (film-type residue). The absorption effect is particularly remarkable during the determination of low-Z elements in a high-elemental concentration solution. In this study, we propose a new film-like-residue preparation process based on the freeze-drying method to obtain accurate TXRF results. The sample solution is dropped onto the substrate and inserted into a chamber. The chamber is cooled using liquid nitrogen; resultantly, an aliquot of the sample is frozen. The chamber is depressurized using a vacuum pump, and the freeze-dried residue is prepared by maintaining the chamber at room temperature. To evaluate the efficiency of the freeze-drying-based method for sample preparation for TXRF analysis, we prepare a multi-element solution containing high-elemental concentration components. For the residue prepared using the freeze-drying method, the relative standard deviations of the quantitative values and the minimum detection limits are improved because the absorption effect is weakened. The sample preparation process based on the freeze-drying method facilitates accurate TXRF analysis of high-elemental concentration solutions and can be applied for the analysis of trace elements in different types of solutions such as environmental water and wastewater. [ABSTRACT FROM AUTHOR]

This research explored the feasibility of using portable X-ray fluorescence (pXRF) spectrometry to quantify Fe, Cu, Zn, and As concentrations in dried and ground organic materials (fungi, vegetation, and animal tissues) with intermediate thickness. The experimental results indicated that pXRF determined concentrations of 39 organic samples (calibration set) with intermediate thickness decreased with increasing sample mass per unit area (also known as surface density or mass thickness) in a power function. The derived mass-correction model allows for correcting the matrix effect caused by analyzing samples with intermediate thickness and for predicting pXRF determined concentrations for infinitely-thick samples. Then a simple linear regression (SLR) model was established based on the linear relationship between the pXRF and inductively coupled plasma mass spectroscopy (ICP-MS) determined concentrations of the organic samples. The SLR model allows for matrix effect correction caused by analyzing organic materials using pXRF soil mode and predicting the ICP-MS concentrations. The mass-correction and SLR calibration models were employed to predict the ICP-MS based trace element concentrations of 10 other organic samples of intermediate thickness (validation samples). The validation results were very good for Zn and As, and acceptable for Fe and Cu. The relative errors (REs) of the predicted Zn, As, Fe, and Cu concentrations were <18%, <22%, <34%, and <30%, respectively. Thus, pXRF shows strong potential for determining the trace element concentration in organic materials of intermediate thickness. The use of pXRF provides an appealing compromise between data quality and analytical time, and a potential alternative to laboratory analysis techniques such as ICP-MS. [ABSTRACT FROM AUTHOR]

Hand-held X-ray fluorescence (XRF) instrumentation has become widely used in a broad range of applications for problem solving. When used for in situ measurements (that is, instrumentation placed in contact with the surface of the sampling target), results can be acquired in a cheap and rapid manner, encouraging rapid resolution of the problem being investigated. However, the interpretation of such results should be undertaken with care to avoid misleading outcomes. In particular, an understanding of the depth of the XRF excited volume, the interpretation of data near detection limit levels and the contributions that can affect the uncertainty budget are important in avoiding errors in the use of XRF data. This tutorial review is designed for hand-held XRF users who do not necessarily have extensive training in XRF methodology. It provides a brief overview of the technique and its development together with a simple and pragmatic approach to understanding issues that affect the interpretation of hand-held XRF measurement results. [ABSTRACT FROM AUTHOR]

Analysis of oceanographic objects aboard a ship requires a method capable of express direct analysis. Laser-induced breakdown spectroscopy (LIBS) can be an adequate solution for this task. However, conventional laboratory setups are too bulky and fragile for on-board application. For this reason, we present a study on the application of a prototype mobile setup for determination of Mn, Fe, Ca, Sr and Ti in marine ferromanganese nodules (FMNs) from the Kara Sea. We investigated the miniaturisation of the experimental setup and considered possible inhomogeneity of FMNs by using a portable air-cooled DPSS laser (λ = 527 nm, E = 0.25 mJ per pulse) with high repetition rate. Additional miniaturisation was studied by comparing two different detection systems – a portable non-gated CCD spectrometer (spectral range of 400–505 nm, resolution up to 5000, 2048 pixels) and an ICCD camera coupled with a high-aperture Czerny–Turner spectrograph (spectral range of 200–530 nm, resolution up to 150 000). Both detectors showed fairly similar R2 of calibration – 0.971–0.999 for the CCD and 0.984–0.999 for the ICCD. We compared the LIBS results with those obtained by energy-dispersive X-ray fluorescence (EDXRF) and used ICP-AES as a reference method. It is shown that determination of Fe and Mn by EDXRF and LIBS with an iCCD camera provides similar results while miniaturisation by means of a compact CCD spectrometer leads to a decrease in accuracy by ∼65%. On the other hand, LIBS is able to provide up to 3 times better results than EDXRF for Ca, Sr and Ti. Further statistical treatment shows that LIBS results are generally closer to the reference rather than EDXRF in terms of median values. The difference between the CCD and iCCD here lies in the interquartile range which is wider for the majority of elements in case of the portable spectrometer. Thus, LIBS can be considered a valuable tool for rapid onboard analysis of complex marine FMN samples prior to more accurate yet time-consuming analysis via methods such as ICP-AES/MS. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A highlight this year was the construction of elemental maps of microalgae cells at the organelle level using XRF-CT with a beam size of just 15 nm. These probably represent the highest ever spatial resolution achieved for XRF-CT images. An innovative approach for reconstruction algorithms used a deep convolutional neural network to correct for the self-absorption effects in the XRF-CT sinogram domain. Such corrections will enable the use of conventional tomographic reconstruction algorithms for XRF-CT analysis. A remarkable advantage of the proposed method was that it could correct for self-absorption effects without any prior knowledge of the scanning setups. A particularly innovative dual-energy X-ray-beam ptycho-fluorescence imaging method combined XRF spectrometry and X-ray ptychography scanning with simultaneous data-collection. To overcome the incompatibility of the different excitation and scanning conditions required for fast ptychography and high spatial resolution XRF spectrometry, two coaxial beams of different sizes were used on the sample simultaneously. In comparison to sequential scans, this combined approach has the potential to reduce the data collection time by a factor of 25. The very good performance of laboratory 2D-XRF spectrometry for the analysis of minute amounts of radioactive material has made it a potential alternative to TXRF spectrometry. Reconstruction algorithms are now capable of significantly enhancing imaging resolution in μXRF spectrometry. The forensic classification of incredibly small amounts of samples such as single fibres was accomplished using elemental profiles determined by TXRF spectrometry. Although characterisation of the low-Z matrix in milk and polymers by conventional TXRF spectrometry is challenging, it was successfully achieved by studying the Compton and Rayleigh scatter with both univariate and multivariate approaches. Portable XRF spectrometry is a well-established technique for a wide range of applications. Emphasis continues to be placed on improving calibration and reducing matrix and inter-element effects. The sample type remains a challenge for portable XRF analysis. Computer vision and statistical methods such as spectral angle mapper are being increasingly applied to maximise the information that can be extracted from the data. For example, an innovative approach exploited the elemental features from XRF spectra to improve the interpretation of molecular information provided by reflectance spectroscopy, and vice versa. [ABSTRACT FROM AUTHOR]

Traditional Chinese herbal medicines are subject to heavy metal contamination. Standard detection methods are too complicated, time-consuming, and expensive for routine analysis, so low-cost methods are in high demand for rapid on-site screening. This study reports a high-sensitivity X-ray fluorescence (HS-XRF) method to determine As, Pb, and Cd residues simultaneously in herbal medicines. It couples monochromatic excitation energy dispersive X-ray fluorescence spectrometry and the fast fundamental parameters method. Each test takes only 10–30 min and costs 1/10th to 1/5th of the standard method. The detection limits, precision and accuracy were evaluated using different approaches, and application notes in practice are also proposed. This study is the first attempt to establish and evaluate HS-XRF in analyzing multiple heavy metals in herbal medicines. This rapid screening method would promote the testing efficiency and thus improve the monitoring of heavy metal contamination in herbal medicines. [ABSTRACT FROM AUTHOR]

Confocal micro-X-ray fluorescence spectroscopy can be performed with laboratory spectrometers for elemental imaging with 3D resolution. Due to self-absorption inside a specimen and energy effects induced by the used polycapillary optics, interpretation of data can be challenging. Thus, quantification techniques to reconstruct sample composition and geometry are mandatory to widen the applicability of the technique to further fields of analytical chemistry. We present an analytical routine which facilitates the quantitative investigation of 3D data sets obtained with laboratory spectrometers. By fully calibrating the spectrometer parameters the procedure is generalized to be suitable for all spectrometers with known excitation spectra and polycapillary optics. Calibration and validation measurements on homogeneous and stratified samples are presented with a discussion on uncertainties and challenges. Finally, the localization of a goethite needle in a quartz matrix is presented as an example of a possible routine application. [ABSTRACT FROM AUTHOR]