X-ray Spectrometry

A novel particle size distribution correction method based on image processing and deep learning for coal quality analysis using NIRS-XRF

The combined application of near-infrared spectroscopy (NIRS) and X-ray fluorescence spectroscopy (XRF) has achieved remarkable results in coal quality analysis by leveraging NIRS's sensitivity to organic compounds and XRF's reliability for inorganic composition. However, variations in particle size distribution negatively affect the diffuse reflectance of NIRS and the fluorescence signal intensities of XRF, leading to decreased accuracy and repeatability in predictions. To address this issue, this study innovatively proposes a particle size correction method that integrates image processing and deep learning. The method first captures micro-images of the coal sample surface using a microscope camera and employs the Segment Anything Model (SAM) for binarization to represent particle size distribution. Subsequently, a Spatial Transformer Network (STN) is applied for geometric correction, followed by feature extraction using a Convolutional Neural Network (CNN) to establish a correlation model between particle size distribution and ash measurement errors. In experiments involving 56 coal samples, including 48 at 0.2 mm for the standard ash prediction model and 8 within a 0∼1 mm range for correction, the results showed significant improvements: standard deviation (SD), mean absolute error (MAE), and root mean square error of prediction (RMSEP) decreased from 0.321%, 0.317%, and 0.335% to 0.229%, 0.225%, and 0.257%, respectively. Using the accuracy of the 0.2 mm particle size validation set as a reference, compared to before correction, the errors in these metrics were reduced by 64.06%, 50%, and 60.80%, respectively. This study demonstrates that integrating deep learning and image analysis significantly enhances the repeatability and accuracy of NIRS-XRF measurements, effectively mitigating sub-millimeter particle size effects on spectral detection results and improving model adaptability. This method, through automated particle size distribution analysis and real-time result correction, holds promise for providing essential technical support for the development of online quality detection technologies for conveyor belt materials.

Enhancing substance identification by Raman spectroscopy using deep neural convolutional networks with an attention mechanism

Raman spectroscopy is widely used for substance identification, providing molecular information from various components along with noise and instrument interference. Consequently, identifying components based on Raman spectra remains challenging. In this study, we collected Raman spectral data of 474 hazardous chemical substances using a portable Raman spectrometer, resulting in a dataset of 59 468 spectra. Our research employed a deep neural convolutional network based on the ResNet architecture, incorporating an attention mechanism called the SE module. By enhancing the weighting of certain spectral features, the performance of the model was significantly improved. We also investigated the classification predictive performance of the model under small-sample conditions, facilitating the addition of new hazardous chemical categories for future deployment on mobile devices. Additionally, we explored the features extracted by the convolutional neural network from Raman spectra, considering both Raman intensity and Raman shift aspects. We discovered that the neural network did not solely rely on intensity or shift for substance classification, but rather effectively combined both aspects. This research contributes to the advancement of Raman spectroscopy applications for hazardous chemical identification, particularly in scenarios with limited data availability. The findings shed light on the significance of spectral features in the model's decision-making process and have implications for broader applications of deep learning techniques in Raman spectroscopy-based substance identification.

Surface Analysis Techniques in Forensic Science: Successes, Challenges, and Opportunities for Operational Deployment

Surface analysis techniques have rapidly evolved in the last decade. Some of these are already routinely used in forensics, such as for the detection of gunshot residue or for glass analysis. Some surface analysis approaches are attractive for their portability to the crime scene. Others can be very helpful in forensic laboratories owing to their high spatial resolution, analyte coverage, speed, and specificity. Despite this, many proposed applications of the techniques have not yet led to operational deployment. Here, we explore the application of these techniques to the most important traces commonly found in forensic casework. We highlight where there is potential to add value and outline the progress that is needed to achieve operational deployment. We consider within the scope of this review surface mass spectrometry, surface spectroscopy, and surface X-ray spectrometry. We show how these tools show great promise for the analysis of fingerprints, hair, drugs, explosives, and microtraces.

Field determination of hazardous chemicals in public security by using a hand-held Raman spectrometer and a deep architecture-search network

With the advanced development of miniaturization and integration of instruments, Raman spectroscopy (RS) has demonstrated its great significance because of its non-invasive property and fingerprint identification ability, and extended its applications in public security, especially for hazardous chemicals. However, the fast and accurate RS analysis of hazardous chemicals in field test by non-professionals is still challenging due to the lack of an effective and timely spectral-based chemical-discriminating solution. In this study, a platform was developed for the field determination of hazardous chemicals in public security by using a hand-held Raman spectrometer and a deep architecture-search network (DASN) incorporated into a cloud server. With the Raman spectra of 300 chemicals, DASN stands out with identification accuracy of 100% and outweighs other machine learning and deep learning methods. The network feature maps for the spectra of methamphetamine and ketamine focus on the main peaks of 1001 and 652 cm^-1, which indicates the powerful feature extraction capability of DASN. Its receiver operating characteristic (ROC) curve completely encloses the other models, and the area under the curve is up to 1, implying excellent robustness. With the well-built platform combining RS, DASN, and cloud server, one test process including Raman measurement and identification can be performed in tens of seconds. Hence, the developed platform is simple, fast, accurate, and could be considered as a promising tool for hazardous chemical identification in public security on the scene.

Recent advances in analysis of trace elements in environmental samples by X-ray based techniques (IUPAC Technical Report)

Trace elements analysis is a fundamental challenge in environmental sciences. Scientists measure trace elements in environmental media in order to assess the quality and safety of ecosystems and to quantify the burden of anthropogenic pollution. Among the available analytical techniques, X-ray based methods are particularly powerful, as they can quantify trace elements in situ. Chemical extraction is not required, as is the case for many other analytical techniques. In the last few years, the potential for X-ray techniques to be applied in the environmental sciences has dramatically increased due to developments in laboratory instruments and synchrotron radiation facilities with improved sensitivity and spatial resolution. In this report, we summarize the principles of the X-ray based analytical techniques most frequently employed to study trace elements in environmental samples. We report on the most recent developments in laboratory and synchrotron techniques, as well as advances in instrumentation, with a special attention on X-ray sources, detectors, and optics. Lastly, we inform readers on recent applications of X-ray based analysis to different environmental matrices, such as soil, sediments, waters, wastes, living organisms, geological samples, and atmospheric particulate, and we report examples of sample preparation.

Application of synchrotron radiation and other techniques in analysis of radioactive microparticles emitted from the Fukushima Daiichi Nuclear Power Plant accident-A review

During the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident, large amounts of radioactive materials were released into the environment. Among them, a large proportion of the radionuclides, such as Cs, entered into the environment as radioactive microparticles (RMs). In recent years, the characterization of RMs based on synchrotron radiation (SR) techniques has been reported, since their physical and chemical properties played an important role in evaluating the chemical reactions and physical changes that occurred when the nuclear material meltdowns took place. In this review, we summarize separation and measurement technologies used in studies of RMs, and we emphasize the application of SR-based techniques in the characterization of RMs. We report research progress, including information for elemental composition, isotopic distribution, radioactivity, and formation processes. Also, we compare the RMs from the FDNPP and the Chernobyl Nuclear Power Plant accidents. The SR-based technologies offer great improvement in the resolution and precision compared to conventional technologies, such as X-ray fluorescence and X-ray diffraction.

Visualizing molecular distributions for biomaterials applications with mass spectrometry imaging: a review

Mass spectrometry imaging (MSI) is a rapidly emerging field that is continually finding applications in new and exciting areas. The ability of MSI to measure the spatial distribution of molecules at or near the surface of complex substrates makes it an ideal candidate for many applications, including those in the sphere of materials chemistry. Continual development and optimization of both ionization sources and analyzer technologies have resulted in a wide array of MSI tools available, both commercially available and custom-built, with each configuration possessing inherent strengths and limitations. Despite the unique potential of MSI over other chemical imaging methods, their potential and application to (bio)materials science remains in our view a largely underexplored avenue. This review will discuss these techniques enabling high parallel molecular detection, focusing on those with reported uses in (bio)materials chemistry applications and highlighted with select applications. Different technologies are presented in three main sections; secondary ion mass spectrometry (SIMS) imaging, matrix-assisted laser desorption ionization (MALDI) MSI, and emerging MSI technologies with potential for biomaterial analysis. The first two sections (SIMS and MALDI) discuss well-established methods that are continually evolving both in technological advancements and in experimental versatility. In the third section, relatively new and versatile technologies capable of performing measurements under ambient conditions will be introduced, with reported applications in materials chemistry or potential applications discussed. The aim of this review is to provide a concise resource for those interested in utilizing MSI for applications such as biomimetic materials, biological/synthetic material interfaces, polymer formulation and bulk property characterization, as well as the spatial and chemical distributions of nanoparticles, or any other molecular imaging application requiring broad chemical speciation.

Synchrotron-based X-ray microscopy for sub-100nm resolution cell imaging

Microscopic imaging provides a straightforward approach to deepen our understanding of cellular events. While the resolution of optical microscopes is generally limited to 200-300nm due to the diffraction limit, there has been ever growing interest in studying cells at the sub-100nm regime. By exploiting the short wavelength, long penetration depth and elemental specificity of X-rays, synchrotron-based X-ray microscopy (XRM) has demonstrated its power in exploring the structure and function of cells at the nanometer resolution. Here we summarize recent advances in using XRM for imaging ultrastructure of organelles and specific biomolecular locations in cells, and provide a perspective on potentials and applications of XRM.

A robust X-ray fluorescence technique for multielemental analysis of solid samples

X-ray fluorescence (XRF) quantitation software programs are widely used for analyzing environmental samples due to their versatility but at the expense of accuracy. In this work, we propose an accurate, robust, and versatile technique for multielemental X-ray fluorescence analytical applications, by spiking solid matrices with standard solutions. National Institute of Standards and Technology (NIST)-certified soil standards were spiked with standard solutions, mixed well, desiccated, and analyzed by an energy dispersive XRF. Homogenous targets were produced and low error calibration curves, for the added and not added, neighboring, elements, were obtained. With the addition of few elements, the technique provides reliable multielemental analysis, even for concentrations of the order of milligram per kilogram (ppm). When results were compared to the ones obtained from XRF commercial quantitation software programs, which are widely used in environmental monitoring and assessment applications, they were found to fit certified values better. Moreover, in all examined cases, results were reliable. Hence, this technique can also be used to overcome difficulties associated with interlaboratory consistency and for cross-validating results. The technique was applied to samples with an environmental interest, collected from a ship/boat repainting area. Increased copper, zinc, and lead loads were observed (284, 270, and 688 mg/kg maximum concentrations in soil, respectively), due to vessels being paint stripped and repainted.

Synchrotron-based X-ray-sensitive nanoprobes for cellular imaging

It is one of the ultimate goals in cell biology to understand the complex spatio-temporal interplay of biomolecules in the cellular context. To this end, there have been great efforts on the development of various probes to detect and localize specific biomolecules in cells with a variety of microscopic imaging techniques. In this Research News, we first summarize several types of microscopy for visualizing specific biomolecular targets. Then we focus on recent advances in the design of X-ray sensitive nanoprobes for applications in synchrotron-based cellular imaging. With the availability of advanced synchrotron techniques, there has been rapid progress toward high-resolution and multi-color X-ray imaging in cells with various types of functional nanoprobes.

Comparison of two confocal micro-XRF spectrometers with different design aspects

Two different confocal micro X-ray fluorescence spectrometers have been developed and installed at Osaka City University and the Vienna University of Technology Atominstitut. The Osaka City University system is a high resolution spectrometer operating in air. The Vienna University of Technology Atominstitut spectrometer has a lower spatial resolution but is optimized for light element detection and operates under vacuum condition. The performance of both spectrometers was compared. In order to characterize the spatial resolution, a set of nine specially prepared single element thin film reference samples (500 nm in thickness, Al, Ti, Cr, Fe Ni, Cu, Zr, Mo, and Au) was used. Lower limits of detection were determined using the National Institute of Standards and Technology standard reference material glass standard 1412. A paint layer sample (cultural heritage application) and paint on automotive steel samples were analyzed with both instruments. The depth profile information was acquired by scanning the sample perpendicular to the surface. © 2013 The Authors. X-Ray Spectrometry published by John Wiley & Sons, Ltd.

Comparative advantages and limitations of the basic metrology methods applied to the characterization of nanomaterials

Fabrication of modern nanomaterials and nanostructures with specific functional properties is both scientifically promising and commercially profitable. The preparation and use of nanomaterials require adequate methods for the control and characterization of their size, shape, chemical composition, crystalline structure, energy levels, pathways and dynamics of physical and chemical processes during their fabrication and further use. In this review, we discuss different instrumental methods for the analysis and metrology of materials and evaluate their advantages and limitations at the nanolevel.

Full-field transmission x-ray imaging with confocal polycapillary x-ray optics

A transmission x-ray imaging setup based on a confocal combination of a polycapillary focusing x-ray optic followed by a polycapillary collimating x-ray optic was designed and demonstrated to have good resolution, better than the unmagnified pixel size and unlimited by the x-ray tube spot size. This imaging setup has potential application in x-ray imaging for small samples, for example, for histology specimens.