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Nanomaterial properties and functionalities are influenced by their shape, size, and chemical composition. The importance of these parameters highlights the need for a statistically robust analysis of a large particle population, necessitating automation. This study introduces a neural network‐empowered smart scan technique that achieves a relative increase in speed compared to traditional energy‐dispersive X‐ray spectroscopy (EDX) mapping. The main advantage is that it reduces the required dose, decreasing potential damage to the sample by avoiding unnecessary exposure. It holds potential use in other multimodal scanning transmission electron microscopy or scanning‐based imaging approaches. In the first example, identifying particles in a matrix with a trained neural network reduces the acquisition time by two orders of magnitude. This acceleration enables a statistical compositional analysis of thousands of particles in less than 1 h. Similar improvements are observed for atomic resolution. The discrete positions of atoms identified by the trained network allow for selective EDX sampling at these centers, thereby identifying the atomic species of the column with much‐reduced sampling. Consequently, a lower sampling dose is required, enabling mapping of more delicate materials with high lateral resolution and at a high statistical confidence interval. Even though manual training is still required, this approach greatly benefits repetitive quality control tasks. This study unveils an innovative, neural network‐driven smart scanning strategy, showcased within scanning transmission electron microscopy (STEM) settings. This method dramatically increases energy‐dispersive X‐ray spectroscopy mapping speed, lowers dose demands, and facilitates efficient compositional analysis of diverse material structures. While STEM serves as the initial application, the versatile smart scan strategy shows potential for broad adaptation across various imaging modalities, indicating its wider implications in materials research and related fields.

Identification of unknown micro‐ and nano‐sized mineral phases is commonly achieved by analyzing chemical maps generated from hyperspectral imaging data sets, particularly scanning electron microscope—energy dispersive X‐ray spectroscopy (SEM‐EDS). However, the accuracy and reliability of mineral identification are often limited by subjective human interpretation, non‐ideal sample preparation, and the presence of mixed chemical signals generated within the electron‐beam interaction volume. Machine learning has emerged as a powerful tool to overcome these problems. Here, we propose a machine‐learning approach to identify unknown phases and unmix their overlapped chemical signals. This approach leverages the guidance of Gaussian mixture modeling clustering fitted on an informative latent space of pixel‐wise elemental data points modeled using a neural network autoencoder, and unmixes the overlapped chemical signals of phases using non‐negative matrix factorization. We evaluate the reliability and the accuracy of the new approach using two SEM‐EDS data sets: a synthetic mixture sample and a real‐world particulate matter sample. In the former, the proposed approach successfully identifies all major phases and extracts background‐subtracted single‐phase chemical signals. The unmixed chemical spectra show an average similarity of 83.0% with the ground truth spectra. In the second case, the approach demonstrates the ability to identify potentially magnetic Fe‐bearing particles and their background‐subtracted chemical signals. We demonstrate a flexible and adaptable approach that brings a significant improvement to mineralogical and chemical analysis in a fully automated manner. The proposed analysis process has been built into a user‐friendly Python code with a graphical user interface for ease of use by general users. Key Points We develop a machine‐learning approach to automatically identify unknown mineral phases and unmix their compositional spectra The approach successfully identifies all phases in a synthetic mixture data set with an average spectrum similarity 83% to the ground truth The approach demonstrates its ability to generalize by identifying unknown minerals and their background‐subtracted chemical signals

Energy dispersive X-ray spectroscopy (EDS) is a powerful technical tool used in the biomedical field to investigate the proportion of chemical elements of interest in research, such as heavy metal bioaccumulation and the enzymatic cofactors and nanoparticle therapy in various pathologies. However, the correct evaluation of the proportion of the elements is subject to some factors, including the method of sample preservation. In this study, we seek to investigate the effect of biological tissue preservation methods on the proportion of chemical elements obtained by the EDS methodology. For such, we used EDS to measure the proportion of chemical elements with biomedical interest in preserved livers, using three common methods for preserving biological tissues: (a) freezing, (b) paraformaldehyde fixative solution, and (c) Karnovsky solution. We found an increased level of sodium and reduced contents of potassium and copper in samples fixed in fixative solutions, when compared to frozen samples (p < 0.05). Our data indicate that preservation methods can change the proportion of chemical elements in biological samples, when measured by EDS. Frozen preservation should be preferred to retain the actual chemical content of samples and allow a correct assessment of the proportion of their elements.

Tiny samples of ancient atmosphere in air bubbles within ice cores contain argon (Ar), which can be used to reconstruct past temperature changes. At a sufficient depth, the air bubbles are compressed by the overburden pressure under low temperature and transform into air-hydrate crystals. While the oxygen (O2) and nitrogen (N2) molecules have indeed been identified in the air-hydrate crystals with Raman spectroscopy, direct observational knowledge of the distribution of Ar at depth within ice sheet and its enclathration has been lacking. In this study, we applied scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) to five air-hydrate crystals in the Greenland NEEM ice core, finding them to contain Ar and N. Given that Ar cannot be detected by Raman spectroscopy, the method commonly used for O2 and N2, the SEM-EDS measurement method may become increasingly useful for measuring inert gases in deep ice cores.

A thermal neutron-absorbing metal matrix composite (MMC) comprised of Al3Hf particles in an aluminum matrix was developed to filter out thermal neutrons and create a fast flux environment for material testing in a mixed-spectrum nuclear reactor. Intermetallic Al3Hf particles capture thermal neutrons and are embedded in a highly conductive aluminum matrix that provides conductive cooling of the heat generated due to thermal neutron capture by the hafnium. These Al3Hf-Al MMCs were fabricated using powder metallurgy via hot pressing. The specimens were neutron-irradiated to between 1.12 and 5.38 dpa and temperatures ranging from 286 °C to 400 °C. The post-irradiation examination included microstructure characterization using transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy. This study reports the microstructural observations of four irradiated samples and one unirradiated control sample. All the samples showed the presence of oxide at the particle–matrix interface. The irradiated specimens revealed needle-like structures that extended from the surface of the Al3Hf particles into the Al matrix. An automated segmentation tool was implemented based on a YOLO11 computer vision-based approach to identify dislocation lines and loops in TEM images of the irradiated Al-Al3Hf MMCs. This work provides insight into the microstructural stability of Al3Hf-Al MMCs under irradiation, supporting their consideration as a novel neutron absorber that enables advanced spectral tailoring.

In the present paper, different types of pure and commercial plastic waste from different EU countries (UK, France, Italy, and Romania) were investigated for microstructure surface morphology and chemical properties by scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDXS). The goal of the current investigation was to determine the chemical composition of selected packaging materials and compare these measurements with data obtained through a carbon-hydrogen-nitrogen-sulfur-oxygen (CHNS-O) elemental analyzer, which is conventionally used to characterize waste materials. The capabilities of the experimental approach are discussed in connection with their application to the study of waste sample materials and in comparison with alternative experimental methods such as elemental analysis. The CHNS-O comparison is made between the present data obtained with SEM-EDXS instrument and EA 3000 elemental analyzer used in previews studies conducted by the authors. Results show a difference of composition among packaging from different countries that can affect the treatment adopted for its valorization and the strategies of circular economy.

Cementitious materials have potential for infrastructure development in low-temperature marine environments, including in seawater at high latitudes and in deep-sea environments (water depths of >1000 m). Although the marine deterioration of cementitious materials has been widely investigated, the influence of seawater temperature has not been elucidated. In this study, to determine the effects of low-temperature seawater on the durability of cementitious materials, cement paste specimens were immersed in a seawater tank at room temperature and 2 °C for 433 days. The specimen immersed in low-temperature seawater exhibited significant deterioration with a partially collapsed surface, whereas the specimen immersed in room-temperature seawater maintained its original shape. Following low-temperature immersion, Ca dissolution was more pronounced and dissolved portlandite, decalcified calcium (alumino)silicate hydrate (C–(A-)S–H), magnesium (alumino)silicate hydrate (M–(A-)S–H), and thaumasite were observed on the collapsed surface. Such significant deterioration can be attributed to the increased solubility of portlandite under low-temperature conditions, which could promote Ca dissolution and subsequently lead to C–(A-)S–H decalcification and the formation of M–(A-)S–H and thaumasite. These insights are expected to contribute to the successful construction and maintenance of cementitious structures in low-temperature seawater.

PREMISE OF THE STUDY: South American Loasaceae have a morphologically complex trichome cover, which is characterized by multiple biomineralization. The current study investigates the ontogeny of these complex trichomes and the process of their biomineralization, since both are very poorly understood. METHODS: The development of stinging trichomes on various parts of the plants and the process of mineralization were studied using scanning electron microscopy (SEM) and energy‐dispersive x‐ray spectroscopy (EDX). KEY RESULTS: Trichomes are initiated very early in organ development and the different trichome types begin developing their distinctive morphology at a very early developmental stage. Biomineralization in the stinging trichomes starts with the deposition of silica or calcium phosphate in the apex and then proceeds basipetally, with a more irregular, subsimultaneous mineralization of the base and the shaft. Mineralization of the scabrid‐glochidiate trichomes starts on the surface processes and in the apex (silica, calcium phosphate), with a subsequent mineralization of the shaft with calcium carbonate. CONCLUSION: Mineralized trichomes in Loasaceae provide an excellent model for the study of biomineralization. The overall sequence of mineralization is typically from distal and peripheral to proximal and central. Typically, three biominerals—silica, calcium carbonate, and calcium phosphate—are differentially and sequentially deposited in different parts of each unicellular stinging trichome.

To develop a general method for NP fabrication from various proteins with maintenance of biological activity. A novel general approach for producing protein nanoparticles (NP) by nanoprecipitation of the protein solutions in 1,1,1,3,3,3-hexafluoroisopropanol is described. Protein NP sizes and shapes were analyzed by dynamic light scattering, scanning electron and atomic force microscopy (SEM and AFM). Chemical composition of the NP was confirmed using ultraviolet (UV) spectroscopy, energy-dispersive X-ray spectroscopy (EDX) and circular dichroism (CD). Biological properties of the NP were analyzed in ELISA, immunofluorescent analysis and lysozyme activity assay. Water-insoluble NP were constructed from globular (bovine serum albumin (BSA), lysozyme, immunoglobulins), fibrillar (fibrinogen) proteins and linear polylysines by means of nanoprecipitation of protein solutions in fluoroalcohols. AFM and SEM revealed NP sizes of 20-250 nm. The NP chemical structure was confirmed by UV spectroscopy, protease digestion and EDX spectroscopy. CD spectra revealed a stable secondary structure of proteins in NP. The UV spectra, microscopy and SDS-PAA gel electrophoresis (PAGE) proved the NP stability at +4°C for 7 months. Co-precipitation of proteins with fluorophores or nanoprecipitation of pre-labeled BSA resulted in fluorescent NP that retained antigenic structures as shown by their binding with specific antibodies. Moreover, NP from monoclonal antibodies could bind with the hepatitis B virus antigen S. Besides that, lysozyme NP could digest bacterial cellular walls. Thus, the water-insoluble, stable protein NP were produced by nanoprecipitation without cross-linking and retained ligand-binding and enzymatic activities.

Scanning electron microscopy, coupled with energy-dispersive X-ray spectroscopy (EDS), is a powerful tool used in many scientific fields. It can provide nanoscale images, allowing size and morphology measurements, as well as provide information on the spatial distribution of elements in a sample. This study compares the capabilities of a traditional EDS detector with a recently developed annular EDS detector when analyzing electron transparent and beam-sensitive NaCl particles on a TEM grid. The optimal settings for single particle analysis are identified in order to minimize beam damage and optimize sample throughput via the choice of acceleration voltage, EDS acquisition time, and quantification model. Here, a linear combination of two models is used to bridge results for particle sizes, which are neither bulk nor sufficiently thin to assume electron transparent. Additionally, we show that the increased count rate obtainable with the annular detector enables mapping as a viable analysis strategy compared with feature detection methods, which only scan segmented regions. Finally, we discuss advantages and disadvantages of the two analysis strategies.