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In situ Electron Energy Loss Spectroscopy (EELS) combined with Transmission Electron Microscopy (TEM) has traditionally been pivotal for understanding how material processing choices affect local structure and composition. However, the ability to monitor and respond to ultrafast transient changes, now achievable with EELS and TEM, necessitates innovative analytical frameworks. Here, we introduce a machine learning (ML) framework tailored for the real-time assessment and characterization of in operando EELS Spectrum Images (EELS-SI). We focus on 2D MXenes as the sample material system, specifically targeting the understanding and control of their atomic-scale structural transformations that critically influence their electronic and optical properties. This approach requires fewer labeled training data points than typical deep learning classification methods. By integrating computationally generated structures of MXenes and experimental datasets into a unified latent space using Variational Autoencoders (VAE) in a unique training method, our framework accurately predicts structural evolutions at latencies pertinent to closed-loop processing within the TEM. This study presents a critical advancement in enabling automated, on-the-fly synthesis and characterization, significantly enhancing capabilities for materials discovery and the precision engineering of functional materials at the atomic scale.
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The development of radiation-tolerant structural materials is an essential element for the success of advanced nuclear energy concepts. A proven strategy to increase radiation resistance is to create microstructures with a high density of internal defect sinks, such as grain boundaries (GBs). However, as GBs absorb defects, they undergo internal transformations that limit their ability to capture defects indefinitely. Here, we show that, as the sink efficiency of GBs becomes exhausted with increasing irradiation dose, networks of irradiation loops form in the vicinity of saturated or near-saturated GB, maintaining and even increasing their capacity to continue absorbing defects. The formation of these networks fundamentally changes the driving force for defect absorption at GB, from "chemical" to "elastic." Using thermally-activated dislocation dynamics simulations, we show that these networks are consistent with experimental measurements of defect densities near GB. Our results point to these networks as a natural continuation of the GB once they exhaust their internal defect absorption capacity.
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A new misfit layered compound with the stoichiometry (EuS)1+δ(NbSe2)2 (δ ≈ 0.13) has been successfully synthesized. High-resolution transmission electron microscopy and powder X-ray diffraction confirm the misfit structure with (EuS)-(EuS) spacing of 18.30(1) Å. Magnetization, electrical resistivity, heat capacity, and thermal transport measurements show that the material is a heavily doped semiconductor or poor metal with a low thermal conductivity of â¼1 W/m K and an antiferromagnetic ordering transition at TN = 4.7 K. In contrast to the parent materials, the misfit is neither ferromagnetic nor superconducting down to T = 0.4 K. We find evidence of a field-driven transition to a ferromagnetic state due to reorientation of ferromagnetic EuS layers at µoH = 0.5 T at T = 2 K.
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Structural characterization is crucial to understanding protein function. Compared with X-ray diffraction methods, electron crystallography can be performed on nanometer-sized crystals and can provide additional information from the resulting Coulomb potential map. Whereas electron crystallography has successfully resolved three-dimensional structures of vitrified protein crystals, its widespread use as a structural biology tool has been limited. One main reason is the fragility of such crystals. Protein crystals can be easily damaged by mechanical stress, change in temperature, or buffer conditions as well as by electron irradiation. This work demonstrates a methodology to preserve these nanocrystals in their natural environment at room temperature for electron diffraction experiments as an alternative to existing cryogenic techniques. Lysozyme crystals in their crystallization solution are hermetically sealed via graphene-coated grids, and their radiation damage is minimized by employing a low-dose data collection strategy in combination with a hybrid-pixel direct electron detector. Diffraction patterns with reflections of up to 3 Å are obtained and successfully indexed using a template-matching algorithm. These results demonstrate the feasibility of in situ protein electron diffraction. The method described will also be applicable to structural studies of hydrated nanocrystals important in many research and technological developments.
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Electrones , Proteínas , Temperatura , Proteínas/química , Cristalografía por Rayos X , Difracción de Rayos XRESUMEN
Grain boundaries are critical in determining the properties of materials, including mechanical stability, conductivity, and corrosion resistance. The specific properties of materials depend not only on the misorientation of the crystals, the three most commonly characterized parameters, but also on the angle of the grain boundary plane between the two crystals, the final two parameters in the five-parameter macroscopic description of the grain boundary. The method presented here allows for the direct measurement of all five parameters of the grain boundary in a transmission electron microscopy specimen of various morphologies. This is especially applicable to atom probe specimens, where only a single-tilt axis is generally available, allowing the crystallographic description to be matched to the detailed chemical data available in the atom probe tomography. This method provides a platform for efficient grain boundary analysis in unique samples, saving operator time and allowing for ease of acquisition and interpretation in comparison with traditional electron diffraction methods.
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The rapid collection and indexing of electron diffraction patterns as produced via electron backscatter diffraction (EBSD) has enabled crystallographic orientation and structural determination, as well as additional property-determining strain and dislocation density information with increasing speed, resolution, and efficiency. Pattern indexing quality is reliant on the noise of the collected electron diffraction patterns, which is often convoluted by sample preparation and data collection parameters. EBSD acquisition is sensitive to many factors and thus can result in low confidence index (CI), poor image quality (IQ), and improper minimization of fit, which can result in noisy datasets and misrepresent the microstructure. In an attempt to enable both higher speed EBSD data collection and enable greater orientation fit accuracy with noisy datasets, an image denoising autoencoder was implemented to improve pattern quality. We show that EBSD data processed through the autoencoder results in a higher CI, IQ, and a more accurate degree of fit. In addition, using denoised datasets in HR-EBSD cross correlative strain analysis can result in reduced phantom strain from erroneous calculations due to the increased indexing accuracy and improved correspondence between collected and simulated patterns.
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MXenes are a growing family of 2D transition-metal carbides and nitrides, which display excellent performance in myriad of applications. Theoretical calculations suggest that manipulation of the MXene surface termination (such as =O or -F) could strongly alter their functional properties; however, experimental control of the MXene surface termination is still in the developmental stage. Here, we demonstrate that annealing MXenes in an Ar + O2 low-power plasma results in increased =O functionalization with minimal formation of secondary phases. We apply this method to two MXenes, Ti2CT x and Mo2TiC2T x (T x represents the mixed surface termination), and show that in both cases, the increased =O content increases the electrical resistance and decreases the surface transition-metal's electron count. For Mo2TiC2O x , we show that the O content can be reversibly altered through successive vacuum and plasma annealing. This work provides an effective way to tune MXene surface functionalization, which may unlock exciting surface-dependent properties.
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Helium bubbles are known to form in nuclear reactor structural components when displacement damage occurs in conjunction with helium exposure and/or transmutation. If left unchecked, bubble production can cause swelling, blistering, and embrittlement, all of which substantially degrade materials and-moreover-diminish mechanical properties. On the mission to produce more robust materials, nanocrystalline (NC) metals show great potential and are postulated to exhibit superior radiation resistance due to their high defect and particle sink densities; however, much is still unknown about the mechanisms of defect evolution in these systems under extreme conditions. Here, the performances of NC nickel (Ni) and iron (Fe) are investigated under helium bombardment via transmission electron microscopy (TEM). Bubble density statistics are measured as a function of grain size in specimens implanted under similar conditions. While the overall trends revealed an increase in bubble density up to saturation in both samples, bubble density in Fe was over 300% greater than in Ni. To interrogate the kinetics of helium diffusion and trapping, a rate theory model is developed that substantiates that helium is more readily captured within grains in helium-vacancy complexes in NC Fe, whereas helium is more prone to traversing the grain matrices and migrating to GBs in NC Ni. Our results suggest that (1) grain boundaries can affect bubble swelling in grain matrices significantly and can have a dominant effect over crystal structure, and (2) an NC-Ni-based material can yield superior resistance to irradiation-induced bubble growth compared to an NC-Fe-based material and exhibits high potential for use in extreme environments where swelling due to He bubble formation is of significant concern.
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Recent advances in detectors for imaging and spectroscopy have afforded in situ, rapid acquisition of hyperspectral data. While electron energy loss spectroscopy (EELS) data acquisition speeds with electron counting are regularly reaching 400 frames per second with near-zero read noise, signal to noise ratio (SNR) remains a challenge owing to fundamental counting statistics. In order to advance understanding of transient materials phenomena during rapid acquisition EELS, trustworthy analysis of noisy spectra must be demonstrated. In this study, we applied machine learning techniques to denoise high frame rate spectra, benchmarking with slower frame rate "ground truths". The results provide a foundation for reliable use of low SNR data acquired in rapid, in-situ spectroscopy experiments. Such a tool-set is a first step toward both automation in microscopy as well as use of these methods to interrogate otherwise poorly understood transformations.
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Iron-chromium and nickel-chromium binary alloys containing sufficient quantities of chromium serve as the prototypical corrosion-resistant metals owing to the presence of a nanometre-thick protective passive oxide film1-8. Should this film be compromised by a scratch or abrasive wear, it reforms with little accompanying metal dissolution, a key criterion for good passive behaviour. This is a principal reason that stainless steels and other chromium-containing alloys are used in critical applications ranging from biomedical implants to nuclear reactor components9,10. Unravelling the compositional dependence of this electrochemical behaviour is a long-standing unanswered question in corrosion science. Herein, we develop a percolation theory of alloy passivation based on two-dimensional to three-dimensional crossover effects that accounts for selective dissolution and the quantity of metal dissolved during the initial stage of passive film formation. We validate this theory both experimentally and by kinetic Monte Carlo simulation. Our results reveal a path forward for the design of corrosion-resistant metallic alloys.
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For Ti6Al4V orthopedic and spinal implants, osseointegration is often achieved using complex porous geometries created via additive manufacturing (AM). While AM porous titanium (pTi) has shown clinical success, concerns regarding metallic implants have spurred interest in alternative AM biomaterials for osseointegration. Insights regarding the evaluation of these new materials may be supported by better understanding the role of preclinical testing for AM pTi. We therefore asked: (a) What animal models have been most commonly used to evaluate AM porous Ti6Al4V for orthopedic bone ingrowth; (b) What were the primary reported quantitative outcome measures for these models; and (c) What were the bone ingrowth outcomes associated with the most frequently used models? We performed a systematic literature search and identified 58 articles meeting our inclusion criteria. We found that AM pTi was evaluated most often using rabbit and sheep femoral condyle defect (FCD) models. Additional ingrowth models including transcortical and segmental defects, spinal fusions, and calvarial defects were also used with various animals based on the study goals. Quantitative outcome measures determined via histomorphometry including ''bone ingrowth'' (range: 3.92-53.4% for rabbit/sheep FCD) and bone-implant contact (range: 9.9-59.7% for rabbit/sheep FCD) were the most common. Studies also used 3D imaging to report outcomes such as bone volume fraction (BV/TV, range: 4.4-61.1% for rabbit/sheep FCD), and push-out testing for outcomes such as maximum removal force (range: 46.6-3092 N for rabbit/sheep FCD). Though there were many commonalities among the study methods, we also found significant heterogeneity in the outcome terms and definitions. The considerable diversity in testing and reporting may no longer be necessary considering the reported success of AM pTi across all model types and the ample literature supporting the rabbit and sheep as suitable small and large animal models, respectively. Ultimately, more standardized animal models and reporting of bone ingrowth for porous AM materials will be useful for future studies.
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Aleaciones/química , Materiales Biocompatibles/química , Andamios del Tejido/química , Titanio/química , Animales , Huesos , Fémur , Humanos , Ensayo de Materiales , Modelos Animales , Oseointegración , Evaluación de Resultado en la Atención de Salud , Porosidad , Impresión Tridimensional , Prótesis e Implantes , Conejos , Ovinos , Fusión Vertebral , Ingeniería de TejidosRESUMEN
Two-dimensional (2D) transition metal carbides and nitrides known as MXenes have shown attractive functionalities such as high electronic conductivity, a wide range of optical properties, versatile transition metal and surface chemistry, and solution processability. Although extensively studied computationally, the magnetic properties of this large family of 2D materials await experimental exploration. 2D magnetic materials have recently attracted significant interest as model systems to understand low-dimensional magnetism and for potential spintronic applications. Here, we report on synthesis of Cr2TiC2Tx MXene and a detailed study of its magnetic as well as electronic properties. Using a combination of magnetometry, synchrotron X-ray linear dichroism, and field- and angular-dependent magnetoresistance measurements, we find clear evidence of a magnetic transition in Cr2TiC2Tx at approximately 30 K, which is not present in its bulk layered carbide counterpart (Cr2TiAlC2 MAX phase). This work presents the first experimental evidence of a magnetic transition in a MXene material and provides an exciting opportunity to explore magnetism in this large family of 2D materials.
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Gold is a noble metal typically stable as a solid in a face-centered cubic (FCC) structure under ambient conditions; however, under particular circumstances aberrant allotropes have been synthesized. In this work, we document the phase transformation of 25 nm thick nanocrystalline (NC) free-standing gold thin-film via in situ ion irradiation studied using atomic-resolution transmission electron microscopy (TEM). Utilizing precession electron diffraction (PED) techniques, crystallographic orientation and the radiation-induced relative strains were measured and furthermore used to determine that a combination of surface and radiation-induced strains lead to an FCC to hexagonal close packed (HCP) crystallographic phase transformation upon a 10 dpa radiation dose of Au4+ ions. Contrary to previous studies, HCP phase in nanostructures of gold was stabilized and did not transform back to FCC due to a combination of size effects and defects imparted by damage cascades.
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Fused filament fabrication three-dimensional (3D) printers have been shown to emit ultrafine particles (UFPs) and volatile organic compounds (VOCs). Previous studies have quantified bulk 3D printer particle and VOC emission rates, as well as described particle chemical composition via ex situ analysis. Here, we present size-resolved aerosol composition measurements from in situ aerosol mass spectrometry and ex situ transmission electron microscopy (TEM). Particles were sampled for in situ analysis during acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA) 3D printing activities and ex situ analysis during ABS printing. We examined the effect of a high-efficiency particulate air filter attachment on ABS emissions and particle chemical composition and demonstrate that filtration was effective in preventing UFP emissions and that particles sampled during filtered prints did not have a high contribution (â¼4% vs â¼10%) from aromatic ions in the mass spectrum. Ex situ analysis of particles collected during ABS printing was performed via TEM and electron energy loss spectroscopy, which indicated a high level of sp2 bonding type consistent with polymeric styrene. One 3D print with PLA resulted in an aerosol mass size distribution with a peak at â¼300 nm. Unfiltered ABS prints resulted in particle mass size distributions with peak diameters of â¼100 nm.
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Contaminación del Aire Interior , Compuestos Orgánicos Volátiles , Aerosoles , Tamaño de la Partícula , Material Particulado , EstirenoRESUMEN
Transmission electron microscopy (TEM) is an established tool used for the investigation of defects in materials. Traditionally, diffraction contrast techniques-two-beam bright-field and weak-beam dark-field-have been used to image defects due to contrast sensitivity from weak lattice strains. Use of these methods entail an intricate tilt series of imaging using different diffracting vectors, g, to verify the gâ¢b invisibility criterion relative to the different defect types and habit planes inherent to the material. Recently, the addition of down-zone imaging and STEM imaging has also proven to be effective imaging techniques for defect density analysis. Interest in nanocrystalline (NC) materials, spurred by their conjectured superior properties compared to their coarse-grain counterparts, has been thriving and the investigation of their defect morphologies is essential. Maneuvering within NC samples in the TEM adds another layer of difficulty making the aforementioned techniques not practical for application to specimens with complex microstructures. For this reason, we have devised a protocol for identifying NC grains optimally oriented for quantitative analysis using NanoMegas ASTAR automated crystal orientation mapping (ACOM) in the TEM. In this work, we conduct a series of experiments assessing the effectiveness of conventional two-beam bright-field, weak-beam dark-field, and down-zone STEM imaging. We also evaluate an ACOM-assisted multibeam imaging method and compare defect density results obtained using each technique in an irradiated nanocrystalline Au sample.
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MXenes have shown promise in myriad applications, such as energy storage, catalysis, EMI shielding, among many others. However, MXene oxidation in aqueous colloidal suspensions when stored in water at ambient conditions remains a challenge. It is now shown that by simply capping the edges of individual MXene flakes, Ti3 C2 Tz and V2 CTz , by polyanions such as polyphosphates, polysilicates or polyborates, it is possible to quite significantly reduce their propensity for oxidation even when held in aerated water for weeks. This breakthrough resulted from the realization that the edges of MXene sheets are positively charged. It is thus an example of selectively functionalizing the edges differently from the MXene sheet surfaces.
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Understanding the effect of microstructural features on corrosion behavior will allow for significant improvements to alloy design for harsh environments. Recently, in-situ TEM has been recognized to offer significant data on corrosion behavior at the nanoscale, but in order for the best information to be acquired, a three dimensional view of the oxidation process is needed so that oxide structure and phase can be identified. Described herein is a new method of sample preparation for transmission electron microscopy (TEM) using a focused ion beam (FIB) to cross-section a previously FIB prepared sample. In-situ TEM was used to oxidize a sample using an environmental cell, and this in-situ sample is cross-sectioned to study oxide depth and oxide structure. This technique provides a new method to investigate in-situ TEM samples in 3D.
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MXenes are an emerging family of highly-conductive 2D materials which have demonstrated state-of-the-art performance in electromagnetic interference shielding, chemical sensing, and energy storage. To further improve performance, there is a need to increase MXenes' electronic conductivity. Tailoring the MXene surface chemistry could achieve this goal, as density functional theory predicts that surface terminations strongly influence MXenes' Fermi level density of states and thereby MXenes' electronic conductivity. Here, we directly correlate MXene surface de-functionalization with increased electronic conductivity through in situ vacuum annealing, electrical biasing, and spectroscopic analysis within the transmission electron microscope. Furthermore, we show that intercalation can induce transitions between metallic and semiconductor-like transport (transitions from a positive to negative temperature-dependence of resistance) through inter-flake effects. These findings lay the groundwork for intercalation- and termination-engineered MXenes, which promise improved electronic conductivity and could lead to the realization of semiconducting, magnetic, and topologically insulating MXenes.
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Nanoparticle synthesis was demonstrated via functionalization-induced self-assembly (FISA) of block copolymers using Suzuki-Miyaura cross-coupling. In situ self-assembly was triggered in organic media by the progressive installation of solvophobic pendant groups onto an initially soluble diblock copolymer, rendering the reactive block insoluble and causing the formation of spherical polymeric micelles. Self-assembly was found to depend on the percent functionalization (f%), where after a critical threshold micelles were accessible that increased in size with increasing f% values. We found the chemical nature of the installed functional group to be crucial for conducting FISA and for controlling the solution morphology, with relatively solvophilic adducts remaining as unimers and increasingly solvophobic adducts trending toward larger micelles, from ca. 40 to 100 nm in diameter. The core and corona of the anticipated micellar structure were visualized using fluorine mapping through electron energy loss spectroscopy, in conjunction with FISA achieved through pendent trifluorophenyl functionality. This work establishes FISA as a new, versatile synthetic strategy to create nanoparticles having tunable morphologies with potential application as molecular payload delivery vehicles.