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When high-energy electrons from scanning transmission electron microscope (STEM) are interacting with the liquid, the vast majority of the chemical reactions that are observed are induced by the radiolysis breakdown of the liquid molecules.
Scanning electron microscopy (SEM) is resource intensive, which limits its throughput in some applications. As an alternative, we propose applying computer vision and machine learning to generate high-quality synthetic SEM micrographs from micrographs obtained using light optical microscopy (LOM). Working with a correlated LOM/SEM dataset of dual-phase steel images, we test generative models of various architectures, including encoder-decoder networks, generative adversarial networks (GANs), and diffusion-based models. We find that the diffusion models significantly outperform other methods on both qualitative and quantitative assessments, while preserving key metallurgical meaning. This work establishes diffusion as the state-of-the-art for microscopy modality transfer and demonstrates the potential of AI-powered microscopy to enhance LOM with micron scale structural recreation.
Multilayer Laue lenses (MLLs) offer significant advantages over traditional diffractive focusing optics, such as zone plates (ZPs), in terms of efficiency and durability when focusing hard X-rays to 10 nm and below. Typically, a pair of 1D MLLs is aligned independently to achieve point focusing. This approach only permits scanning microscopy by moving the sample stage, which presents a limitation when it comes to scanning large or bulky objects at high speed. In this study, we present an experimental demonstration of scanning monolithically bonded 2D MLLs while keeping the sample stationary, effectively addressing the limitation of the sample’s size and weight. We characterized the system’s positional and angular stabilities during scanning, which are critical parameters to the nanofocusing performance of MLLs. In addition, we provided the X-ray measurement results and corresponding image resolution analysis.
A robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on ensemble learning and iterative training (ELIT) of deep convolutional neural networks, is implemented on an operational microscope, enabling the exploration of the dynamics of specific atomic configurations under electron beam irradiation via an automated experiment in STEM. Combined with beam control, this approach allows studying beam effects on selected atomic groups and chemical bonds in a fully automated mode. Here, in this study, we demonstrate atomically precise engineering of single vacancy lines in transition metal dichalcogenides and the creation and identification of topological defects in graphene. The ELIT-based approach facilitates direct on-the-fly analysis of the STEM data and engenders real-time feedback schemes for probing electron beam chemistry, atomic manipulation, and atom by atom assembly.
Structuring materials with atomic precision is the ultimate goal of nanotechnology and is becoming increasingly relevant as an enabling technology for quantum electronics/spintronics and quantum photonics. Here, we create atomic defects in monolayer MoS 2 by helium ion (He-ion) beam lithography with a spatial fidelity approaching the single-atom limit in all three dimensions. Using low-temperature scanning tunneling microscopy (STM), we confirm the formation of individual point defects in MoS 2 upon He-ion bombardment and show that defects are generated within 9 nm of the incident helium ions. Atom-specific sputtering yields are determined by analyzing the type and occurrence of defects observed in high-resolution STM images and compared with Monte Carlo simulations. Both theory and experiment indicate that the He-ion bombardment predominantly generates sulfur vacancies.
Van der Waals layered ferroelectrics, such as CuInP 2 S 6 (CIPS), offer a versatile platform for miniaturization of ferroelectric device technologies. Control of the targeted composition and kinetics of CIPS synthesis enables the formation of stable self-assembled heterostructures of ferroelectric CIPS and nonferroelectric In 4/3 P 2 S 6 (IPS). Here, we use quantitative scanning probe microscopy methods combined with density functional theory (DFT) to explore in detail the nanoscale variability in dynamic functional properties of the CIPS-IPS heterostructure. Within, we report evidence of fast ionic transport which mediates an appreciable out-of-plane electromechanical response of the CIPS surface in the paraelectric phase. Further, we map the nanoscale dielectric and ionic conductivity properties as we thermally stimulate the ferroelectric-paraelectric phase transition, recovering the local dielectric behavior during this phase transition. Finally, aided by DFT, we reveal a substantial and tunable conductivity enhancement at the CIPS/IPS interface, indicating the possibility of engineering its interfacial properties for next generation device applications.
Coordination-driven self-assembly has been extensively employed to construct a variety of discrete structures as a bottom-up strategy. However, mechanistic understanding regarding whether self-assembly is under kinetic or thermodynamic control is less explored. To date, such mechanistic investigation has been limited to distinct, assembled structures. It still remains a formidable challenge to study the kinetic and thermodynamic behavior of self-assembly systems with multiple assembled isomers due to the lack of characterization methods. Herein, we use a stepwise strategy which combined self-recognition and self-assembly processes to construct giant metallo-supramolecules with 8 positional isomers in solution. With the help of ultrahigh-vacuum, low-temperature scanning tunneling microscopy and scanning tunneling spectroscopy, we were able to unambiguously differentiate 14 isomers on the substrate which correspond to 8 isomers in solution. Through measurement of 162 structures, the experimental probability of each isomer was obtained and compared with the theoretical probability. Finally, such a comparison along with density functional theory (DFT) calculation suggested that although both kinetic and thermodynamic control existed in this self-assembly, the increased experimental probabilities of isomers compared to theoretical probabilities should be attributed to thermodynamic control.
Four-dimensional scanning transmission electron microscopy (4D-STEM) is a versatile analytical tool for characterizing materials structural properties. However, extending such analysis to disordered materials is challenging, especially in technologically important samples with mixed ordered and disordered phases. Here, in this work, we present a new 4D-STEM method, called fluctuation cepstral STEM (FC-STEM), based on the fluctuation analysis of cepstral transform of diffraction patterns. The peaks in the associated transformation relate to inter-atomic distances in a thin sample. By varying the real-space range over which fluctuations are calculated, distinct ordered and disordered phases can be mapped in a diffractive image reconstruction. We demonstrate the principles of FC-STEM by characterizing a silicon anode, harvested from a cycled lithium-ion battery. A mixture of amorphous and nanocrystalline silicon, graphitic carbon, and electrolyte by-products is identified and mapped. Comparisons with conventional electron imaging and energy-dispersive X-ray spectroscopy show that FC-STEM is highly effective for the structure determination of mixed-phase amorphous materials.
A mathematical model of image contrast in scanning electron acoustic microscopy (SEAM) due to the effect of residual stresses in materials is presented. It is found that in regions near the ends of the radial cracks induced by Vickers indentation the SEAM micrographs reveal a rather large variation of the acoustic output signal.
We present a comprehensive strategy and its practical implementation using the commercial ScanImage software platform to perform hyperspectral point scanning microscopy when a fast time-dependent signal varies at each pixel level. In the proposed acquisition scheme, the scan along the X-axis is slowed down while the data acquisition is maintained at a high pace to enable the rapid acquisition of the time-dependent signal at each pixel level. The ScanImage generated raw 2D images have a very asymmetric aspect ratio between X and Y, the X axis encoding both for space and time acquisition. The results are X-axis macro-pixel where the associated time-dependent signal is sampled to provide hyperspectral information. We exemplified the proposed hyperspectral scheme in the context of time-domain coherent Raman imaging, where a pump pulse impulsively excites molecular vibrations that are subsequently probed by a time-delayed probe pulse. In this case, the time-dependent signal is a fast acousto-optics delay line that can scan a delay of 4.5ps in 25 μ s at each pixel level. With this acquisition scheme, we demonstrate ultra-fast hyperspectral vibrational imaging in the low frequency range [10 cm −1 , 150 cm −1 ] over a 500 μm field of view (64 x 64 pixels) in 130ms (∼ 7.5 frames/s). The proposed acquisition scheme can be readily extended to other applications requiring the acquisition of a fast-evolving signal at each pixel level.
Material thermal conductivity is a key factor in various applications, from thermal management to energy harvesting. With microstructure engineering being a widely used method for customizing material properties, including thermal properties, understanding and controlling the role of extended phonon-scattering defects, like grain boundaries, is crucial for efficient material design. However, systematic studies are still lacking primarily due to limited tools. In this study, we demonstrate an approach for measuring grain boundary thermal resistance by probing the propagation of thermal waves across grain boundaries with a temperature-sensitive scanning probe. The method, implemented with a spatial resolution of about 100 nm on finely grained Nb-substituted SrTiO 3 ceramics, achieves a detectability of about 2 × 10 –8 K m 2 W –1 , suitable for chalcogenide-based thermoelectrics. The measurements indicated that the thermal resistance of the majority of grain boundaries in the STiO 3 ceramics is below this value. While there are challenges in improving sensitivity, considering spatial resolution and the amount of material involved in the detection, the sensitivity of the scanning probe method is comparable to that of optical thermoreflectance techniques, and the method opens up an avenue to characterize thermal resistance at the level of single grain boundaries and domain walls in a spectrum of microstructured materials.
The increasing size, severity, and frequency of wildfires have led to dramatic increases in particulate matter concentrations in the troposphere. Severe wildfires can generate intense convective systems capable of transporting large quantities of biomass burning organic aerosols (BBOA) to the upper troposphere and lower stratosphere (UTLS). Chemically complex organic matter and light-absorbing carbonaceous material is introduced into stratospheric regions that were historically isolated from direct surface emissions. In this study, stratospheric particles were sampled over North America during the Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) campaign, an aircraft-based research project designed to characterize convective perturbation in the UTLS. Particle samples collected from six research flights during summer 2022 were analyzed using Computer-Controlled Scanning Electron Microscopy and Scanning Transmission X-ray Microscopy to investigate particle size distributions, morphology, chemical composition, and mixing state of stratospheric particles along transects across the continental United States and adjacent Pacific Ocean airspace. Analysis revealed that all sampled particles contained detectable levels of carbon, with most exhibiting organic volume fractions of 0.37 ± 0.20. Notably, about 5% of the particles also contained soot inclusions, which indicates the presence of refractory black carbon transported to stratospheric altitudes and provides direct evidence of wildfire-derived black carbon reaching the UTLS. Typical particle morphology exhibits organic shells over soot and inorganic cores and suggests secondary processing and aging of BBOA during transport to and within the UTLS. These findings provide compelling evidence that wildfire emissions play a critical role in affecting the long-term composition and radiative properties of stratospheric particles.
Whiting events—the episodic precipitation of fine-grained suspended calcium carbonates in the water column—have been documented across a variety of marine and lacustrine environments. Whitings likely are a major source of carbonate muds, a constituent of limestones, and important archives for geochemical proxies of Earth history. While several biological and physical mechanisms have been proposed to explain the onset of these precipitation events, no consensus has been reached thus far. Fayetteville Green Lake (New York, USA) is a meromictic lake that experiences annual whitings. Materials suspended in the water column collected through the whiting season were characterized using scanning electron microscopy and scanning transmission X-ray microscopy. Whitings in Fayetteville Green Lake are initiated in the spring within the top few meters of the water column, by precipitation of fine amorphous calcium carbonate (ACC) phases nucleating on microbial cells, as well as on abundant extracellular polymeric substances (EPS) frequently associated with centric diatoms. Whiting particles found in the summer consist of 5–7 μm calcite grains forming aggregates with diatoms and EPS. Simple calculations demonstrate that calcite particles continuously grow over several days, then sink quickly through the water column. In the late summer, partial calcium carbonate dissolution is observed deeper in the water column. Settling whiting particles, however, reach the bottom of the lake, where they form a major constituent of the sediment, along with diatom frustules. The role of diatoms and associated EPS acting as nucleation surfaces for calcium carbonates is described for the first time here as a potential mechanism participating in whitings at Fayetteville Green Lake. This mechanism may have been largely overlooked in other whiting events in modern and ancient environments.
Polarization dynamics in ferroelectric materials is governed by the effective potential energy landscape of the order parameter. The unique aspect of ferroelectrics compared to many other transitions is the possibility of more than two potential wells, leading to complicated energy landscapes with new fundamental and functional properties. In this work, direct dynamic evidence is revealed of a triple-well potential in the metal thiophosphate Sn 2 P 2 S 6 compound using multivariate scanning probe microscopy combined with theoretical simulations. The key finding is that the metastable zero polarization state can be accessed through a gradual switching process and is stabilized over a broad range of electric fields. Simulations confirm that the observed zero polarization state originates from a kinetic stabilization of the nonpolar state of the triple-well, as opposed to domain walls. Dynamically, the triple-well of Sn 2 P 2 S 6 becomes equivalent to antiferroelectric hysteresis loops. Therefore, this material combines the robust and well-defined domain structure of a proper ferroelectric with dynamic hysteresis loops present in antiferroelectrics. Moreover, the triple-well enhances mem-capacitive effects in Sn 2 P 2 S 6 , which are forbidden for ideal double-well ferroelectrics. These findings provide a path to tunable electronic elements for beyond binary high-density computing devices and neuromorphic circuits based on dynamic properties of the triple-well.
The results of a combined grazing incidence wide-angle X-ray scattering (GIWAXS) and 4D scanning transmission microscopy (4D-STEM) analysis of the effects of thermal processing on poly(3[2-(2-methoxyethoxy)ethoxy]-methylthiophene-2,5-diyl) are reported, a conjugated semiconducting polymer used as the active layer in organic electrochemical transistor devices. GIWAXS provides a measure of overall crystallinity in the film, while 4D-STEM produces real-space maps of the morphology and orientation of individual crystallites along with their spatial extent and distribution. The sensitivity of the 4D-STEM detector allows for collection of electron diffraction patterns at each position in an image scan while limiting the imparted electron dose to below the damage threshold. In conclusion, the effects of heat treatment on the distribution and type of crystallites present in the films is determined.
Local resistance imaging can provide information on nm-scale carrier distribution in semiconductor devices. Scanning spreading resistance microscopy (SSRM), an atomic force microscopy-based nm-scale resistance mapping technique, has been developed for carrier delineation in Si microdevices. We report on the development and validation of SSRM on CdTe materials, by testing on molecular beam epitaxy (MBE) grown CdTe films. The probe/CdTe contact resistance was suppressed sufficiently below sample's spreading resistance by pressing the probe into the sample with ∼mN contact force and applying a large sample/probe forward bias voltage (V s ), which was understood by analyzing current-voltage (I-V) involving a serially connected insulating top layer with underlying spreading resistance. The carrier concentration as deduced from the resistance measurement, using a single mobility value, is consistent with Hall measurement with a standard deviation of 14% based on a set of MBE films with carrier concentrations in the range of 10 15 –10 16 /cm 3 . The doping polarity was readily identified by flipping V s polarity, where the resistance with reverse V s is orders of magnitude larger than forward V s . While focusing on the SSRM technique validation, we also show an example on an As-doped Cd(Se,Te) polycrystalline thin film of a high-performance CdTe solar cell, which illustrates the local resistance nonuniformity with up to two orders of magnitude differences, indicating if local mobility is roughly constant, local carrier concentration can have significant nonuniformity.
X-ray fluorescence (XRF) microscopy is a powerful technique for quantifying the distribution of elements in complex materials, which makes it a crucial imaging technique across a wide range of disciplines in physical and biological sciences, including chemistry, materials science, microbiology, and geosciences. However, as a scanning microscopy technique, the spatial resolution of XRF imaging is inherently constrained by the x-ray probe profile and scanning step size. Here we propose a dual-branch machine learning (ML) model, which can extract scale-variant features and bypass abundant low-frequency information separately, to enhance the spatial resolution of the XRF images by mitigating the effects of blurring from the probe profile. The model is trained by simulated natural images, and a two-stage training strategy is used to overcome the domain gap between the natural images and experimental data. The tomography reconstruction from enhanced XRF projections shows an improvement in resolution by a scale factor of four and reveals distinct internal features invisible in low-resolution XRF within a battery sample. This study offers a promising approach for obtaining high-resolution XRF imaging from its low-resolution version, paving the way for future investigations in a broader range of disciplines and materials.
In situ and operando (scanning) transmission electron microscopy [(S)TEM] is a powerful characterization technique that uses imaging, diffraction, and spectroscopy to gain nano-to-atomic scale insights into the structure–property relationships in materials. This technique is both customizable and complex because many factors impact the ability to collect structural, compositional, and bonding information from a sample during environmental exposure or under application of an external stimulus. In the past two decades, in situ and operando (S)TEM methods have diversified and grown to encompass additional capabilities, higher degrees of precision, dynamic tracking abilities, enhanced reproducibility, and improved analytical tools. Much of this growth has been shared through the community and within commercialized products that enable rapid adoption and training in this approach. This tutorial aims to serve as a guide for students, collaborators, and nonspecialists to learn the important factors that impact the success of in situ and operando (S)TEM experiments and assess the value of the results obtained. As this is not a step-by-step guide, readers are encouraged to seek out the many comprehensive resources available for gaining a deeper understanding of in situ and operando (S)TEM methods, property measurements, data acquisition, reproducibility, and data analytics.