The long-term stability of electrocatalysts is increasingly recognized as a key parameter for catalyst design. However, it is still less emphasized in fundamental electrocatalysis studies compared to applied research on fuel cells and batteries. A valuable metric for measuring catalyst durability is turnover number, which indicates the maximum product yield from a catalytic center. Traditionally, turnover number for a specific catalyst is assessed on a macroscopic scale, providing a single value with error margins under specific reaction conditions. We have developed a new method to quantitatively assess both the number of active sites on individual platinum nanoparticles and their overall catalytic charge during electrochemical hydrogen evolution at high current densities (>0.4 A/cm²). This approach enables the direct measurement of the turnover number of individual monodispersed nanoparticles, revealing a wide statistical distribution spanning more than an order of magnitude. We further demonstrate that the heterogeneity is influenced by nanoparticle size and applied potential. These findings establish this method as a powerful accelerated testing platform for assessing macroscopic catalyst performance, which is strongly influenced by underlying nanoscale heterogeneity. To further investigate the origins of catalyst deactivation, we employed electrochemical correlative microscopy, which combines scanning electrochemical cell microscopy with transmission electron microscopy. We determined that deactivation of individual Pt nanoparticles seems to involve surface-blocking effects (such as nanobubble formation or subsurface hydrogen incorporation), consistent across nanoimpact, microelectrode, and scanning electrochemical cell microscopy measurements. Together, single-particle turnover number quantification, electrochemical correlative microscopy imaging, and multiscale measurements create a unified framework for linking nanoscale electrocatalytic behavior to macroscopic performance. This work advances the mechanistic understanding of catalyst degradation. Understanding these processes from the bottom up is crucial for predictive catalyst design and the early detection of catalyst degradation. Building on this platform, we plan on investigating a deliberately poisoned system using industrially relevant inhibitors, addressing a critical challenge for fuel-cell technologies.

High-throughput analysis of multidimensional transmission electron microscopy (TEM) datasets remains a significant challenge, restricting TEM's broader applicability in strategic materials research. Conventional workflows typically involve sequential, modular processing steps that necessitate extensive manual intervention and offline parameter tuning. In this work, we introduce an end-to-end post-processing framework for large-scale four-dimensional scanning transmission electron microscopy (4D-STEM) datasets, built around a highly efficient neural network-based object detection model. Central to our method is a sub-pixel accurate object center localization algorithm, which serves as the foundation for high-precision and high-throughput analysis of electron diffraction patterns. We demonstrate a strain measurement precision of 5x10(-4), quantified by the standard deviation of strain values within the strain-free Si substrate of a Si/SiGe multilayer TEM sample. Furthermore, by implementing an asynchronous, non-blocking object detection workflow, we achieve speeds exceeding 100 frames per second (fps), substantially accelerating the crystallographic phase identification and strain mapping in complex multiphase metallic alloys.

Modern aberration-corrected scanning transmission electron microscopes can acquire four-dimensional data sets ("4D STEM") by recording convergent beam electron diffraction (CBED) patterns, using precisely positioned, sub-angstrom probes. Here, we demonstrate that these patterns can probe the site symmetry, atomic displacements, and valence electron distributions at individual atomic columns. To this end, 4D STEM CBED patterns were acquired from SrTiO3 single crystals and compared with patterns calculated using scattering potentials derived from density functional theory. We show that an aspherical valence electron charge build-up at the oxygen sites causes intensity asymmetries in the low-angle scattering portion of the patterns. Using strained SrTiO3 films containing subtle polar displacements within nanometer-sized domains, it is shown that the high-angle scattering portion in each pattern is sensitive to atomic displacements.

Accurate and efficient characterization of nanoparticles (NPs), particularly regarding particle size distribution, is essential for advancing our understanding of their structure-property relationship and facilitating their design for various applications. In this study, we introduce a novel two-stage artificial intelligence (AI)-driven workflow for NP analysis that leverages prompt engineering techniques from state-of-the-art single-stage object detection and large-scale vision transformer (ViT) architectures. This methodology is applied to transmission electron microscopy (TEM) and scanning TEM (STEM) images of heterogeneous catalysts, enabling high-resolution, high-throughput analysis of particle size distributions for supported metal catalyst NPs. The model's performance in detecting and segmenting NPs is validated across diverse heterogeneous catalyst systems, including various metals (Ru, Cu, PtCo, and Pt), supports (silica (SiO2), γ-alumina (γ-Al2O3), and carbon black), and particle diameter size distributions with mean and standard deviations ranging from 1.6 ± 0.2 nm to 9.7 ± 4.6 nm. The proposed machine learning (ML) methodology achieved an average F1 overlap score of 0.91 ± 0.01 and demonstrated the ability to disentangle overlapping NPs anchored on catalytic support materials. The segmentation accuracy is further validated using the Hausdorff distance and robust Hausdorff distance metrics, with the 90th percent of the robust Hausdorff distance showing errors within 0.4 ± 0.1 nm to 1.4 ± 0.6 nm. Our AI-assisted NP analysis workflow demonstrates robust generalization across diverse datasets and can be readily applied to similar NP segmentation tasks without requiring costly model retraining.

Strontium titanate (SrTiO 3 ) can exhibit multiple orders, including superconductivity, an antiferrodistortive instability, and ferroelectricity. The cooperation or competition between these orders in samples that undergo all three transitions is of great fundamental interest. Here, we report scanning transmission electron microscopy imaging of the antiferrodistortive and ferroelectric structural distortions in a compressively strained SrTiO 3 film that was previously shown to become superconducting at similar to 410 mK. The experiments are complemented by first -principles simulations. Unlike the polar ferroelectric phase, which is suppressed by dopants, the antiferrodistortive order is insensitive to the presence of the free carriers. The single -domain nature of the antiferrodistortive phase excludes any role of antiferrodistortive domain walls in the superconductivity. A previously reported low -temperature resistance anomaly is associated with the ferroelectric transition, not the antiferrodistortive transition.

Journal Article Probing Local Symmetry Breaking of EuxSr1-xTiO3 Films with HAADF-STEM Get access Guomin Zhu, Guomin Zhu Materials Department, University of California, Santa Barbara, CA, United States Search for other works by this author on: Oxford Academic Google Scholar Nicholas G Combs, Nicholas G Combs Materials Department, University of California, Santa Barbara, CA, United States Search for other works by this author on: Oxford Academic Google Scholar Binghao Guo, Binghao Guo Materials Department, University of California, Santa Barbara, CA, United States Search for other works by this author on: Oxford Academic Google Scholar Arda Genc, Arda Genc Materials Department, University of California, Santa Barbara, CA, United States Search for other works by this author on: Oxford Academic Google Scholar Susanne Stemmer Susanne Stemmer Materials Department, University of California, Santa Barbara, CA, United States Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Pages 1680–1681, https://doi.org/10.1093/micmic/ozad067.864 Published: 22 July 2023

Journal Article Gated Dense Convolutional Neural Networks for Unbalanced Representations in STEM Tomography Get access Arda Genc, Arda Genc Center for the Accelerated Maturation of Materials, Department of Materials Science and Engineering, The Ohio State University, Columbus, OH, USA Corresponding author: genc.2@osu.edu Search for other works by this author on: Oxford Academic Google Scholar Libor Kovarik, Libor Kovarik Institute for Integrated Catalysis, Pacific Northwest National Laboratory, Richland, WA, USA Search for other works by this author on: Oxford Academic Google Scholar Hamish L Fraser Hamish L Fraser Center for the Accelerated Maturation of Materials, Department of Materials Science and Engineering, The Ohio State University, Columbus, OH, USA Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 3136–3137, https://doi.org/10.1017/S1431927622011667 Published: 01 August 2022

In computed TEM tomography, image segmentation represents one of the most basic tasks with implications not only for 3D volume visualization, but more importantly for quantitative 3D analysis. In case of large and complex 3D data sets, segmentation can be an extremely difficult and laborious task, and thus has been one of the biggest hurdles for comprehensive 3D analysis. Heterogeneous catalysts have complex surface and bulk structures, and often sparse distribution of catalytic particles with relatively poor intrinsic contrast, which possess a unique challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a γ-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net’s fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 ± 0.003 in the γ-Alumina support material and 0.84 ± 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for γ-Alumina and Pt NPs segmentations. The complex surface morphology of γ-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions.

The solid-electrolyte interphase (SEI), a layer formed on the electrode surface, is essential for electrochemical reactions in batteries and critically governs the battery stability. Active materials, especially those with extremely high energy density, such as silicon (Si), often inevitably undergo a large volume swing upon ion insertion and extraction, raising a critical question as to how the SEI interactively responds to and evolves with the material and consequently controls the cycling stability of the battery. Here, by integrating sensitive elemental tomography, an advanced algorithm and cryogenic scanning transmission electron microscopy, we unveil, in three dimensions, a correlated structural and chemical evolution of Si and SEI. Corroborated with a chemomechanical model, we demonstrate progressive electrolyte permeation and SEI growth along the percolation channel of the nanovoids due to vacancy injection and condensation during the delithiation process. Consequently, the Si-SEI spatial configuration evolves from the classic 'core-shell' structure in the first few cycles to a 'plum-pudding' structure following extended cycling, featuring the engulfing of Si domains by the SEI, which leads to the disruption of electron conduction pathways and formation of dead Si, contributing to capacity loss. The spatially coupled interactive evolution model of SEI and active materials, in principle, applies to a broad class of high-capacity electrode materials, leading to a critical insight for remedying the fading of high-capacity electrodes.

Catalytic CO2 reduction to fuels and chemicals is a major pursuit in reducing greenhouse gas emissions. One approach utilizes the reverse water-gas shift reaction, followed by Fischer-Tropsch synthesis, and iron is a well-known candidate for this process. Some attempts have been made to modify and improve its reactivity, but resulted in limited success. Now, using ruthenium-iron oxide colloidal heterodimers, close contact between the two phases promotes the reduction of iron oxide via a proximal hydrogen spillover effect, leading to the formation of ruthenium-iron core-shell structures active for the reaction at significantly lower temperatures than in bare iron catalysts. Furthermore, by engineering the iron oxide shell thickness, a fourfold increase in hydrocarbon yield is achieved compared to the heterodimers. This work shows how rational design of colloidal heterostructures can result in materials with significantly improved catalytic performance in CO2 conversion processes.

Understanding the unique properties of ultra-wide band gap semiconductors requires detailed information about the exact nature of point defects and their role in determining the properties. Here, we report the first direct microscopic observation of an unusual formation of point defect complexes within the atomic-scale structure of beta-Ga2O3 using high resolution scanning transmission electron microscopy (STEM). Each complex involves one cation interstitial atom paired with two cation vacancies. These divacancy-interstitial complexes correlate directly with structures obtained by density functional theory, which predicts them to be compensating acceptors in beta-Ga2O3. This prediction is confirmed by a comparison between STEM data and deep level optical spectroscopy results, which reveals that these complexes correspond to a deep trap within the band gap, and that the development of the complexes is facilitated by Sn doping through increased vacancy concentration. These findings provide new insight on this emerging material's unique response to the incorporation of impurities that can critically influence their properties.

We present the properties of a photo -catalytically activated CoTiO 3 framework supported on TiO 2 (rutile). To demonstrate the effectiveness of this TiO 2 -CoTiO 3 compound under light illumination, we tested its degradation potential against acid orange 7 dye. The dye degradation is effective over a wide range of laser excitation wavelengths from 1064 nm to 532 nm as well as at sunlight simulation conditions. The TiO 2 -CoTiO 3 compound particles have sizes from 100 nm to a few microns and provide a surprising photocatalytic enhancement under 1064 nm pulsed laser excitation, that is, at photon energies well below the light absorption edge. We attribute the high activity of this material to the interphases between the rutile and CoTiO 3 and discuss a number of possible mechanisms for the photocatalytic enhancement under pulsed laser excitations. The dye degradation activity of TiO 2 -CoTiO 3 increases up to two orders of magnitude when compared to pure TiO 2 or 20at%Co-TiO 2 with TiO 2 in either phases, amorphous or anatase.

Using in-situ techniques, which couple energy dispersive spectroscopy mapping with a heated transmission electron microscope stage, solid-state diffusion of Ni-Al is studied in the temperature range 623-723 K with characteristic diffusion lengths of 1-100 nm. When the concentration profiles are analyzed using the Sauer-Freise method, and evaluated for 50 atomic percent Ni, the diffusion coefficients follow an Arrhenius temperature dependence: D-Ni -> Al = 8.2x10(-9) exp(- 111 kJ/RT)m(2)/s. Additionally, the formation of Al-rich intermetallic phases (Al3Ni & Al3Ni2) is shown to occur within heating durations of a second at the studied temperatures.

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In this report, we examine the structure of bimetallic nanomaterials prepared by an electrochemical approach known as hydride-terminated (HT) electrodeposition. It has been shown previously that this method can lead to deposition of a single Pt monolayer on bulk-phase Au surfaces. Specifically, under appropriate electrochemical conditions and using a solution containing PtCl42-, a monolayer of Pt atoms electrodeposits onto bulk-phase Au immediately followed by a monolayer of H atoms. The H atom capping layer prevents deposition of Pt multilayers. We applied this method to ∼1.6 nm Au nanoparticles (AuNPs) immobilized on an inert electrode surface. In contrast to the well-defined, segregated Au/Pt structure of the bulk-phase surface, we observe that HT electrodeposition leads to the formation of AuPt quasi-random alloy NPs rather than the core@shell structure anticipated from earlier reports relating to deposition onto bulk phases. The results provide a good example of how the phase behavior of macro materials does not always translate to the nano world. A key component of this study was the structure determination of the AuPt NPs, which required a combination of electrochemical methods, electron microscopy, X-ray absorption spectroscopy, and theory (DFT and MD).

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In this paper, we show that the strengthening from (Ni, Al, Ti)-rich B2 particles in the recrystallized (Fe, Mn)-rich f.c.c. high entropy alloy (HEA) Fe42Ni12Mn36Al8Ti2 arises indirectly by reducing the grain size thus providing increased Hall-Petch strengthening, rather than directly through dislocation pinning, a concept first introduced by P.M. Hazzledine (Scripta Metall. Mater. 26 (1992) 57–58). Thus, the yield strength increased from 200MPa for the as-cast HEA to 509MPa for the recrystallized HEA. The contributions to the increase of the yield strength were calculated to be 28MPa from particle pinning and 281MPa from Hall-Petch strengthening. We also show using high angle angular dark field imaging in a scanning transmission electron microscope that Fe42Ni12Mn36Al8Ti2 exhibits no evidence of the severe lattice distortion that is a core tenet of HEAs. Both as-cast and recrystallized HEAs showed ductile fracture modes.

S/TEM sample preparation of aluminium and aluminium alloys to characterize grain boundary phases by focused ion beam (FIB) continues to be a major interest in metallurgical analysis because of FIB’s ability to prepare site specific specimens and eliminating damage from mechanical polishing or electro-polishing [1]. Recent instrumentation using plasma FIB (PFIB) technology and Xe ions offer increased milling rates because of its ability to deliver 30 – 40 times more current compared to Ga FIBs. While the measured sputter rate of aluminum using Ga and Xe differs by about 25% (0.31 m/nC [Ga] and 0.41 m/nC [Xe]), the ability to use more current for micromachining will allow users to increase throughput significantly and prepare much larger cross-sections for S/TEM sample preparation if PFIB is employed. Therefore, it is of interest to understand the amount of FIB damage introduced into the sidewall of a thin section of aluminum by FIB. 30 kV FIB damage employing a different preparation method has been measured to be ~ 4 nm [2].

The kinetics of energy storage in transition metal oxides are usually limited by solid-state diffusion, and the strategy most often utilized to improve their rate capability is to reduce ion diffusion distances by utilizing nanostructured materials. Here, another strategy for improving the kinetics of layered transition metal oxides by the presence of structural water is proposed. To investigate this strategy, the electrochemical energy storage behavior of a model hydrated layered oxide, WO3. 2H(2)O, is compared with that of anhydrous WO3 in an acidic electrolyte. It is found that the presence of structural water leads to a transition from battery-like behavior in the anhydrous WO3 to ideally pseudocapacitive behavior in WO3 center dot 2H(2)O. As a result, WO3 center dot 2H(2)O exhibits significantly improved capacity retention and energy efficiency for proton storage over WO3 at sweep rates as fast as 200 mV s(-1), corresponding to charge/discharge times of just a few seconds. Importantly, the energy storage of WO3. 2H(2)O at such rates is nearly 100% efficient, unlike in the case of anhydrous WO3. Pseudocapacitance in WO3 center dot 2H(2)O allows for high-mass loading electrodes (>3 mg cm(-2)) and high areal capacitances (>0.25 F cm(-2) at 200 mV s(-1)) with simple slurry-cast electrodes. These results demonstrate a new approach for developing pseudocapacitance in layered transition metal oxides for high-power energy storage, as well as the importance of energy efficiency as a metric of performance of pseudocapacitive materials.

Abstract This paper reports on the substantial improvement of specimen quality by use of a low voltage (0.05 to ~1 keV), small diameter (~1 μm), argon ion beam following initial preparation using conventional broad-beam ion milling or focused ion beam. The specimens show significant reductions in the amorphous layer thickness and implanted artifacts. The targeted ion milling controls the specimen thickness according to the needs of advanced aberration-corrected and/or analytical transmission electron microscopy applications.