Clamping enables enhanced electromechanical responses in antiferroelectric thin films.

Thin-film materials with large electromechanical responses are fundamental enablers of next-generation micro-/nano-electromechanical applications. Conventional electromechanical materials (for example, ferroelectrics and relaxors), however, exhibit severely degraded responses when scaled down to submicrometre-thick films due to substrate constraints (clamping). This limitation is overcome, and substantial electromechanical responses in antiferroelectric thin films are achieved through an unconventional coupling of the field-induced antiferroelectric-to-ferroelectric phase transition and the substrate constraints. A detilting of the oxygen octahedra and lattice-volume expansion in all dimensions are observed commensurate with the phase transition using operando electron microscopy, such that the in-plane clamping further enhances the out-of-plane expansion, as rationalized using first-principles calculations. In turn, a non-traditional thickness scaling is realized wherein an electromechanical strain (1.7%) is produced from a model antiferroelectric PbZrO3 film that is just 100 nm thick. The high performance and understanding of the mechanism provide a promising pathway to develop high-performance micro-/nano-electromechanical systems.

Modeling Temperature-Dependent Electron Thermal Diffuse Scattering via Machine-Learned Interatomic Potentials and Path-Integral Molecular Dynamics.

Electron thermal diffuse scattering is shown to be sensitive to subtle changes in atomic vibrations and shows promise in assessing lattice dynamics at nanometer resolution. Here, we demonstrate that machine-learned interatomic potentials (MLIPs) and path-integral molecular dynamics can accurately capture the potential energy landscape and lattice dynamics needed to describe electron thermal diffuse scattering. Using SrTiO_{3} as a test bed at cryogenic and room temperatures, we compare electron thermal diffuse scattering simulations using different approximations to incorporate thermal motion. Only when the simulations are based on quantum mechanically accurate MLIPs in combination with path-integral molecular dynamics that include nuclear quantum effects is there excellent agreement with experiments.

A Methodology for Robust Multislice Ptychography.

While multislice electron ptychography can provide thermal vibration limited resolution and structural information in 3D, it relies on properly selecting many intertwined acquisition and computational parameters. Here, we outline a methodology for selecting acquisition parameters to enable robust ptychographic reconstructions. We develop two physically informed metrics, areal oversampling and Ronchigram magnification, to describe the selection of these parameters in multislice ptychography. Through simulations, we comprehensively evaluate the validity of these two metrics over a broad range of conditions and show that they accurately guide reconstruction success. Further, we validate these conclusions with experimental ptychographic data and demonstrate close agreement between trends in simulated and experimental data. Using these metrics, we achieve experimental multislice reconstructions at a scan step of 2.1Å/px, enabling large field-of-view, data-efficient reconstructions. These experimental design principles enable the routine and robust use of multislice ptychography for 3D characterization of materials at the atomic scale.

Machine Learning-Enabled Superior Energy Storage in Ferroelectric Films with a Slush-Like Polar State.

Heterogeneities in structure and polarization have been employed to enhance the energy storage properties of ferroelectric films. The presence of nonpolar phases, however, weakens the net polarization. Here, we achieve a slush-like polar state with fine domains of different ferroelectric polar phases by narrowing the large combinatorial space of likely candidates using machine learning methods. The formation of the slush-like polar state at the nanoscale in cation-doped BaTiO3 films is simulated by phase field simulation and confirmed by aberration-corrected scanning transmission electron microscopy. The large polarization and the delayed polarization saturation lead to greatly enhanced energy density of 80 J/cm3 and transfer efficiency of 85% over a wide temperature range. Such a data-driven design recipe for a slush-like polar state is generally applicable to quickly optimize functionalities of ferroelectric materials.

Nanotwinning-assisted dynamic recrystallization at high strains and strain rates.

Grain refinement is a widely sought-after feature of many metal production processes and frequently involves a process of recrystallization. Some processing methods use very high strain rates and high strains to refine the grain structure into the nanocrystalline regime. However, grain refinement processes are not clear in these extreme conditions, which are hard to study systematically. Here, we access those extreme conditions of strain and strain rate using single copper microparticle impact events with a laser-induced particle impact tester. Using a combined dictionary-indexing electron backscatter diffraction and scanning transmission electron microscopy approach for postmortem characterization of impact sites, we systematically explore increasing strain levels and observe a recrystallization process that is facilitated by nanotwinning, which we term nanotwinning-assisted dynamic recrystallization. It achieves much finer grain sizes than established modes of recrystallization and therefore provides a pathway to the finest nanocrystalline grain sizes through extreme straining processes.

Tunable Artificial Relaxor Behavior in [BaTiO_{3}]_{m}/[BaZrO_{3}]_{n} Superlattices.

[BaTiO_{3}]_{m}/[BaZrO_{3}]_{n} (m, n=4-12) superlattices are used to demonstrate the fabrication and deterministic control of an artificial relaxor. X-ray diffraction and atomic-resolution imaging studies confirm the production of high-quality heterostructures. With decreasing BaTiO_{3} layer thickness, dielectric measurements reveal systematically lower dielectric-maximum temperatures, while hysteresis loops and third-harmonic nonlinearity studies suggest a transition from ferroelectriclike to relaxorlike behavior driven by tuning the random-field strength. This system provides a novel platform for studying the size effect and interaction length scale of the nanoscale-polar structures in relaxors.

Exsolution-Driven Surface Transformation in the Host Oxide.

Exsolution synthesizes self-assembled metal nanoparticle catalysts via phase precipitation. An overlooked aspect in this method thus far is how exsolution affects the host oxide surface chemistry and structure. Such information is critical as the oxide itself can also contribute to the overall catalytic activity. Combining X-ray and electron probes, we investigated the surface transformation of thin-film SrTi0.65Fe0.35O3 during Fe0 exsolution. We found that exsolution generates a highly Fe-deficient near-surface layer of about 2 nm thick. Moreover, the originally single-crystalline oxide near-surface region became partially polycrystalline after exsolution. Such drastic transformations at the surface of the oxide are important because the exsolution-induced nonstoichiometry and grain boundaries can alter the oxide ion transport and oxygen exchange kinetics and, hence, the catalytic activity toward water splitting or hydrogen oxidation reactions. These findings highlight the need to consider the exsolved oxide surface, in addition to the metal nanoparticles, in designing the exsolved nanocatalysts.

Atomic-resolution electron microscopy of nanoscale local structure in lead-based relaxor ferroelectrics.

Relaxor ferroelectrics, which can exhibit exceptional electromechanical coupling, are some of the most important functional materials, with applications ranging from ultrasound imaging to actuators. Since their discovery, their complex nanoscale chemical and structural heterogeneity has made the origins of their electromechanical properties extremely difficult to understand. Here, we employ aberration-corrected scanning transmission electron microscopy to quantify various types of nanoscale heterogeneities and their connection to local polarization in the prototypical relaxor ferroelectric system Pb(Mg1/3Nb2/3)O3-PbTiO3. We identify three main contributions that each depend on Ti content: chemical order, oxygen octahedral tilt and oxygen octahedral distortion. These heterogeneities are found to be spatially correlated with low-angle polar domain walls, indicating their role in disrupting long-range polarization and leading to nanoscale domain formation and the relaxor response. We further locate nanoscale regions of monoclinic-like distortion that correlate directly with Ti content and electromechanical performance. Through this approach, the connections between chemical heterogeneity, structural heterogeneity and local polarization are revealed, validating models that are needed to develop the next generation of relaxor ferroelectrics.

Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflows.

Here, we report a case study implementation of reinforcement learning (RL) to automate operations in the scanning transmission electron microscopy workflow. To do so, we design a virtual, prototypical RL environment to test and develop a network to autonomously align the electron beam position without prior knowledge. Using this simulator, we evaluate the impact of environment design and algorithm hyperparameters on alignment accuracy and learning convergence, showing robust convergence across a wide hyperparameter space. Additionally, we deploy a successful model on the microscope to validate the approach and demonstrate the value of designing appropriate virtual environments. Consistent with simulated results, the on-microscope RL model achieves convergence to the goal alignment after minimal training. Overall, the results highlight that by taking advantage of RL, microscope operations can be automated without the need for extensive algorithm design, taking another step toward augmenting electron microscopy with machine learning methods.

Expanding the Dimensions of a Small, Two-Dimensional Diffraction Detector.

We report an approach to expand the effective number of pixels available to small, two-dimensional electron detectors. To do so, we acquire subsections of a diffraction pattern that are then accurately stitched together in post-processing. Using an electron microscopy pixel array detector (EMPAD) that has only 128 × 128 pixels, we show that the field of view can be expanded while achieving high reciprocal-space sampling. Further, we highlight the need to properly account for the detector position (rotation) and the non-orthonormal diffraction shift axes to achieve an accurate reconstruction. Applying the method, we provide examples of spot and convergent beam diffraction patterns acquired with a pixelated detector.

An Ultrathin Single Crystalline Relaxor Ferroelectric Integrated on a High Mobility Semiconductor.

The epitaxial growth of multifunctional oxides on semiconductors has opened a pathway to introduce new functionalities to semiconductor device technologies. In particular, the integration of gate materials that enable nonvolatile or hysteretic functionality in field-effect transistors could lead to device technologies that consume less power or allow for novel modalities in computing. Here we present electrical characterization of ultrathin single crystalline SrZrxTi1-xO3 (x = 0.7) films epitaxially grown on a high mobility semiconductor, Ge. Epitaxial films of SrZrxTi1-xO3 exhibit relaxor behavior, characterized by a hysteretic polarization that can modulate the surface potential of Ge. We find that gate layers as thin as 5 nm corresponding to an equivalent-oxide thickness of just 1.0 nm exhibit a ∼2 V hysteretic window in the capacitance-voltage characteristics. The development of hysteretic metal-oxide-semiconductor capacitors with nanoscale gate thicknesses opens new vistas for nanoelectronic devices.

Accurate Nanoscale Crystallography in Real-Space Using Scanning Transmission Electron Microscopy.

Here, we report reproducible and accurate measurement of crystallographic parameters using scanning transmission electron microscopy. This is made possible by removing drift and residual scan distortion. We demonstrate real-space lattice parameter measurements with <0.1% error for complex-layered chalcogenides Bi2Te3, Bi2Se3, and a Bi2Te2.7Se0.3 nanostructured alloy. Pairing the technique with atomic resolution spectroscopy, we connect local structure with chemistry and bonding. Combining these results with density functional theory, we show that the incorporation of Se into Bi2Te3 causes charge redistribution that anomalously increases the van der Waals gap between building blocks of the layered structure. The results show that atomic resolution imaging with electrons can accurately and robustly quantify crystallography at the nanoscale.

Atom column indexing: atomic resolution image analysis through a matrix representation.

Here, we report the development of an approach to map atomic resolution images into a convenient matrix representation. Through the combination of two-dimensional Gaussian fitting and the projective standard deviation, atom column locations are projected onto two noncollinear reference lattice vectors that are used to assign each a unique (i, j) matrix index. By doing so, straightforward atomic resolution image analysis becomes possible. Using practical examples, we demonstrate that the matrix representation greatly simplifies categorizing atom columns to different sublattices. This enables a myriad of direct analyses, such as mapping atom column properties and correlating long-range atom column pairs. MATLAB source code can be downloaded from https://github.com/subangstrom/aci.

A comparison of molecular dynamics potentials used to account for thermal diffuse scattering in multislice simulations.

Here we investigate electron scattering simulations with thermal displacements incorporated using molecular dynamics potentials. Specifically, we explore the sensitivity of electron scattering to the phonon band structure, or more explicitly interatomic forces. Silicon serves as the model material where we introduce thermal atomic displacements via empirical and machine-learned molecular dynamics interatomic potentials and compare them to finite-temperature density functional theory interatomic forces. We demonstrate that when molecular dynamics potentials do not sufficiently reproduce the correct phonon band structure, significant errors in the simulated diffraction and image intensities can occur. Moreover, for Si, we find that multislice simulations using machine-learned interatomic potentials are more accurate than empirical ones. In addition to the selected atomic potential, we demonstrate that the sensitivity to the phonon band structure also depends on the crystal zone axis, which can be used to enhance sensitivity to thermal displacements. Finally, we provide a sensitivity analysis with angle-resolved scanning transmission electron microscopy (STEM) to enhance image sensitivity to the details of the phonon band structure.

Correlating local chemical and structural order using Geographic Information Systems-based spatial statistics.

Analysis of nanoscale short-range chemical and/or structural order via (scanning) transmission electron microscopy (S/TEM) imaging is fundamentally limited by projection of the three dimensional sample, which averages informational along the beam direction. Extracting statistically significant spatial correlations between the structure and chemistry determined from these two-dimensional datasets thus remains challenging. Here, we apply methods commonly used in Geographic Information Systems (GIS) to determine the spatial correlation between measures of local chemistry and structure from atomic-resolution STEM imaging of a compositionally complex relaxor, Pb(Mg1/3Nb2/3)O3 (PMN). The approach is used to determine the type of ordering present and to quantify the spatial variation of chemical order, oxygen octahedral distortions, and oxygen octahedral tilts. The extent of autocorrelation and inter-feature correlation among these short-range ordered regions are then evaluated through a spatial covariance analysis, showing correlation as a function of distance. The results demonstrate that integrating GIS tools for analyzing microscopy datasets can serve to unravel subtle relationships among chemical and structural features in complex materials that can be hidden when ignoring their spatial distributions.

Tuning Surface Acidity of Mixed Conducting Electrodes: Recovery of Si-Induced Degradation of Oxygen Exchange Rate and Area Specific Resistance.

Metal oxides are an important class of functional materials, and for many applications, ranging from solid oxide fuel/electrolysis cells, oxygen permeation membranes, and oxygen storage materials to gas sensors (semiconducting and electrolytic) and catalysts, the interaction between the surface and oxygen in the gas phase is central. Ubiquitous Si-impurities are known to impede this interaction, commonly attributed to the formation of glassy blocking layers on the surface. Here, the surface oxygen exchange coefficient (kchem ) is examined for Pr0.1 Ce0.9 O2-δ (PCO), a model mixed ionic electronic conductor, via electrical conductivity relaxation measurements, and the area-specific resistance (ASR) by electrochemical impedance spectroscopy. It is demonstrated that even low silica levels, introduced by infiltration, depress kchem by a factor 4000, while the ASR increases 40-fold and we attribute this to its acidity relative to that of PCO. The ability to fully regenerate the poisoned surface by the subsequent addition of basic Ca- or Li-species is further shown. This ability to not only recover Si-poisoned surfaces by tuning the relative surface acidity of an oxide surface, but subsequently outperform the pre-poisoned response, promises to extend the operating life of materials and devices for which the catalytic oxygen/solid interface reaction is central.