Helical van der Waals crystals with discretized Eshelby twist.

The ability to manipulate the twisting topology of van der Waals structures offers a new degree of freedom through which to tailor their electrical and optical properties. The twist angle strongly affects the electronic states, excitons and phonons of the twisted structures through interlayer coupling, giving rise to exotic optical, electric and spintronic behaviours1-5. In twisted bilayer graphene, at certain twist angles, long-range periodicity associated with moiré patterns introduces flat electronic bands and highly localized electronic states, resulting in Mott insulating behaviour and superconductivity3,4. Theoretical studies suggest that these twist-induced phenomena are common to layered materials such as transition-metal dichalcogenides and black phosphorus6,7. Twisted van der Waals structures are usually created using a transfer-stacking method, but this method cannot be used for materials with relatively strong interlayer binding. Facile bottom-up growth methods could provide an alternative means to create twisted van der Waals structures. Here we demonstrate that the Eshelby twist, which is associated with a screw dislocation (a chiral topological defect), can drive the formation of such structures on scales ranging from the nanoscale to the mesoscale. In the synthesis, axial screw dislocations are first introduced into nanowires growing along the stacking direction, yielding van der Waals nanostructures with continuous twisting in which the total twist rates are defined by the radii of the nanowires. Further radial growth of those twisted nanowires that are attached to the substrate leads to an increase in elastic energy, as the total twist rate is fixed by the substrate. The stored elastic energy can be reduced by accommodating the fixed twist rate in a series of discrete jumps. This yields mesoscale twisting structures consisting of a helical assembly of nanoplates demarcated by atomically sharp interfaces with a range of twist angles. We further show that the twisting topology can be tailored by controlling the radial size of the structure.

Mid-Infrared, Optically Active Black Phosphorus Thin Films on Centimeter Scale.

Black phosphorus (BP) is a narrow bandgap (∼0.3 eV) semiconductor with a great potential for optoelectronic devices in the mid-infrared wavelength. However, it has been challenging to achieve a high-quality scalable BP thin film. Here we present the successful synthesis of optically active BP films on a centimeter scale. We utilize the pulsed laser deposition of amorphous red phosphorus, another allotrope of phosphorus, followed by a high-pressure treatment at ∼8 GPa to induce a phase conversion into BP crystals. The crystalline quality was improved through thermal annealing, resulting in the observation of photoluminescence emission at mid-infrared wavelengths. We demonstrate high-pressure conversion on a centimeter scale with a continuous film with a thickness of ∼18 nm using a flat-belt-type high-pressure apparatus. This synthesis procedure presents a promising route to obtain optical-quality BP films, enabling the exploration of integrated optoelectronic device applications such as light-emitting devices and mid-infrared cameras on a chip scale.

Generalization Across Experimental Parameters in Neural Network Analysis of High-Resolution Transmission Electron Microscopy Datasets.

Neural networks are promising tools for high-throughput and accurate transmission electron microscopy (TEM) analysis of nanomaterials, but are known to generalize poorly on data that is "out-of-distribution" from their training data. Given the limited set of image features typically seen in high-resolution TEM imaging, it is unclear which images are considered out-of-distribution from others. Here, we investigate how the choice of metadata features in the training dataset influences neural network performance, focusing on the example task of nanoparticle segmentation. We train and validate neural networks across curated, experimentally collected high-resolution TEM image datasets of nanoparticles under various imaging and material parameters, including magnification, dosage, nanoparticle diameter, and nanoparticle material. Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters. Additionally, data preprocessing can have unintended consequences on neural network generalization. Our results highlight the need to understand how dataset features affect deployment of data-driven algorithms.

Direct Observation of Transient Structural Dynamics of Atomically Thin Halide Perovskite Nanowires.

Halide perovskite is a unique dynamical system, whose structural and chemical processes happening across different timescales have significant impact on its physical properties and device-level performance. However, due to its intrinsic instability, real-time investigation of the structure dynamics of halide perovskite is challenging, which hinders the systematic understanding of the chemical processes in the synthesis, phase transition, and degradation of halide perovskite. Here, we show that atomically thin carbon materials can stabilize ultrathin halide perovskite nanostructures against otherwise detrimental conditions. Moreover, the protective carbon shells enable atomic-level visualization of the vibrational, rotational, and translational movement of halide perovskite unit cells. Albeit atomically thin, protected halide perovskite nanostructures can maintain their structural integrity up to an electron dose rate of 10,000 e-/Å2·s while exhibiting unusual dynamical behaviors pertaining to the lattice anharmonicity and nanoscale confinement. Our work demonstrates an effective method to protect beam-sensitive materials during in situ observation, unlocking new solutions to study new modes of structure dynamics of nanomaterials.

Analysis of Interpretable Data Representations for 4D-STEM Using Unsupervised Learning.

Understanding the structure of materials is crucial for engineering devices and materials with enhanced performance. Four-dimensional scanning transmission electron microscopy (4D-STEM) is capable of mapping nanometer-scale local crystallographic structure over micron-scale field of views. However, 4D-STEM datasets can contain tens of thousands of images from a wide variety of material structures, making it difficult to automate detection and classification of structures. Traditional automated analysis pipelines for 4D-STEM focus on supervised approaches, which require prior knowledge of the material structure and cannot describe anomalous or deviant structures. In this article, a pipeline for engineering 4D-STEM feature representations for unsupervised clustering using non-negative matrix factorization (NMF) is introduced. Each feature is evaluated using NMF and results are presented for both simulated and experimental data. It is shown that some data representations more reliably identify overlapping grains. Additionally, real space refinement is applied to identify spatially distinct sample regions, allowing for size and shape analysis to be performed. This work lays the foundation for improved analysis of nanoscale structural features in materials that deviate from expected crystallographic arrangement using 4D-STEM.

Chemical and Structural Alterations in the Amorphous Structure of Obsidian due to Nanolites.

Obsidian is volcanic glass that results from the rapid cooling of silica-rich melt. Nanoscale crystallites precipitate out of the melt prior to solidification and remain embedded in the amorphous matrix. These crystallites provide information on the flow kinetics and composition of the melt. Due to the sparsity and size of nanolites, studies often focus on supramicron crystallites. This research takes advantage of the conchoidal fracture of obsidian by knapping samples with nanometer-thin edges for transmission electron microscopy characterization. Nanolites in the amorphous matrix are studied using energy-dispersive spectroscopy (EDS) and electron diffraction. Certain alkali and alkaline-earth cations exhibit patterns of depletion near Fe-oxide nanolites. EDS is used to identify nanolites and variations in the composition of the matrix. Parallel beam diffraction and radial distribution function analysis of nearest-neighbor distances determine average bond lengths in the matrix near nanolites, showing that nanolites influence the nearby short-range ordering and atomic character of the matrix. Analysis reveals decreased mean nearest-neighbor distances in the matrix adjacent to nanolites compared to the bulk. Our methods exhibit the required sensitivity to detect variations in the composition and structure near nanolites, and our findings indicate that obsidian nanolites contribute to quantifiable localized changes in the amorphous structure.

Understanding the Influence of Receptive Field and Network Complexity in Neural Network-Guided TEM Image Analysis.

Trained neural networks are promising tools to analyze the ever-increasing amount of scientific image data, but it is unclear how to best customize these networks for the unique features in transmission electron micrographs. Here, we systematically examine how neural network architecture choices affect how neural networks segment, or pixel-wise separate, crystalline nanoparticles from amorphous background in transmission electron microscopy (TEM) images. We focus on decoupling the influence of receptive field, or the area of the input image that contributes to the output decision, from network complexity, which dictates the number of trainable parameters. For low-resolution TEM images which rely on amplitude contrast to distinguish nanoparticles from background, we find that the receptive field does not significantly influence segmentation performance. On the other hand, for high-resolution TEM images which rely on both amplitude and phase-contrast changes to identify nanoparticles, receptive field is an important parameter for increased performance, especially in images with minimal amplitude contrast. Rather than depending on atom or nanoparticle size, the ideal receptive field seems to be inversely correlated to the degree of nanoparticle contrast in the image. Our results provide insight and guidance as to how to adapt neural networks for applications with TEM datasets.

Automated Crystal Orientation Mapping in py4DSTEM using Sparse Correlation Matching.

Crystalline materials used in technological applications are often complex assemblies composed of multiple phases and differently oriented grains. Robust identification of the phases and orientation relationships from these samples is crucial, but the information extracted from the diffraction condition probed by an electron beam is often incomplete. We have developed an automated crystal orientation mapping (ACOM) procedure which uses a converged electron probe to collect diffraction patterns from multiple locations across a complex sample. We provide an algorithm to determine the orientation of each diffraction pattern based on a fast sparse correlation method. We demonstrate the speed and accuracy of our method by indexing diffraction patterns generated using both kinematical and dynamical simulations. We have also measured orientation maps from an experimental dataset consisting of a complex polycrystalline twisted helical AuAgPd nanowire. From these maps we identify twin planes between adjacent grains, which may be responsible for the twisted helical structure. All of our methods are made freely available as open source code, including tutorials which can be easily adapted to perform ACOM measurements on diffraction pattern datasets.

Revealing the Phase Separation Behavior of Thermodynamically Immiscible Elements in a Nanoparticle.

Phase-separation is commonly observed in multimetallic nanomaterials, yet it is not well understood how immiscible elements distribute in a thermodynamically stable nanoparticle. Herein, we studied the phase-separation of Au and Rh in nanoparticles using electron microscopy and tomography techniques. The nanoparticles were thermally annealed to form thermodynamically stable structures. HAADF-STEM and EDS characterizations reveal that Au and Rh segregate into two domains while their miscibility is increased. Using aberration-corrected HAADF-STEM and atomic electron tomography, we show that the increased solubility of Au in Rh is achieved by forming Au clusters and single atoms inside the Rh domains and on the Rh surface. Furthermore, based on the three-dimensional reconstruction of a AuRh nanoparticle, we can visualize the uneven interface that is embedded in the nanoparticle. The results advance our understanding on the nanoscale thermodynamic behavior of metal mixtures, which is crucial for the optimization of multimetallic nanostructures for many applications.

Machine Learning Pipeline for Segmentation and Defect Identification from High-Resolution Transmission Electron Microscopy Data.

In the field of transmission electron microscopy, data interpretation often lags behind acquisition methods, as image processing methods often have to be manually tailored to individual datasets. Machine learning offers a promising approach for fast, accurate analysis of electron microscopy data. Here, we demonstrate a flexible two-step pipeline for the analysis of high-resolution transmission electron microscopy data, which uses a U-Net for segmentation followed by a random forest for the detection of stacking faults. Our trained U-Net is able to segment nanoparticle regions from the amorphous background with a Dice coefficient of 0.8 and significantly outperforms traditional image segmentation methods. Using these segmented regions, we are then able to classify whether nanoparticles contain a visible stacking fault with 86% accuracy. We provide this adaptable pipeline as an open-source tool for the community. The combined output of the segmentation network and classifier offer a way to determine statistical distributions of features of interest, such as size, shape, and defect presence, enabling the detection of correlations between these features.

A Fast Algorithm for Scanning Transmission Electron Microscopy Imaging and 4D-STEM Diffraction Simulations.

Scanning transmission electron microscopy (STEM) is an extremely versatile method for studying materials on the atomic scale. Many STEM experiments are supported or validated with electron scattering simulations. However, using the conventional multislice algorithm to perform these simulations can require extremely large calculation times, particularly for experiments with millions of probe positions as each probe position must be simulated independently. Recently, the plane-wave reciprocal-space interpolated scattering matrix (PRISM) algorithm was developed to reduce calculation times for large STEM simulations. Here, we introduce a new method for STEM simulation: partitioning of the STEM probe into "beamlets," given by a natural neighbor interpolation of the parent beams. This idea is compatible with PRISM simulations and can lead to even larger improvements in simulation time, as well requiring significantly less computer random access memory (RAM). We have performed various simulations to demonstrate the advantages and disadvantages of partitioned PRISM STEM simulations. We find that this new algorithm is particularly useful for 4D-STEM simulations of large fields of view. We also provide a reference implementation of the multislice, PRISM, and partitioned PRISM algorithms.

py4DSTEM: A Software Package for Four-Dimensional Scanning Transmission Electron Microscopy Data Analysis.

Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and spectroscopy of materials on length scales ranging from microns to atoms. By using a high-speed, direct electron detector, it is now possible to record a full two-dimensional (2D) image of the diffracted electron beam at each probe position, typically a 2D grid of probe positions. These 4D-STEM datasets are rich in information, including signatures of the local structure, orientation, deformation, electromagnetic fields, and other sample-dependent properties. However, extracting this information requires complex analysis pipelines that include data wrangling, calibration, analysis, and visualization, all while maintaining robustness against imaging distortions and artifacts. In this paper, we present py4DSTEM, an analysis toolkit for measuring material properties from 4D-STEM datasets, written in the Python language and released with an open-source license. We describe the algorithmic steps for dataset calibration and various 4D-STEM property measurements in detail and present results from several experimental datasets. We also implement a simple and universal file format appropriate for electron microscopy data in py4DSTEM, which uses the open-source HDF5 standard. We hope this tool will benefit the research community and help improve the standards for data and computational methods in electron microscopy, and we invite the community to contribute to this ongoing project.

Quantitative Mid-infrared Photoluminescence Characterization of Black Phosphorus-Arsenic Alloys.

Black phosphorus (bP) is a promising material for mid-infrared (mid-IR) optoelectronic applications, exhibiting high performance light emission and detection. Alloying bP with arsenic extends its operation toward longer wavelengths from 3.7 µm (bP) to 5 µm (bP3As7), which is of great practical interest. Quantitative optical characterizations are performed to establish black phosphorus-arsenic (bPAs) alloys optoelectronic quality. Anisotropic optical constants (refractive index, extinction coefficient, and absorption coefficient) of bPAs alloys from near-infrared to mid-IR (0.2-0.9 eV) are extracted with reflection measurements, which helps optical device design. Quantitative photoluminescence (PL) of bPAs alloys with different As concentrations are measured from room temperature to 77 K. PL quantum yield measurements reveal a 2 orders of magnitude decrease in radiative efficiency with increasing As concentration. An optical cavity is designed for bP3As7, which allows for up to an order of magnitude enhancement in the quantum yield due to the Purcell effect. Our comprehensive optical characterization provides the foundation for high performance mid-IR optical device design using bPAs alloys.

Multicolor Inks of Black Phosphorus for Midwave-Infrared Optoelectronics.

Black phosphorus (bP) based ink with a bulk bandgap of 0.33 eV (λ = 3.7 µm) has recently been shown to be promising for large-area, high performance mid-wave infrared (MWIR) optoelectronics. However, the development of multicolor bP inks expanding across the MWIR wavelength range has been challenging. Here a multicolor ink process based on bP with spectral emission tuned from 0.28 eV (λ = 4.4 µm) to 0.8 eV (λ = 1.5 µm) is demonstrated. Specifically, through the reduction of bP particle size distribution (i.e., lateral dimension and thickness), the optical bandgap systematically blueshifts, reaching up to 0.8 eV. Conversely, alloying bP with arsenic (bP1- xAsx) induces a redshift in the bandgap to 0.28 eV. The ink processed films are passivated with an infrared-transparent epoxy for stable infrared emission in ambient air. Utilizing these multicolor bP-based inks as an infrared light source, a gas sensing system is demonstrated that selectively detects gases, such as CO2 and CH4 whose absorption band varies around 4.3 and 3.3 µm, respectively. The presented ink formulation sets the stage for the advancement of multiplex MWIR optoelectronics, including spectrometers and spectral imaging using a low-cost material processing platform.