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Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.

Corrosion is a ubiquitous failure mode of materials. Often, the progression of localized corrosion is accompanied by the evolution of porosity in materials previously reported to be either three-dimensional or two-dimensional. However, using new tools and analysis techniques, we have realized that a more localized form of corrosion, which we call 1D wormhole corrosion, has previously been miscategorized in some situations. Using electron tomography, we show multiple examples of this 1D and percolating morphology. To understand the origin of this mechanism in a Ni-Cr alloy corroded by molten salt, we combined energy-filtered four-dimensional scanning transmission electron microscopy and ab initio density functional theory calculations to develop a vacancy mapping method with nanometer-resolution, identifying a remarkably high vacancy concentration in the diffusion-induced grain boundary migration zone, up to 100 times the equilibrium value at the melting point. Deciphering the origins of 1D corrosion is an important step towards designing structural materials with enhanced corrosion resistance.

High-throughput scanning electron microscopy (SEM) coupled with classification using neural networks is an ideal method to determine the morphological handedness of large populations of chiral nanoparticles. Automated labeling removes the time-consuming manual labeling of training data, but introduces label error, and subsequently classification error in the trained neural network. Here, we evaluate methods to minimize classification error when training from automated labels of SEM datasets of chiral Tellurium nanoparticles. Using the mirror relationship between images of opposite handed particles, we artificially create populations of varying label error. We analyze the impact of label error rate and training method on the classification error of neural networks on an ideal dataset and on a practical dataset. Of the three training methods considered, we find that a pretraining approach yields the most accurate results across label error rates on ideal datasets, where size and other morphological variables are held constant, but that a co-teaching approach performs the best in practical application.

Scanning transmission electron microscopy (STEM), where a converged electron probe is scanned over a sample's surface and an imaging, diffraction, or spectroscopic signal is measured as a function of probe position, is an extremely powerful tool for materials characterization. The widespread adoption of hardware aberration correction, direct electron detectors, and computational imaging methods have made STEM one of the most important tools for atomic-resolution materials science. Many of these imaging methods rely on accurate imaging and diffraction simulations in order to interpret experimental results. However, STEM simulations have traditionally required large calculation times, as modeling the electron scattering requires a separate simulation for each of the typically millions of probe positions. We have created the Prismatic simulation code for fast simulation of STEM experiments with support for multi-CPU and multi-GPU (graphics processing unit) systems, using both the conventional multislice and our recently-introduced PRISM method. In this paper, we introduce Prismatic version 2.0, which adds many new algorithmic improvements, an updated graphical user interface (GUI), post-processing of simulation data, and additional operating modes such as plane-wave TEM. We review various aspects of the simulation methods and codes in detail and provide various simulation examples. Prismatic 2.0 is freely available both as an open-source package that can be run using a C++ or Python command line interface, or GUI, as well within a Docker container environment.

Three-dimensional phase-contrast imaging of multiply-scattering samples in x-ray and electron microscopy is challenging due to small numerical apertures, the unavailability of wave front shaping optics, and the highly nonlinear inversion required from intensity-only measurements. In this work, we present an algorithm using the scattering matrix formalism to solve the scattering from a noncrystalline medium from scanning diffraction measurements and simultaneously recover the illumination aberrations. We demonstrate our method experimentally in a scanning transmission electron microscope, recovering the scattering matrix of a heterogeneous sample with two layers of multiwall carbon nanotubes filled with TaTe2 core-shell structures, spaced 10nm apart in the axial direction. Our work enables phase contrast imaging and materials characterization in multiply-scattering samples at high resolution for a wide range of materials.

Organic-inorganic hybrids have recently emerged as a class of high-performing thermoelectric materials that are lightweight and mechanically flexible. However, the fundamental electrical and thermal transport in these materials has remained elusive due to the heterogeneity of bulk, polycrystalline, thin films reported thus far. Here, we systematically investigate a model hybrid comprising a single core/shell nanowire of Te-PEDOT:PSS. We show that as the nanowire diameter is reduced, the electrical conductivity increases and the thermal conductivity decreases, while the Seebeck coefficient remains nearly constant-this collectively results in a figure of merit, ZT, of 0.54 at 400 K. The origin of the decoupling of charge and heat transport lies in the fact that electrical transport occurs through the organic shell, while thermal transport is driven by the inorganic core. This study establishes design principles for high-performing thermoelectrics that leverage the unique interactions occurring at the interfaces of hybrid nanowires.

Organic–inorganic hybrids offer great promise as solution-processable thermoelectric materials. However, they have struggled to surpass the performance of their rigid inorganic counterparts due, in part, to a lack of synthetic control and limited understanding of how inorganic nanostructure dimensions impact overall charge transport. While it has been hypothesized that length, diameter, and aspect ratio (AR) all impact electronic transport in hybrid nanowires, the field lacks clarity on the relative role of each. In this study, the experimental parameter of ligand molecular weight (MW) is investigated as a synthetic knob for modulating nanowire dimensions, as well as the deconvolution of nanowire length versus diameter impacts on electron transport. By increasing ligand MW, larger nanowire AR dispersions occur and an optimal power factor of ≈130 μWm−1 K−2 is achieved for a modest AR of 73. Power factors of this magnitude are thought to only be achievable in ultrahigh AR systems; representing a 183% increase in performance over literature reports with similar AR. Additionally, nanowire diameter is demonstrated to be a far more sensitive parameter for enhancing performance than modulating length. This study provides improved fundamental insight into rational synthetic design avenues for future enhancements in the performance of hybrid materials.

The unitized regenerative fuel cell (URFC) is a promising electrochemical device for intermittent renewable energy storage in chemical bonds. However, widespread application has been hindered due to low round-trip efficiencies (RTEs) and disappointing durability, in particular at high rates. Here, we break through that barrier by demonstrating highly efficient, flexible, and stable URFCs via hierarchical design of the multiscale catalyst-layer structures. A more porous and less tortuous Pt and Ir catalyst layer is realized using a doctor blade fabrication method that significantly improves URFC performance. We demonstrate RTEs of 56% and 53% under constant-electrode and constant-gas mode, respectively, while operating at 1000 mA cm-2, and significantly, a RTE of 45% at 2000 mA cm-2, achievements that were previously viewed as unfeasible under the onerous demands of URFC operation. At the same time we demonstrate URFCs under both constant-electrode and constant-gas mode operated continuously for over 500 h with negligible degradation. These results demonstrate the viability of applying URFCs for long-term energy storage at previously unattainable efficiencies and cast new light on electrode design and optimization of URFCs. This journal is

Solid-state batteries (SSBs) are considered promising next-generation energy storage devices but tend to suffer from rapid capacity fade. Here, we demonstrate that mechanical contact loss between the solid conductor and cathode, induced by its volume changes during cycling, plays a significant role in the observed capacity fade. Focused ion beam-scanning electron microscope (FIB-SEM) tomography with nanoscale resolution was used for 3D characterization of the composite electrode morphology before and after cycling. The tomography data demonstrates the development of voids and cracks near the cathode particles and significant contact loss between the cathode particles and solid electrolyte after cycling. The observed mechanical degradation in the electrode composite highlights the difficulty and importance of engineering mechanically stable SSBs. The application of large external pressure after long-term cycling led to recovery of lost capacity and reduced the cell resistance, confirming the effect of mechanical degradation. This journal is

Fluctuation electron microscopy (FEM) is a scanning nanodiffraction-based method that offers a unique approach to characterizing nanometer-scale medium-range order (MRO) in disordered materials. In addition to determining the degree of MRO, careful analysis of scanning nanodiffraction data can also be used to determine strain in thin film amorphous samples. We applied FEM to characterize the strain and MRO of magnetron sputtered amorphous tantalum (a-Ta) thin films over a range of tilt angles from 0 ° to 45 ° in order to measure any deviations between the in-plane and out-of-plane strain and MRO. We validate our approach using electron diffraction simulations of FEM experiments for a-Ta. We measure anisotropic strain in the simulated a-Ta diffraction patterns and find that the experimental a-Ta is isotropically strained within the accuracy of our method. Our approach provides a workflow for acquiring tilted scanning nanodiffraction data, determining the relative strain and ordering as a function of in- A nd out-of-plane directions, and removing any artifacts induced in FEM data due to strain. We also describe some limitations of the tilted FEM method when applied to thin films with very low strains.

The interfacial instability between a thiophosphate solid electrolyte and oxide cathodes results in rapid capacity fade and has driven the need for cathode coatings. In this work, the stability, evolution, and performance of uncoated, Li2ZrO3-coated, and Li3B11O18-coated LiNi0.5Co0.2Mn0.3O2 cathodes are compared using first-principles computations and electron microscopy characterization. Li3B11O18 is identified as a superior coating that exhibits excellent oxidation/chemical stability, leading to substantially improved performance over cells with Li2ZrO3-coated or uncoated cathodes. The chemical and structural origin of the different performance is interpreted using different microscopy techniques which enable the direct observation of the phase decomposition of the Li2ZrO3 coating. It is observed that Li is already extracted from the Li2ZrO3 in the first charge, leading to the formation of ZrO2 nanocrystallites with loss of protection of the cathode. After 50 cycles separated (Co, Ni)-sulfides and Mn-sulfides can be observed within the Li2ZrO3-coated material. This work illustrates the severity of the interfacial reactions between a thiophosphate electrolyte and oxide cathode and shows the importance of using coating materials that are absolutely stable at high voltage.

Two-dimensional (2D) noble-metal dichalcogenides exhibit exceptionally strong thickness-dependent bandgaps, which can be leveraged in a wide variety of device applications. A detailed study of their optical (e.g., optical bandgaps) and electrical properties (e.g., mobilities) is important in determining potential future applications of these materials. In this work, we perform detailed optical and electrical characterization of 2D PdSe2 nanoflakes mechanically exfoliated from a single-crystalline source. Layer-dependent bandgap analysis from optical absorption results indicates that this material is an indirect semiconductor with bandgaps of approximately 1.37 and 0.50 eV for the monolayer and bulk, respectively. Spectral photoresponse measurements further confirm these bandgap values. Moreover, temperature-dependent electrical measurements of a 6.8-nm-thick PdSe2 flake-based transistor show effective electron mobilities of 130 and 520 cm2 V-1 s-1 at 300 K and 77 K, respectively. Finally, we demonstrate that PdSe2 can be utilized for short-wave infrared photodetectors. A room-temperature specific detectivity (D) of 1.8 × 1010 cm Hz1/2 W-1 at 1 μm with a band edge at 1.94 μm is achieved on a 6.8-nm-thick PdSe2 flake-based photodetector.

Few-layer black phosphorus (BP) nanosheets were first reported as a 2D material for the application of field-effect transistors in 2014 and have stimulated intense activity among physicists, chemists, and material and biomedical scientists, driving research into novel synthetic techniques to produce BP nanosheets. At present, exfoliation is the main route toward few-layer BP nanosheets via employing bulk BP as raw material. However, this is a complicated and time-consuming process, which is difficult for the large-scale synthesis of BP nanosheets. Moreover, BP degrades rapidly when exfoliated to nanoscale dimensions, resulting in the rapid loss of semiconducting properties. Here, we report the direct wet-chemical synthesis of few-layer BP nanosheets in gram-scale quantities in a bottom-up approach based on common laboratory reagents at low temperature, showing excellent stability due to partial oxidation of surface. Solvent and temperature are two critical factors, controlling not only the formation of BP nanosheets but also the thickness. The as-prepared BP nanosheets can extract hydrogen from pure water (pH = 6.8), exhibiting more than 24-fold higher activity than the well-known C3N4 nanosheets. Our results reporting the ability to prepare few-layer BP nanosheets with a facile, scalable, low-cost approach take us a step closer to real-world applications of phosphorene including next-generation metal-free photocatalysts for photosynthesis.

Solar-driven water splitting using powdered catalysts is considered as the most economical means for hydrogen generation. However, four-electron-driven oxidation half-reaction showing slow kinetics, accompanying with insufficient light absorption and rapid carrier combination in photocatalysts leads to low solar-to-hydrogen energy conversion efficiency. Here, we report amorphous cobalt phosphide (Co-P)-supported black phosphorus nanosheets employed as photocatalysts can simultaneously address these issues. The nanosheets exhibit robust hydrogen evolution from pure water (pH = 6.8) without bias and hole scavengers, achieving an apparent quantum efficiency of 42.55% at 430 nm and energy conversion efficiency of over 5.4% at 353 K. This photocatalytic activity is attributed to extremely efficient utilization of solar energy (~75% of solar energy) by black phosphorus nanosheets and high-carrier separation efficiency by amorphous Co-P. The hybrid material design realizes efficient solar-to-chemical energy conversion in suspension, demonstrating the potential of black phosphorus-based materials as catalysts for solar hydrogen production.

Solar-driven water splitting using powdered catalysts is considered as the most economical means for hydrogen generation. However, four-electron-driven oxidation half-reaction showing slow kinetics, accompanying with insufficient light absorption and rapid carrier combination in photocatalysts leads to low solar-to-hydrogen energy conversion efficiency. Here, we report amorphous cobalt phosphide (Co-P)-supported black phosphorus nanosheets employed as photocatalysts can simultaneously address these issues. The nanosheets exhibit robust hydrogen evolution from pure water (pH = 6.8) without bias and hole scavengers, achieving an apparent quantum efficiency of 42.55% at 430 nm and energy conversion efficiency of over 5.4% at 353 K. This photocatalytic activity is attributed to extremely efficient utilization of solar energy (~75% of solar energy) by black phosphorus nanosheets and high-carrier separation efficiency by amorphous Co-P. The hybrid material design realizes efficient solar-to-chemical energy conversion in suspension, demonstrating the potential of black phosphorus-based materials as catalysts for solar hydrogen production.

Solar to chemical energy conversion could provide an alternative to mankind's unsustainable use of fossil fuels. One promising approach is the electrochemical reduction of CO2 into chemical products, in particular hydrocarbons and oxygenates which are formed by multi-electron transfer reactions. Here, a nanostructured Cu-Ag bimetallic cathode is utilized to selectively and efficiently facilitate these reactions. When operated in an electrolysis cell, the cathode provides a constant energetic efficiency for hydrocarbon and oxygenate production. As a result, when coupled to Si photovoltaic cells, solar conversion efficiencies of 3-4% to the target products are achieved for 0.35 to 1 Sun illumination. Use of a four-terminal III-V/Si tandem solar cell configuration yields a conversion efficiency to hydrocarbons and oxygenates exceeding 5% at 1 Sun illumination. This study provides a clear framework for the future advancement of efficient solar-driven CO2 reduction devices.

Perfect crystals are rare in nature. Real materials often contain crystal defects and chemical order/disorder such as grain boundaries, dislocations, interfaces, surface reconstructions and point defects. Such disruption in periodicity strongly affects material properties and functionality. Despite rapid development of quantitative material characterization methods, correlating three-dimensional (3D) atomic arrangements of chemical order/disorder and crystal defects with material properties remains a challenge. On a parallel front, quantum mechanics calculations such as density functional theory (DFT) have progressed from the modelling of ideal bulk systems to modelling 'real' materials with dopants, dislocations, grain boundaries and interfaces; but these calculations rely heavily on average atomic models extracted from crystallography. To improve the predictive power of first-principles calculations, there is a pressing need to use atomic coordinates of real systems beyond average crystallographic measurements. Here we determine the 3D coordinates of 6,569 iron and 16,627 platinum atoms in an iron-platinum nanoparticle, and correlate chemical order/disorder and crystal defects with material properties at the single-atom level. We identify rich structural variety with unprecedented 3D detail including atomic composition, grain boundaries, anti-phase boundaries, anti-site point defects and swap defects. We show that the experimentally measured coordinates and chemical species with 22 picometre precision can be used as direct input for DFT calculations of material properties such as atomic spin and orbital magnetic moments and local magnetocrystalline anisotropy. This work combines 3D atomic structure determination of crystal defects with DFT calculations, which is expected to advance our understanding of structure-property relationships at the fundamental level.

Solar to chemical energy conversion could provide an alternative to mankind's unsustainable use of fossil fuels. One promising approach is the electrochemical reduction of CO2 into chemical products, in particular hydrocarbons and oxygenates which are formed by multi-electron transfer reactions. Here, a nanostructured Cu-Ag bimetallic cathode is utilized to selectively and efficiently facilitate these reactions. When operated in an electrolysis cell, the cathode provides a constant energetic efficiency for hydrocarbon and oxygenate production. As a result, when coupled to Si photovoltaic cells, solar conversion efficiencies of 3-4% to the target products are achieved for 0.35 to 1 Sun illumination. Use of a four-terminal III-V/Si tandem solar cell configuration yields a conversion efficiency to hydrocarbons and oxygenates exceeding 5% at 1 Sun illumination. This study provides a clear framework for the future advancement of efficient solar-driven CO2 reduction devices.

Electron tomography in materials science has flourished with the demand to characterize nanoscale materials in three dimensions (3D). Access to experimental data is vital for developing and validating reconstruction methods that improve resolution and reduce radiation dose requirements. This work presents five high-quality scanning transmission electron microscope (STEM) tomography datasets in order to address the critical need for open access data in this field. The datasets represent the current limits of experimental technique, are of high quality, and contain materials with structural complexity. Included are tomographic series of a hyperbranched Co2P nanocrystal, platinum nanoparticles on a carbon nanofibre imaged over the complete 180° tilt range, a platinum nanoparticle and a tungsten needle both imaged at atomic resolution by equal slope tomography, and a through-focal tilt series of PtCu nanoparticles. A volumetric reconstruction from every dataset is provided for comparison and development of post-processing and visualization techniques. Researchers interested in creating novel data processing and reconstruction algorithms will now have access to state of the art experimental test data.

Crystallography, the primary method for determining the 3D atomic positions in crystals, has been fundamental to the development of many fields of science. However, the atomic positions obtained from crystallography represent a global average of many unit cells in a crystal. Here, we report, for the first time, the determination of the 3D coordinates of thousands of individual atoms and a point defect in a material by electron tomography with a precision of ∼19 pm, where the crystallinity of the material is not assumed. From the coordinates of these individual atoms, we measure the atomic displacement field and the full strain tensor with a 3D resolution of ∼1 nm(3) and a precision of ∼10(-3), which are further verified by density functional theory calculations and molecular dynamics simulations. The ability to precisely localize the 3D coordinates of individual atoms in materials without assuming crystallinity is expected to find important applications in materials science, nanoscience, physics, chemistry and biology.

Transmission electron microscopy is a powerful imaging tool that has found broad application in materials science, nanoscience and biology. With the introduction of aberration-corrected electron lenses, both the spatial resolution and the image quality in transmission electron microscopy have been significantly improved and resolution below 0.5 ångströms has been demonstrated. To reveal the three-dimensional (3D) structure of thin samples, electron tomography is the method of choice, with cubic-nanometre resolution currently achievable. Discrete tomography has recently been used to generate a 3D atomic reconstruction of a silver nanoparticle two to three nanometres in diameter, but this statistical method assumes prior knowledge of the particle's lattice structure and requires that the atoms fit rigidly on that lattice. Here we report the experimental demonstration of a general electron tomography method that achieves atomic-scale resolution without initial assumptions about the sample structure. By combining a novel projection alignment and tomographic reconstruction method with scanning transmission electron microscopy, we have determined the 3D structure of an approximately ten-nanometre gold nanoparticle at 2.4-ångström resolution. Although we cannot definitively locate all of the atoms inside the nanoparticle, individual atoms are observed in some regions of the particle and several grains are identified in three dimensions. The 3D surface morphology and internal lattice structure revealed are consistent with a distorted icosahedral multiply twinned particle. We anticipate that this general method can be applied not only to determine the 3D structure of nanomaterials at atomic-scale resolution, but also to improve the spatial resolution and image quality in other tomography fields.