Characteristic Plasmon Energies for 2D In2Se3 Phase Identification at Nanoscale.

Two-dimensional (2D) materials with competing polymorphs offer remarkable potential to switch the associated 2D functionalities for novel device applications. Probing their phase transition and competition mechanisms requires nanoscale characterization techniques that can sensitively detect the nucleation of secondary phases down to single-layer thickness. Here we demonstrate nanoscale phase identification on 2D In2Se3 polymorphs, utilizing their distinct plasmon energies that can be distinguished by electron energy-loss spectroscopy (EELS). The characteristic plasmon energies of In2Se3 polymorphs have been validated by first-principles calculations, and also been successfully applied to reveal phase transitions using in situ EELS. Correlating with in situ X-ray diffraction, we further derive a subtle difference in the valence electron density of In2Se3 polymorphs, consistent with their disparate electronic properties. The nanometer resolution and independence of orientation make plasmon-energy mapping a versatile technique for nanoscale phase identification on 2D materials.

Quantum Limits of Transmission Electron Microscopy.

New-generation transmission electron microscopes (TEMs) are equipped with detectors that approach the shot-noise limit. Hence it is pertinent to ask: What are the quantum limits of electron scattering experiments in the TEM? For example, for a given electron dose, what is the ultimate accuracy allowed by quantum mechanics for the atomic structure of a material? We provide quantitative answers based on quantum estimation theory. We also show that, for an arbitrary set of sample parameters, the quantum limit is achievable under conditions of weak scattering, but not strong multiple scattering (this conclusion extends to scattering of other types of radiation). Implications for structure determination of radiation-sensitive materials are discussed.

Direct mapping of Li-enabled octahedral tilt ordering and associated strain in nanostructured perovskites.

Self-assembled nanostructures with periodic phase separation hold great promise for creating two- and three-dimensional superlattices with extraordinary physical properties. Understanding the mechanism(s) driving the formation of such superlattices demands an understanding of their underlying atomic structure. However, the nanoscale structural fluctuations intrinsic to these superlattices pose a new challenge for structure determination methods. Here we develop an optimized atomic-level imaging condition to measure TiO6 octahedral tilt angles, unit-cell-by-unit-cell, in perovskite-based Li(0.5-3x)Nd(0.5+x)TiO3, and thereby determine the mathematical formula governing this nanoscale superstructure. We obtain a direct real-space correlation of the octahedral tilt modulation with the superstructure geometry and lattice-parameter variations. This reveals a composition-dependent, self-ordered octahedral superlattice. Amazingly, we observe a reversible annihilation/reconstruction of the octahedral superlattice correlated with the delithiation/lithiation process in this promising Li-ion conductor. This approach to quantify local octahedral tilt and correlate it with strain can be applied to characterize complex octahedral behaviours in other advanced oxide systems.

Stability of crystal facets in gold nanorods.

Metal nanocrystals can be grown in a variety of shapes through the modification of surface facet energies via surfactants. However, the surface facets are only a few atoms wide, making it extremely challenging to measure their geometries and energies. Here, we locate and count atoms in Au nanorods at successive time intervals using quantitative scanning transmission electron microscopy. This enables us to determine the atomic-level geometry and the relative stability of the facets and to expound their relationship to the overall three-dimensional nanocrystal shape and size. We reveal coexisting high- and low-index facets with comparable stability and dimensions and find the geometry of the nanorods is remarkably stable, despite significant atom movements. This information provides unique insights into the mechanisms that govern growth kinetics and nanocrystal morphology.

Quantitative position-averaged K-, L-, and M-shell core-loss scattering in STEM.

We present a quantitative comparison between experimental position-averaged core-loss scattering from K-, L-, and M-shells of various elements and simulations based on a single-particle description of the core-loss process. To facilitate a direct comparison free of adjustable or compensating parameters, we compare absolute scattering cross-sections for zone-axis-aligned crystals whose thicknesses have been measured independently. The results show that the single-particle model accurately predicts the absolute scattering intensity from K-shells, and L-shells in some cases, but achieves only semi-quantitative agreement for M-shells.

Efficient atomic-scale kinetics through a complex heterophase interface.

Atomic-scale imaging and first-principles modeling are applied to the heterophase interface between the Al-Cu solid solution (αCu) and θ' (Al2Cu) phases. Contrary to recent studies, our observations reveal a diffuse interface of complex but well-defined structure that enables the progression from αCu to θ' over a distance of ≈1 nm. We demonstrate that, surprisingly, the observed interfacial structure is not preferred on energetic grounds. Rather, the excess in interfacial energy is compensated by efficient atomic-scale kinetics of the αCu→θ' phase transformation.

Scanning confocal electron energy-loss microscopy using valence-loss signals.

Finding a faster alternative to tilt-series electron tomography is critical for rapidly evolving fields such as the semiconductor industry, where failure analysis could greatly benefit from higher throughput. We present a theoretical and experimental evaluation of scanning confocal electron energy-loss microscopy (SCEELM) using valence-loss signals, which is a promising technique for the reliable reconstruction of materials with sub-10-nm resolution. Such a confocal geometry transfers information from the focused portion of the electron beam and enables rapid three-dimensional (3D) reconstruction by depth sectioning. SCEELM can minimize or eliminate the missing-information cone and the elongation problem that are associated with other depth-sectioning image techniques in a transmission electron microscope. Valence-loss SCEELM data acquisition is an order of magnitude faster and requires little postprocessing compared with tilt-series electron tomography. With postspecimen chromatic aberration (C c) correction, SCEELM signals can be acquired in parallel in the direction of energy dispersion with the aid of a physical pinhole. This increases the efficiency by 10×-100×, and can provide 3D resolved chemical information for multiple core-loss signals simultaneously.

Correlative Fluorescence and Transmission Electron Microscopy Assisted by 3D Machine Learning Reveals Thin Nanodiamonds Fluoresce Brighter.

Nitrogen vacancy (NV) centers in fluorescent nanodiamonds (FNDs) draw widespread attention as quantum sensors due to their room-temperature luminescence, exceptional photo- and chemical stability, and biocompatibility. For bioscience applications, NV centers in FNDs offer high-spatial-resolution capabilities that are unparalleled by other solid-state nanoparticle emitters. On the other hand, pursuits to further improve the optical properties of FNDs have reached a bottleneck, with intense debate in the literature over which of the many factors are most pertinent. Here, we describe how substantial progress can be achieved using a correlative transmission electron microscopy and photoluminescence (TEMPL) method that we have developed. TEMPL enables a precise correlative analysis of the fluorescence brightness, size, and shape of individual FND particles. Augmented with machine learning, TEMPL can be used to analyze a large, statistically meaningful number of particles. Our results reveal that FND fluorescence is strongly dependent on particle shape, specifically, that thin, flake-shaped particles are up to several times brighter and that fluorescence increases with decreasing particle sphericity. Our theoretical analysis shows that these observations are attributable to the constructive interference of light waves within the FNDs. Our findings have significant implications for state-of-the-art sensing applications, and they offer potential avenues for improving the sensitivity and resolution of quantum sensing devices.

Atom-scale ptychographic electron diffractive imaging of boron nitride cones.

Ptychographic coherent diffractive imaging (CDI) has been extensively applied using both x rays and electrons. The extension to atomic resolution has been elusive. This Letter demonstrates ptychographic electron diffractive imaging at atomic resolution, permitting identification of structure in a boron nitride helical cone at a resolution of order 1 Å, beyond that of comparative Z-contrast images. A scanning transmission electron microscope is used to create a diverging illumination in a defocused Fresnel CDI geometry, providing a robust strategy leading to a unique solution.

Imaging high-energy electrons propagating in a crystal.

It has recently become possible to focus a beam of high-energy electrons to a spot smaller than an atom, with vast potential for the study of condensed matter. We devise an experiment that can image, with subangstrom resolution, the distribution of such an electron beam as it scatters within an atomic lattice. Our experiments reveal the acute sensitivity of the scattered electron distribution to 0.1 Å shifts of the impact point. Scattering due to plasmon excitations is also examined. Implications for the study of the atomic and electronic structure of condensed matter are discussed.

Statistically Representative Metrology of Nanoparticles via Unsupervised Machine Learning of TEM Images.

The morphology of nanoparticles governs their properties for a range of important applications. Thus, the ability to statistically correlate this key particle performance parameter is paramount in achieving accurate control of nanoparticle properties. Among several effective techniques for morphological characterization of nanoparticles, transmission electron microscopy (TEM) can provide a direct, accurate characterization of the details of nanoparticle structures and morphology at atomic resolution. However, manually analyzing a large number of TEM images is laborious. In this work, we demonstrate an efficient, robust and highly automated unsupervised machine learning method for the metrology of nanoparticle systems based on TEM images. Our method not only can achieve statistically significant analysis, but it is also robust against variable image quality, imaging modalities, and particle dispersions. The ability to efficiently gain statistically significant particle metrology is critical in advancing precise particle synthesis and accurate property control.

Metrology of convex-shaped nanoparticles via soft classification machine learning of TEM images.

The shape of nanoparticles is a key performance parameter for many applications, ranging from nanophotonics to nanomedicines. However, the unavoidable shape variations, which occur even in precision-controlled laboratory synthesis, can significantly impact on the interpretation and reproducibility of nanoparticle performance. Here we have developed an unsupervised, soft classification machine learning method to perform metrology of convex-shaped nanoparticles from transmission electron microscopy images. Unlike the existing methods, which are based on hard classification, soft classification provides significantly greater flexibility in being able to classify both distinct shapes, as well as non-distinct shapes where hard classification fails to provide meaningful results. We demonstrate the robustness of our method on a range of nanoparticle systems, from laboratory-scale to mass-produced synthesis. Our results establish that the method can provide quantitative, accurate, and meaningful metrology of nanoparticle ensembles, even for ensembles entailing a continuum of (possibly irregular) shapes. Such information is critical for achieving particle synthesis control, and, more importantly, for gaining deeper understanding of shape-dependent nanoscale phenomena. Lastly, we also present a method, which we coin the "binary DoG", which achieves significant progress on the challenging problem of identifying the shapes of aggregated nanoparticles.

Imaging Beam-Sensitive Materials by Electron Microscopy.

Electron microscopy allows the extraction of multidimensional spatiotemporally correlated structural information of diverse materials down to atomic resolution, which is essential for figuring out their structure-property relationships. Unfortunately, the high-energy electrons that carry this important information can cause damage by modulating the structures of the materials. This has become a significant problem concerning the recent boost in materials science applications of a wide range of beam-sensitive materials, including metal-organic frameworks, covalent-organic frameworks, organic-inorganic hybrid materials, 2D materials, and zeolites. To this end, developing electron microscopy techniques that minimize the electron beam damage for the extraction of intrinsic structural information turns out to be a compelling but challenging need. This article provides a comprehensive review on the revolutionary strategies toward the electron microscopic imaging of beam-sensitive materials and associated materials science discoveries, based on the principles of electron-matter interaction and mechanisms of electron beam damage. Finally, perspectives and future trends in this field are put forward.

Dynamic self-assembly of detonation nanodiamond in water.

Nanodiamonds are increasingly used in many areas of science and technology, yet, their colloidal properties remain poorly understood. Here we use direct imaging as well as light and X-ray scattering reveal that purified detonation nanodiamond (DND) particles in an aqueous environment exhibit a self-assembled lace-like network, even without additional surface modification. Such behaviour is previously unknown and contradicts the current consensus that DND exists as mono-dispersed single particles. With the aid of mesoscale simulations, we show that the lace network is likely the result of competition between a short-ranged electrostatic attraction between faceted particles and a longer-ranged repulsion arising from the interaction between the surface functional groups and the surrounding water molecules which prevents complete flocculation. Our findings have significant implications for applications of DND where control of the aggregation behaviour is critical to performance.

Inpainting vs denoising for dose reduction in scanning-beam microscopies.

We consider sampling strategies for reducing the radiation dose during image acquisition in scanning-beam microscopies, such as SEM, STEM, and STXM. Our basic assumption is that we may acquire subsampled image data (with some pixels missing) and then inpaint the missing data using a compressed-sensing approach. Our noise model consists of Poisson noise plus random Gaussian noise. We include the possibility of acquiring fully-sampled image data, in which case the inpainting approach reduces to a denoising procedure. We use numerical simulations to compare the accuracy of reconstructed images with the "ground truths." The results generally indicate that, for sufficiently high radiation doses, higher sampling rates achieve greater accuracy, commensurate with well-established literature. However, for very low radiation doses, where the Poisson noise and/or random Gaussian noise begins to dominate, then our results indicate that subsampling/inpainting can result in smaller reconstruction errors. We also present an information-theoretic analysis, which allows us to quantify the amount of information gained through the different sampling strategies and enables some broader discussion of the main results.

Size dependent surface reconstruction in detonation nanodiamonds.

Nanometer-sized diamond particles are used in bio-medical applications, where the nature of the nanodiamond surfaces is crucial to achieving correct functionalisation. Herein, using high-resolution transmission electron microscopy and electronic structure calculations, we study the surface reconstructions that occur in detonation-synthesized nanodiamonds. Our results show that particles smaller than 3 nm exhibit size- and shape-dependent surface reconstructions, and that the surfaces can exhibit a higher-than-expected fraction of sp2+x bonding. This indicates an aliphatic character for sub-3 nm nanodiamond particles. Such behaviour impacts the functionality of nanodiamonds, where both size and surface charge can drive performance. Our observations offer a potential strategy for better functionalization control via the size range of the particles.

Subsampling and inpainting approaches for electron tomography.

With the aim of addressing the issue of sample damage during electron tomography data acquisition, we propose a number of new reconstruction strategies based on subsampling (which uses only a subset of a full image) and inpainting (recovery of a full image from subsampled one). We point out that the total-variation (TV) inpainting model commonly used to inpaint subsampled images may be inappropriate for 2D projection images of typical TEM specimens. Thus, we propose higher-order TV (HOTV) inpainting, which accommodates the fact that projection images may be inherently smooth, as a more suitable image inpainting scheme. We also describe how the HOTV method can be extended to 3D, a scheme which makes use of both image data and sinogram data. Additionally, we propose gradient subsampling as a more efficient scheme than random subsampling. We make a rigorous comparison of our proposed new reconstruction schemes with existing ones. The new schemes are demonstrated to perform better than or as well as existing schemes, and we show that they outperform existing schemes at low subsampling rates.

Prospects for quantitative and time-resolved double and continuous exposure off-axis electron holography.

The technique of double exposure electron holography, which is based on the superposition of two off-axis electron holograms, was originally introduced before the availability of digital image processing to allow differences between electron-optical phases encoded in two electron holograms to be visualised directly without the need for holographic reconstruction. Here, we review the original method and show how it can now be extended to permit quantitative studies of phase shifts that oscillate in time. We begin with a description of the theory of off-axis electron hologram formation for a time-dependent electron wave that results from the excitation of a specimen using an external stimulus with a square, sinusoidal, triangular or other temporal dependence. We refer to the more general method as continuous exposure electron holography, present preliminary experimental measurements and discuss how the technique can be used to image electrostatic potentials and magnetic fields during high frequency switching experiments.