Three-dimensional morphology of iron oxide nanoparticles with reactive concave surfaces. A compressed sensing-electron tomography (CS-ET) approach

Atomic-level imaging of beam-sensitive COFs and MOFs by low-dose electron microscopy

Electron microscopy, an important technique that allows for the precise determination of structural information with high spatiotemporal resolution, has become indispensable in unravelling the complex relationships between material structure and properties ranging from mesoscale morphology to atomic arrangement. However, beam-sensitive materials, particularly those comprising organic components such as metal-organic frameworks (MOFs) and covalent organic frameworks (COFs), would suffer catastrophic damage from the high energy electrons, hindering the determination of atomic structures. A low-dose approach has arisen as a possible solution to this problem based on the integration of advancements in several aspects: electron optical system, detector, image processing, and specimen preservation. This article summarizes the transmission electron microscopy characterization of MOFs and COFs, including local structures, host-guest interactions, and interfaces at the atomic level. Revolutions in advanced direct electron detectors, algorithms in image acquisition and processing, and emerging methodology for high quality low-dose imaging are also reviewed. Finally, perspectives on the future development of electron microscopy methodology with the support of computer science are presented.

Charting the molecular landscape of the cell

Biological function of macromolecules is closely tied to their cellular location, as well as to interactions with other molecules within the native environment of the cell. Therefore, to obtain detailed mechanistic insights into macromolecular functionality, one of the outstanding targets for structural biology is to produce an atomic-level understanding of the cell. One structural biology technique that has already been used to directly derive atomic models of macromolecules from cells, without any additional external information, is electron cryotomography (cryoET). In this perspective article, we discuss possible routes to chart the molecular landscape of the cell by advancing cryoET imaging as well as by embedding cryoET into correlative imaging workflows.

WRAP: A wavelet-regularised reconstruction algorithm for magnetic vector electron tomography

Magnetic vector electron tomography (VET) is a promising technique that enables better understanding of micro- and nano-magnetic phenomena through the reconstruction of 3D magnetic fields at high spatial resolution. Here we introduce WRAP (Wavelet Regularised A Program), a reconstruction algorithm for magnetic VET that directly reconstructs the magnetic vector potential A using a compressed sensing framework which regularises for sparsity in the wavelet domain. We demonstrate that using WRAP leads to a significant increase in the fidelity of the 3D reconstruction and is especially robust when dealing with very limited data; using datasets simulated with realistic noise, we compare WRAP to a conventional reconstruction algorithm and find an improvement of ca. 60% when averaged over several performance metrics. Moreover, we further validate WRAP's performance on experimental electron holography data, revealing the detailed magnetism of vortex states in a CuCo nanowire. We believe WRAP represents a major step forward in the development of magnetic VET as a tool for probing magnetism at the nanoscale.

Cones and spirals: Multi-axis acquisition for scalar and vector electron tomography

Electron tomography (ET) has become an important tool for understanding the 3D nature of nanomaterials, with recent developments enabling not only scalar reconstructions of electron density, but also vector reconstructions of magnetic fields. However, whilst new signals have been incorporated into the ET toolkit, the acquisition schemes have largely kept to conventional single-axis tilt series for scalar ET, and dual-axis schemes for magnetic vector ET. In this work, we explore the potential of using multi-axis tilt schemes including conical and spiral tilt schemes to improve reconstruction fidelity in scalar and magnetic vector ET. Through a combination of systematic simulations and a proof-of-concept experiment, we show that spiral and conical tilt schemes have the potential to produce substantially improved reconstructions, laying the foundations of a new approach to electron tomography acquisition and reconstruction.

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

In the last few years, electron microscopy has experienced a new methodological paradigm aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine learning and artificial intelligence are answering this call providing powerful resources towards automation, exploration, and development. In this review, we evaluate the state-of-the-art of machine learning applied to electron microscopy (and obliquely, to materials and nano-sciences). We start from the traditional imaging techniques to reach the newest higher-dimensionality ones, also covering the recent advances in spectroscopy and tomography. Additionally, the present review provides a practical guide for microscopists, and in general for material scientists, but not necessarily advanced machine learning practitioners, to straightforwardly apply the offered set of tools to their own research. To conclude, we explore the state-of-the-art of other disciplines with a broader experience in applying artificial intelligence methods to their research (e.g., high-energy physics, astronomy, Earth sciences, and even robotics, videogames, or marketing and finances), in order to narrow down the incoming future of electron microscopy, its challenges and outlook.

Multimode Electron Tomography Sheds Light on Synthesis, Structure, and Properties of Complex Metal-Based Nanoparticles

Electron tomography has become a cornerstone technique for the visualization of nanoparticle morphology in three dimensions. However, to obtain in-depth information about a nanoparticle beyond surface faceting and morphology, different electron microscopy signals must be combined. The most notable examples of these combined signals include annular dark-field scanning transmission electron microscopy (ADF-STEM) with different collection angles and the combination of ADF-STEM with energy-dispersive X-ray or electron energy loss spectroscopies. Here, the experimental and computational development of various multimode tomography techniques in connection to the fundamental materials science challenges that multimode tomography has been instrumental to overcoming are summarized. Although the techniques can be applied to a wide variety of compositions, the study is restricted to metal and metal oxide nanoparticles for the sake of simplicity. Current challenges and future directions of multimode tomography are additionally discussed.

Unitary two-state quantum operators realized by quadrupole fields in the electron microscope

In this work, a novel method for using a set of electromagnetic quadrupole fields is presented to implement arbitrary unitary operators on a two-state quantum system of electrons. In addition to analytical derivations of the required quadrupole and beam settings which allow an easy direct implementation, numerical simulations of realistic scenarios show the feasibility of the proposed setup. This is expected to pave the way not only for new measurement schemes in electron microscopy and related fields but even one day for the implementation of quantum computing in the electron microscope.

Compressed sensing for electron cryotomography and high-resolution subtomogram averaging of biological specimens

Cryoelectron tomography (cryo-ET) and subtomogram averaging (STA) allow direct visualization and structural studies of biological macromolecules in their native cellular environment, in situ. Often, low signal-to-noise ratios in tomograms, low particle abundance within the cell, and low throughput in typical cryo-ET workflows severely limit the obtainable structural information. To help mitigate these limitations, here we apply a compressed sensing approach using 3D second-order total variation (CS-TV²) to tomographic reconstruction. We show that CS-TV² increases the signal-to-noise ratio in tomograms, enhancing direct visualization of macromolecules, while preserving high-resolution information up to the secondary structure level. We show that, particularly with small datasets, CS-TV² allows improvement of the resolution of STA maps. We further demonstrate that the CS-TV² algorithm is applicable to cellular specimens, leading to increased visibility of molecular detail within tomograms. This work highlights the potential of compressed sensing-based reconstruction algorithms for cryo-ET and in situ structural biology.

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Compressed sensing (CS-TV²) for cryo-ET using 3D second-order total variation

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CS-TV² increases signal contrast while retaining high-resolution information

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Improved subtomogram averaging from CS-TV² reconstructions of small datasets

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Increased contrast and detail in CS-TV² reconstructions of cellular specimens

Making the Most of your Electrons: Challenges and Opportunities in Characterizing Hybrid Interfaces with STEM

Inspired by the unique architectures composed of hard and soft materials in natural and biological systems, synthetic hybrid structures and associated soft-hard interfaces have recently evoked significant interest. Soft matter is typically dominated by fluctuations even at room temperature, while hard matter (which often serves as the substrate or anchor for the soft component) is governed by rigid mechanical behavior. This dichotomy offers considerable opportunities to leverage the disparate properties offered by these components across a wide spectrum spanning from basic science to engineering insights with significant technological overtones. Such hybrid structures, which include polymer nanocomposites, DNA functionalized nanoparticle superlattices and metal organic frameworks to name a few, have delivered promising insights into the areas of catalysis, environmental remediation, optoelectronics, medicine, and beyond. The interfacial structure between these hard and soft phases exists across a variety of length scales and often strongly influence the functionality of hybrid systems. While scanning/transmission electron microscopy (S/TEM) has proven to be a valuable tool for acquiring intricate molecular and nanoscale details of these interfaces, the unusual nature of hybrid composites presents a suite of challenges that make assessing or establishing the classical structure-property relationships especially difficult. These include challenges associated with preparing electron-transparent samples and obtaining sufficient contrast to resolve the interface between dissimilar materials given the dose sensitivity of soft materials. We discuss each of these challenges and supplement a review of recent developments in the field with additional experimental investigations and simulations to present solutions for attaining a nano or atomic-level understanding of these interfaces. These solutions present a host of opportunities for investigating and understanding the role interfaces play in this unique class of functional materials.

Super-compression of large electron microscopy time series by deep compressive sensing learning

The development of ultrafast detectors for electron microscopy (EM) opens a new door to exploring dynamics of nanomaterials; however, it raises grand challenges for big data processing and storage. Here, we combine deep learning and temporal compressive sensing (TCS) to propose a novel EM big data compression strategy. Specifically, TCS is employed to compress sequential EM images into a single compressed measurement; an end-to-end deep learning network is leveraged to reconstruct the original images. Owing to the significantly improved compression efficiency and built-in denoising capability of the deep learning framework over conventional JPEG compression, compressed videos with a compression ratio of up to 30 can be reconstructed with high fidelity. Using this approach, considerable encoding power, memory, and transmission bandwidth can be saved, allowing it to be deployed to existing detectors. We anticipate the proposed technique will have far-reaching applications in edge computing for EM and other imaging techniques.

Gradient-based and wavelet-based compressed sensing approaches for highly undersampled tomographic datasets

Electron tomography is widely employed for the 3D morphological characterization at the nanoscale. In recent years, there has been a growing interest in analytical electron tomography (AET) as it is capable of providing 3D information about the elemental composition, chemical bonding and optical/electronic properties of nanomaterials. AET requires advanced reconstruction algorithms as the datasets often consist of a very limited number of projections. Total variation (TV)-based compressed sensing approaches were shown to provide high-quality reconstructions from undersampled datasets, but staircasing artefacts can appear when the assumption about piecewise constancy does not hold. In this paper, we compare higher-order TV and wavelet-based approaches for AET applications and provide an open-source Python toolbox, Pyetomo, containing 2D and 3D implementations of both methods. A highly sampled STEM-HAADF dataset of an Er-doped porous Si sample and a heavily undersampled STEM-EELS dataset of a Ge-rich GeSbTe (GST) thin film annealed at 450°C are used to evaluate the performance of the different approaches. We show that polynomial annihilation with order 3 (HOTV₃) and the Bior4.4 wavelet outperform the classical TV minimization and the related Haar wavelet.

Improved Acquisition and Reconstruction for Wavelength-Resolved Neutron Tomography

Wavelength-resolved neutron tomography (WRNT) is an emerging technique for characterizing samples relevant to the materials sciences in 3D. WRNT studies can be carried out at beam lines in spallation neutron or reactor-based user facilities. Because of the limited availability of experimental time, potential imperfections in the neutron source, or constraints placed on the acquisition time by the type of sample, the data can be extremely noisy resulting in tomographic reconstructions with significant artifacts when standard reconstruction algorithms are used. Furthermore, making a full tomographic measurement even with a low signal-to-noise ratio can take several days, resulting in a long wait time before the user can receive feedback from the experiment when traditional acquisition protocols are used. In this paper, we propose an interlaced scanning technique and combine it with a model-based image reconstruction algorithm to produce high-quality WRNT reconstructions concurrent with the measurements being made. The interlaced scan is designed to acquire data so that successive measurements are more diverse in contrast to typical sequential scanning protocols. The model-based reconstruction algorithm combines a data-fidelity term with a regularization term to formulate the wavelength-resolved reconstruction as minimizing a high-dimensional cost-function. Using an experimental dataset of a magnetite sample acquired over a span of about two days, we demonstrate that our technique can produce high-quality reconstructions even during the experiment compared to traditional acquisition and reconstruction techniques. In summary, the combination of the proposed acquisition strategy with an advanced reconstruction algorithm provides a novel guideline for designing WRNT systems at user facilities.

Novel nonlinear reconstruction method with grey-level quantisation units for electron tomography

We report a new computed tomography reconstruction method, named quantisation units reconstruction technique (QURT), applicable to electron and other fields of tomography. Conventional electron tomography methods such as filtered back projection, weighted back projection, simultaneous iterative reconstructed technique, etc. suffer from the 'missing wedge' problem due to the limited tilt-angle range. QURT demonstrates improvements to solve this problem by recovering a structural image blurred due to the missing wedge and substantially reconstructs the structure even if the number of projection images is small. QURT reconstructs a cross-section image by arranging grey-level quantisation units (QU pieces) in three-dimensional image space via unique discrete processing. Its viability is confirmed by model simulations and experimental results. An important difference from recently developed methods such as discrete algebraic reconstruction technique (DART), total variation regularisation-DART, and compressed sensing is that prior knowledge of the conditions regarding the specimen or the expected cross-section image is not necessary.

Dynamic compressed sensing for real-time tomographic reconstruction

Electron tomography has achieved higher resolution and quality at reduced doses with recent advances in compressed sensing. Compressed sensing (CS) exploits the inherent sparse signal structure to efficiently reconstruct three-dimensional (3D) volumes at the nanoscale from undersampled measurements. However, the process bottlenecks 3D reconstruction with computation times that run from hours to days. Here we demonstrate a framework for dynamic compressed sensing that produces a 3D specimen structure that updates in real-time as new specimen projections are collected. Researchers can begin interpreting 3D specimens as data is collected to facilitate high-throughput and interactive analysis. Using scanning transmission electron microscopy (STEM), we show that dynamic compressed sensing accelerates the convergence speed by ~3-fold while also reducing its error by 27% for a Au/SrTiO₃ nanoparticle specimen. Before a tomography experiment is completed, the 3D tomogram has interpretable structure within ~33% of completion and fine details are visible as early as ~66%. Upon completion of an experiment, a high-fidelity 3D visualization is produced without further delay. Additionally, reconstruction parameters that tune data fidelity can be manipulated throughout the computation without re-running the entire process.

Electron tomography imaging methods with diffraction contrast for materials research

Transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM) enable the visualization of three-dimensional (3D) microstructures ranging from atomic to micrometer scales using 3D reconstruction techniques based on computed tomography algorithms. This 3D microscopy method is called electron tomography (ET) and has been utilized in the fields of materials science and engineering for more than two decades. Although atomic resolution is one of the current topics in ET research, the development and deployment of intermediate-resolution (non-atomic-resolution) ET imaging methods have garnered considerable attention from researchers. This research trend is probably not irrelevant due to the fact that the spatial resolution and functionality of 3D imaging methods of scanning electron microscopy (SEM) and X-ray microscopy have come to overlap with those of ET. In other words, there may be multiple ways to carry out 3D visualization using different microscopy methods for nanometer-scale objects in materials. From the above standpoint, this review paper aims to (i) describe the current status and issues of intermediate-resolution ET with regard to enhancing the effectiveness of TEM/STEM imaging and (ii) discuss promising applications of state-of-the-art intermediate-resolution ET for materials research with a particular focus on diffraction contrast ET for crystalline microstructures (superlattice domains and dislocations) including a demonstration of in situ dislocation tomography.

High-Efficiency Three-Dimensional Visualization of Complex Microstructures via Multidimensional STEM Acquisition and Reconstruction

Complex material systems in which microstructure and microchemistry are nonuniformly dispersed require three-dimensional (3D) rendering(s) to provide an accurate determination of the physio-chemical nature of the system. Current scanning transmission electron microscope (STEM)-based tomography techniques enable 3D visualization but can be time-consuming, so only select systems or regions are analyzed in this manner. Here, it is presented that through high-efficiency multidimensional STEM acquisition and reconstruction, complex point cloud-like microstructural features can quickly and effectively be reconstructed in 3D. The proposed set of techniques is demonstrated, analyzed, and verified for a high-chromium steel with heterogeneously situated features induced using high-energy neutron bombardment.

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.

Electrostatic subframing and compressive-sensing video in transmission electron microscopy

We present kilohertz-scale video capture rates in a transmission electron microscope, using a camera normally limited to hertz-scale acquisition. An electrostatic deflector rasters a discrete array of images over a large camera, decoupling the acquisition time per subframe from the camera readout time. Total-variation regularization allows features in overlapping subframes to be correctly placed in each frame. Moreover, the system can be operated in a compressive-sensing video mode, whereby the deflections are performed in a known pseudorandom sequence. Compressive sensing in effect performs data compression before the readout, such that the video resulting from the reconstruction can have substantially more total pixels than that were read from the camera. This allows, for example, 100 frames of video to be encoded and reconstructed using only 15 captured subframes in a single camera exposure. We demonstrate experimental tests including laser-driven melting/dewetting, sintering, and grain coarsening of nanostructured gold, with reconstructed video rates up to 10 kHz. The results exemplify the power of the technique by showing that it can be used to study the fundamentally different temporal behavior for the three different physical processes. Both sintering and coarsening exhibited self-limiting behavior, whereby the process essentially stopped even while the heating laser continued to strike the material. We attribute this to changes in laser absorption and to processes inherent to thin-film coarsening. In contrast, the dewetting proceeded at a relatively uniform rate after an initial incubation time consistent with the establishment of a steady-state temperature profile.

Quantifying Morphology and Diffusion Properties of Mesoporous Carbon From High-Fidelity 3D Reconstructions

A reliable quantitative analysis in electron tomography, which depends on the segmentation of the three-dimensional reconstruction, is challenging because of constraints during tilt-series acquisition (missing wedge) and reconstruction artifacts introduced by reconstruction algorithms such as the Simultaneous Iterative Reconstruction Technique (SIRT) and Discrete Algebraic Reconstruction Technique (DART). We have carefully evaluated the fidelity of segmented reconstructions analyzing a disordered mesoporous carbon used as support in catalysis. Using experimental scanning transmission electron microscopy (STEM) tomography data as well as realistic phantoms, we have quantitatively analyzed the effect on the morphological description as well as on diffusion properties (based on a random-walk particle-tracking simulation) to understand the role of porosity in catalysis. The morphological description of the pore structure can be obtained reliably both using SIRT and DART reconstructions even in the presence of a limited missing wedge. However, the measured pore volume is sensitive to the threshold settings, which are difficult to define globally for SIRT reconstructions. This leads to noticeable variations of the diffusion coefficients in the case of SIRT reconstructions, whereas DART reconstructions resulted in more reliable data. In addition, the anisotropy of the determined diffusion properties was evaluated, which was significant in the presence of a limited missing wedge for SIRT and strongly reduced for DART.

Tomographic Collection of Block-Based Sparse STEM Images: Practical Implementation and Impact on the Quality of the 3D Reconstructed Volume

The reduction of the electron dose in electron tomography of biological samples is of high significance to diminish radiation damages. Simulations have shown that sparse data collection can perform efficient electron dose reduction. Frameworks based on compressive-sensing or inpainting algorithms have been proposed to accurately reconstruct missing information in sparse data. The present work proposes a practical implementation to perform tomographic collection of block-based sparse images in scanning transmission electron microscopy. The method has been applied on sections of chemically-fixed and resin-embedded Trypanosoma brucei cells. There are 3D reconstructions obtained from various amounts of downsampling, which are compared and eventually the limits of electron dose reduction using this method are explored.

Parameters affecting the accuracy of nanoparticle shape and size measurement in 3D

While electron tomography can be used to visualize objects at nanoscale, it is difficult to perform reproducible quantitative measurements. Here we measure the shape and size of nanoparticles (NPs) in three dimensions (3D) using electron tomography. We evaluated the accuracy of maximum Feret diameter (Feret_max), minimum Feret diameter (Feret_mini) and volume of NPs measurements from reconstructed 3D images which were obtained from data acquired with varied electron dose. We perform both simulations and experiment to clarify what factors effect on the accuracy of the NP shape measurement. Based on the results, suitable reconstruction methods and threshold for binarization were evaluated. We also report comparison results obtained on exactly the same samples in two different laboratories.

Total generalized variation regularization for multi-modal electron tomography

In multi-modal electron tomography, tilt series of several signals such as X-ray spectra, electron energy-loss spectra, annular dark-field, or bright-field data are acquired at the same time in a transmission electron microscope and subsequently reconstructed in three dimensions. However, the acquired data are often incomplete and suffer from noise, and generally each signal is reconstructed independently of all other signals, not taking advantage of correlation between different datasets. This severely limits both the resolution and validity of the reconstructed images. In this paper, we show how image quality in multi-modal electron tomography can be greatly improved by employing variational modeling and multi-channel regularization techniques. To achieve this aim, we employ a coupled Total Generalized Variation (TGV) regularization that exploits correlation between different channels. In contrast to other regularization methods, coupled TGV regularization allows to reconstruct both hard transitions and gradual changes inside each sample, and links different channels at the level of first and higher order derivatives. This favors similar interface positions for all reconstructions, thereby improving the image quality for all data, in particular, for 3D elemental maps. We demonstrate the joint multi-channel TGV reconstruction on tomographic energy-dispersive X-ray spectroscopy (EDXS) and high-angle annular dark field (HAADF) data, but the reconstruction method is generally applicable to all types of signals used in electron tomography, as well as all other types of projection-based tomographies.

Imaging Three-Dimensional Elemental Inhomogeneity in Pt-Ni Nanoparticles Using Spectroscopic Single Particle Reconstruction

The properties of nanoparticles are known to critically depend on their local chemistry but characterizing three-dimensional (3D) elemental segregation at the nanometer scale is highly challenging. Scanning transmission electron microscope (STEM) tomographic imaging is one of the few techniques able to measure local chemistry for inorganic nanoparticles but conventional methodologies often fail due to the high electron dose imparted. Here, we demonstrate realization of a new spectroscopic single particle reconstruction approach built on a method developed by structural biologists. We apply this technique to the imaging of PtNi nanocatalysts and find new evidence of a complex inhomogeneous alloying with a Pt-rich core, a Ni-rich hollow octahedral intermediate shell and a Pt-rich rhombic dodecahedral skeleton framework with less Pt at ⟨100⟩ vertices. The ability to gain evidence of local surface enrichment that varies with the crystallographic orientation of facets and vertices is expected to provide significant insight toward the development of nanoparticles for sensing, medical imaging, and catalysis.

Nanomagnetic properties of the meteorite cloudy zone

Meteorites contain a record of their thermal and magnetic history, written in the intergrowths of iron-rich and nickel-rich phases that formed during slow cooling. Of intense interest from a magnetic perspective is the "cloudy zone," a nanoscale intergrowth containing tetrataenite-a naturally occurring hard ferromagnetic mineral that has potential applications as a sustainable alternative to rare-earth permanent magnets. Here we use a combination of high-resolution electron diffraction, electron tomography, atom probe tomography (APT), and micromagnetic simulations to reveal the 3D architecture of the cloudy zone with subnanometer spatial resolution and model the mechanism of remanence acquisition during slow cooling on the meteorite parent body. Isolated islands of tetrataenite are embedded in a matrix of an ordered superstructure. The islands are arranged in clusters of three crystallographic variants, which control how magnetic information is encoded into the nanostructure. The cloudy zone acquires paleomagnetic remanence via a sequence of magnetic domain state transformations (vortex to two domain to single domain), driven by Fe-Ni ordering at 320 °C. Rather than remanence being recorded at different times at different positions throughout the cloudy zone, each subregion of the cloudy zone records a coherent snapshot of the magnetic field that was present at 320 °C. Only the coarse and intermediate regions of the cloudy zone are found to be suitable for paleomagnetic applications. The fine regions, on the other hand, have properties similar to those of rare-earth permanent magnets, providing potential routes to synthetic tetrataenite-based magnetic materials.

Monofacet-Selective Cavitation within Solid-State Silica-Nanoconfinement toward Janus Iron Oxide Nanocube

Here, a highly selective solid-state nanocrystal conversion strategy is developed toward concave iron oxide (Fe₃O₄) nanocube with an open-mouthed cavity engraved exclusively on a single face. The strategy is based on a novel heat-induced nanospace-confined domino-type migration of Fe²⁺ ions from the SiO₂-Fe₃O₄ interface toward the surrounding silica shell and concomitant self-limiting nanoscale phase-transition to the Fe-silicate form. Equipped with the chemically unique cavity, the produced Janus-type concave iron oxide nanocube was further functionalized with controllable density of catalytic Pt-nanocrystals exclusively on concave sites and utilized as a highly diffusive catalytic Janus nanoswimmer for the efficient degradation of pollutant-dyes in water.

3D reconstruction of the spatial distribution of dislocation loops using an automated stereo-imaging approach

We propose an automated stereoscopic imaging approach for reconstructing the 3D spatial distribution of small dislocation loops (DLs) from 2D TEM micrographs. This method is demonstrated for small DLs in tungsten, formed by low-dose ion-implantation, that appear as circular spots in diffraction contrast TEM images. To extract the 3D position of specific DLs, their 2D position in multiple weak-beam dark-field TEM micrographs, recorded at different tilt angles, is fitted. From this fit the geometric centre and size of each DL in each micrograph can be extracted. To identify each specific DL in all the 2D projections, an automated forward prediction approach is used. A system of linear equations can then be setup, linking the 3D position of each DL to its 2D position in each projection, and solved using least-squares fitting. This approach is initially tested on synthetic data. For low projected loop densities (<20  ×  10¹⁵ m^-2) only 3 projections are required for perfect recovery of the defect microstructure. More projections are required when the projected number density increases or realistic errors are included. 3D reconstruction of experimental data from the low-dose self-ion implanted tungsten sample reveals a damage microstructure in good agreement with the depth-dependent damage profile predicted by SRIM. A comparison with weighed back-projection shows that the stereo-imaging-approach requires fewer projections, is less sensitive to the angular range spanned, and is more resilient to spurious variations in local contrast. It also allows a more straightforward retrieval of quantitative information such as size and position of each loop.

Characterization techniques for nanoparticles: comparison and complementarity upon studying nanoparticle properties

Nanostructures have attracted huge interest as a rapidly growing class of materials for many applications. Several techniques have been used to characterize the size, crystal structure, elemental composition and a variety of other physical properties of nanoparticles. In several cases, there are physical properties that can be evaluated by more than one technique. Different strengths and limitations of each technique complicate the choice of the most suitable method, while often a combinatorial characterization approach is needed. In addition, given that the significance of nanoparticles in basic research and applications is constantly increasing, it is necessary that researchers from separate fields overcome the challenges in the reproducible and reliable characterization of nanomaterials, after their synthesis and further process (e.g. annealing) stages. The principal objective of this review is to summarize the present knowledge on the use, advances, advantages and weaknesses of a large number of experimental techniques that are available for the characterization of nanoparticles. Different characterization techniques are classified according to the concept/group of the technique used, the information they can provide, or the materials that they are destined for. We describe the main characteristics of the techniques and their operation principles and we give various examples of their use, presenting them in a comparative mode, when possible, in relation to the property studied in each case.

Liquid phase blending of metal-organic frameworks

The liquid and glass states of metal-organic frameworks (MOFs) have recently become of interest due to the potential for liquid-phase separations and ion transport, alongside the fundamental nature of the latter as a new, fourth category of melt-quenched glass. Here we show that the MOF liquid state can be blended with another MOF component, resulting in a domain structured MOF glass with a single, tailorable glass transition. Intra-domain connectivity and short range order is confirmed by nuclear magnetic resonance spectroscopy and pair distribution function measurements. The interfacial binding between MOF domains in the glass state is evidenced by electron tomography, and the relationship between domain size and T_g investigated. Nanoindentation experiments are also performed to place this new class of MOF materials into context with organic blends and inorganic alloys.

Sampling limits for electron tomography with sparsity-exploiting reconstructions

Electron tomography (ET) has become a standard technique for 3D characterization of materials at the nano-scale. Traditional reconstruction algorithms such as weighted back projection suffer from disruptive artifacts with insufficient projections. Popularized by compressed sensing, sparsity-exploiting algorithms have been applied to experimental ET data and show promise for improving reconstruction quality or reducing the total beam dose applied to a specimen. Nevertheless, theoretical bounds for these methods have been less explored in the context of ET applications. Here, we perform numerical simulations to investigate performance of ℓ₁-norm and total-variation (TV) minimization under various imaging conditions. From 36,100 different simulated structures, our results show specimens with more complex structures generally require more projections for exact reconstruction. However, once sufficient data is acquired, dividing the beam dose over more projections provides no improvements-analogous to the traditional dose-fraction theorem. Moreover, a limited tilt range of ±75° or less can result in distorting artifacts in sparsity-exploiting reconstructions. The influence of optimization parameters on reconstructions is also discussed.

A Simple Preparation Method for Full-Range Electron Tomography of Nanoparticles and Fine Powders

Electron tomography has become a valuable and widely used tool for studying the three-dimensional nanostructure of materials and biological specimens. However, the incomplete tilt range provided by conventional sample holders limits the fidelity and quantitative interpretability of tomographic images by leaving a "missing wedge" of unknown information in Fourier space. Imaging over a complete range of angles eliminates missing wedge artifacts and dramatically improves tomogram quality. Full-range tomography is usually accomplished using needle-shaped samples milled from bulk material with focused ion beams, but versatile specimen preparation methods for nanoparticles and other fine powders are lacking. In this work, we present a new preparation technique in which powder specimens are supported on carbon nanofibers that extend beyond the end of a tungsten needle. Using this approach, we produced tomograms of platinum fuel cell catalysts and gold-decorated strontium titanate photocatalyst specimens. Without the missing wedge, these tomograms are free from elongation artifacts, supporting straightforward automatic segmentation and quantitative analysis of key materials properties such as void size and connectivity, and surface area and curvature. This approach may be generalized to other samples that can be dispersed in liquids, such as biological structures, creating new opportunities for high-quality electron tomography across disciplines.

Optimization of Three-Dimensional (3D) Chemical Imaging by Soft X-Ray Spectro-Tomography Using a Compressed Sensing Algorithm

Soft X-ray spectro-tomography provides three-dimensional (3D) chemical mapping based on natural X-ray absorption properties. Since radiation damage is intrinsic to X-ray absorption, it is important to find ways to maximize signal within a given dose. For tomography, using the smallest number of tilt series images that gives a faithful reconstruction is one such method. Compressed sensing (CS) methods have relatively recently been applied to tomographic reconstruction algorithms, providing faithful 3D reconstructions with a much smaller number of projection images than when conventional reconstruction methods are used. Here, CS is applied in the context of scanning transmission X-ray microscopy tomography. Reconstructions by weighted back-projection, the simultaneous iterative reconstruction technique, and CS are compared. The effects of varying tilt angle increment and angular range for the tomographic reconstructions are examined. Optimization of the regularization parameter in the CS reconstruction is explored and discussed. The comparisons show that CS can provide improved reconstruction fidelity relative to weighted back-projection and simultaneous iterative reconstruction techniques, with increasingly pronounced advantages as the angular sampling is reduced. In particular, missing wedge artifacts are significantly reduced and there is enhanced recovery of sharp edges. Examples of using CS for low-dose scanning transmission X-ray microscopy spectroscopic tomography are presented.

A Survey of the Use of Iterative Reconstruction Algorithms in Electron Microscopy

One of the key steps in Electron Microscopy is the tomographic reconstruction of a three-dimensional (3D) map of the specimen being studied from a set of two-dimensional (2D) projections acquired at the microscope. This tomographic reconstruction may be performed with different reconstruction algorithms that can be grouped into several large families: direct Fourier inversion methods, back-projection methods, Radon methods, or iterative algorithms. In this review, we focus on the latter family of algorithms, explaining the mathematical rationale behind the different algorithms in this family as they have been introduced in the field of Electron Microscopy. We cover their use in Single Particle Analysis (SPA) as well as in Electron Tomography (ET).

Sub-nanometer surface chemistry and orbital hybridization in lanthanum-doped ceria nano-catalysts revealed by 3D electron microscopy

Surface chemical composition, electronic structure, and bonding characteristics determine catalytic activity but are not resolved for individual catalyst particles by conventional spectroscopy. In particular, the nano-scale three-dimensional distribution of aliovalent lanthanide dopants in ceria catalysts and their effect on the surface electronic structure remains unclear. Here, we reveal the surface segregation of dopant cations and oxygen vacancies and observe bonding changes in lanthanum-doped ceria catalyst particle aggregates with sub-nanometer precision using a new model-based spectroscopic tomography approach. These findings refine our understanding of the spatially varying electronic structure and bonding in ceria-based nanoparticle aggregates with aliovalent cation concentrations and identify new strategies for advancing high efficiency doped ceria nano-catalysts.

Accelerating electron tomography reconstruction algorithm ICON with GPU

Electron tomography (ET) plays an important role in studying in situ cell ultrastructure in three-dimensional space. Due to limited tilt angles, ET reconstruction always suffers from the "missing wedge" problem. With a validation procedure, iterative compressed-sensing optimized NUFFT reconstruction (ICON) demonstrates its power in the restoration of validated missing information for low SNR biological ET dataset. However, the huge computational demand has become a major problem for the application of ICON. In this work, we analyzed the framework of ICON and classified the operations of major steps of ICON reconstruction into three types. Accordingly, we designed parallel strategies and implemented them on graphics processing units (GPU) to generate a parallel program ICON-GPU. With high accuracy, ICON-GPU has a great acceleration compared to its CPU version, up to 83.7×, greatly relieving ICON's dependence on computing resource.

Tomographic imaging of the photonic environment of plasmonic nanoparticles

The photonic local density of states (LDOS) governs the enhancement of light-matter interaction at the nanoscale, but despite its importance for nanophotonics and plasmonics experimental local density of states imaging remains extremely challenging. Here we introduce a tomography scheme based on electron microscopy that allows retrieval of the three-dimensional local density of states of plasmonic nanoparticles with nanometre spatial and sub-eV energy resolution. From conventional electron tomography experiments we obtain the three-dimensional morphology of the nanostructure, and use this information to compute an expansion basis for the photonic environment. The expansion coefficients are obtained through solution of an inverse problem using as input electron-energy loss spectroscopy images. We demonstrate the applicability of our scheme for silver nanocuboids and coupled nanodisks, and resolve local density of states enhancements with extreme sub-wavelength dimensions in hot spots located at roughness features or in gaps of coupled nanoparticles.Imaging the photonic local density of states of plasmonic nanoparticles remains extremely challenging. Here, the authors introduce a tomography scheme based on electron microscopy that allows retrieval of the three-dimensional local density of states with nanometre spatial and sub-eV energy resolution.

The advent of structural biology in situ by single particle cryo-electron tomography

Single particle tomography (SPT), also known as subtomogram averaging, is a powerful technique uniquely poised to address questions in structural biology that are not amenable to more traditional approaches like X-ray crystallography, nuclear magnetic resonance, and conventional cryoEM single particle analysis. Owing to its potential for in situ structural biology at subnanometer resolution, SPT has been gaining enormous momentum in the last five years and is becoming a prominent, widely used technique. This method can be applied to unambiguously determine the structures of macromolecular complexes that exhibit compositional and conformational heterogeneity, both in vitro and in situ. Here we review the development of SPT, highlighting its applications and identifying areas of ongoing development.

Compressed sensing for STEM tomography

A central challenge in scanning transmission electron microscopy (STEM) is to reduce the electron radiation dosage required for accurate imaging of 3D biological nano-structures. Methods that permit tomographic reconstruction from a reduced number of STEM acquisitions without introducing significant degradation in the final volume are thus of particular importance. In random-beam STEM (RB-STEM), the projection measurements are acquired by randomly scanning a subset of pixels at every tilt view. In this work, we present a tailored RB-STEM acquisition-reconstruction framework that fully exploits the compressed sensing principles. We first demonstrate that RB-STEM acquisition fulfills the "incoherence" condition when the image is expressed in terms of wavelets. We then propose a regularized tomographic reconstruction framework to recover volumes from RB-STEM measurements. We demonstrate through simulations on synthetic and real projection measurements that the proposed framework reconstructs high-quality volumes from strongly downsampled RB-STEM data and outperforms existing techniques at doing so. This application of compressed sensing principles to STEM paves the way for a practical implementation of RB-STEM and opens new perspectives for high-quality reconstructions in STEM tomography.

A methodology for finding the optimal iteration number of the SIRT algorithm for quantitative Electron Tomography

The SIRT (Simultaneous Iterative Reconstruction Technique) algorithm is commonly used in Electron Tomography to calculate the original volume of the sample from noisy images, but the results provided by this iterative procedure are strongly dependent on the specific implementation of the algorithm, as well as on the number of iterations employed for the reconstruction. In this work, a methodology for selecting the iteration number of the SIRT reconstruction that provides the most accurate segmentation is proposed. The methodology is based on the statistical analysis of the intensity profiles at the edge of the objects in the reconstructed volume. A phantom which resembles a a carbon black aggregate has been created to validate the methodology and the SIRT implementations of two free software packages (TOMOJ and TOMO3D) have been used.

Linear chemically sensitive electron tomography using DualEELS and dictionary-based compressed sensing

We have investigated the use of DualEELS in elementally sensitive tilt series tomography in the scanning transmission electron microscope. A procedure is implemented using deconvolution to remove the effects of multiple scattering, followed by normalisation by the zero loss peak intensity. This is performed to produce a signal that is linearly dependent on the projected density of the element in each pixel. This method is compared with one that does not include deconvolution (although normalisation by the zero loss peak intensity is still performed). Additionally, we compare the 3D reconstruction using a new compressed sensing algorithm, DLET, with the well-established SIRT algorithm. VC precipitates, which are extracted from a steel on a carbon replica, are used in this study. It is found that the use of this linear signal results in a very even density throughout the precipitates. However, when deconvolution is omitted, a slight density reduction is observed in the cores of the precipitates (a so-called cupping artefact). Additionally, it is clearly demonstrated that the 3D morphology is much better reproduced using the DLET algorithm, with very little elongation in the missing wedge direction. It is therefore concluded that reliable elementally sensitive tilt tomography using EELS requires the appropriate use of DualEELS together with a suitable reconstruction algorithm, such as the compressed sensing based reconstruction algorithm used here, to make the best use of the limited data volume and signal to noise inherent in core-loss EELS.

Biological application of Compressed Sensing Tomography in the Scanning Electron Microscope

The three-dimensional tomographic reconstruction of a biological sample, namely collagen fibrils in human dermal tissue, was obtained from a set of projection-images acquired in the Scanning Electron Microscope. A tailored strategy for the transmission imaging mode was implemented in the microscope and proved effective in acquiring the projections needed for the tomographic reconstruction. Suitable projection alignment and Compressed Sensing formulation were used to overcome the limitations arising from the experimental acquisition strategy and to improve the reconstruction of the sample. The undetermined problem of structure reconstruction from a set of projections, limited in number and angular range, was indeed supported by exploiting the sparsity of the object projected in the electron microscopy images. In particular, the proposed system was able to preserve the reconstruction accuracy even in presence of a significant reduction of experimental projections.

3D Visualization of the Iron Oxidation State in FeO/Fe3O4 Core-Shell Nanocubes from Electron Energy Loss Tomography

The physicochemical properties used in numerous advanced nanostructured devices are directly controlled by the oxidation states of their constituents. In this work we combine electron energy-loss spectroscopy, blind source separation, and computed tomography to reconstruct in three dimensions the distribution of Fe(2+) and Fe(3+) ions in a FeO/Fe3O4 core/shell cube-shaped nanoparticle with nanometric resolution. The results highlight the sharpness of the interface between both oxides and provide an average shell thickness, core volume, and average cube edge length measurements in agreement with the magnetic characterization of the sample.

Compressed Sensing Electron Tomography for Determining Biological Structure

There has been growing interest in applying compressed sensing (CS) theory and practice to reconstruct 3D volumes at the nanoscale from electron tomography datasets of inorganic materials, based on known sparsity in the structure of interest. Here we explore the application of CS for visualizing the 3D structure of biological specimens from tomographic tilt series acquired in the scanning transmission electron microscope (STEM). CS-ET reconstructions match or outperform commonly used alternative methods in full and undersampled tomogram recovery, but with less significant performance gains than observed for the imaging of inorganic materials. We propose that this disparity stems from the increased structural complexity of biological systems, as supported by theoretical CS sampling considerations and numerical results in simulated phantom datasets. A detailed analysis of the efficacy of CS-ET for undersampled recovery is therefore complicated by the structure of the object being imaged. The numerical nonlinear decoding process of CS shares strong connections with popular regularized least-squares methods, and the use of such numerical recovery techniques for mitigating artifacts and denoising in reconstructions of fully sampled datasets remains advantageous. This article provides a link to the software that has been developed for CS-ET reconstruction of electron tomographic data sets.

Nanomaterial datasets to advance tomography in scanning transmission electron microscopy

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.

Electron tomography image reconstruction using data-driven adaptive compressed sensing

Electron tomography (ET) is an increasingly important technique for the study of the three-dimensional morphologies of nanostructures. ET involves the acquisition of a set of two-dimensional projection images, followed by the reconstruction into a volumetric image by solving an inverse problem. However, due to limitations in the acquisition process, this inverse problem is ill-posed (i.e., a unique solution may not exist). Furthermore, reconstruction usually suffers from missing wedge artifacts (e.g., star, fan, blurring, and elongation artifacts). Recently, compressed sensing (CS) has been applied to ET and showed promising results for reducing missing wedge artifacts. This uses image sparsity as a priori knowledge to improve the accuracy of reconstruction, and can require fewer projections than other reconstruction techniques. The performance of CS relies heavily on the degree of sparsity in the selected transform domain and this depends essentially on the choice of sparsifying transform. We propose a new image reconstruction algorithm for ET that learns the sparsifying transform adaptively using a dictionary-based approach. We demonstrate quantitatively using simulations from complex phantoms that this new approach reconstructs the morphology with higher fidelity than either analytically based CS reconstruction algorithms or traditional weighted back projection from the same dataset. SCANNING 38:251-276, 2016. © 2015 Wiley Periodicals, Inc.

ICON: 3D reconstruction with 'missing-information' restoration in biological electron tomography

Electron tomography (ET) plays an important role in revealing biological structures, ranging from macromolecular to subcellular scale. Due to limited tilt angles, ET reconstruction always suffers from the 'missing wedge' artifacts, thus severely weakens the further biological interpretation. In this work, we developed an algorithm called Iterative Compressed-sensing Optimized Non-uniform fast Fourier transform reconstruction (ICON) based on the theory of compressed-sensing and the assumption of sparsity of biological specimens. ICON can significantly restore the missing information in comparison with other reconstruction algorithms. More importantly, we used the leave-one-out method to verify the validity of restored information for both simulated and experimental data. The significant improvement in sub-tomogram averaging by ICON indicates its great potential in the future application of high-resolution structural determination of macromolecules in situ.