Improved Three-Dimensional (3D) Resolution of Electron Tomograms Using Robust Mathematical Data-Processing Techniques.

Electron tomography has become an essential tool for three-dimensional (3D) characterization of nanomaterials. In recent years, advances have been made in specimen preparation and mounting, acquisition geometries, and reconstruction algorithms. All of these components work together to optimize the resolution and clarity of an electron tomogram. However, one important component of the data-processing has received less attention: the 2D tilt series alignment. This is challenging for a number of reasons, namely because the nature of the data sets and the need to be coherently aligned over the full range of angles. An inaccurate alignment may be difficult to identify, yet can significantly limit the final 3D resolution. In this work, we present an improved center-of-mass alignment model that allows us to overcome discrepancies from unwanted objects that enter the imaging area throughout the tilt series. In particular, we develop an approach to overcome changes in the total mass upon rotation of the imaging area. We apply our approach to accurately recover small Pt nanoparticles embedded in a zeolite that may otherwise go undetected both in the 2D microscopy images and the 3D reconstruction. In addition to this, we highlight the particular effectiveness of the compressed sensing methods with this data set.

Nanoporous Dielectric Resistive Memories Using Sequential Infiltration Synthesis.

Resistance switching in metal-insulator-metal structures has been extensively studied in recent years for use as synaptic elements for neuromorphic computing and as nonvolatile memory elements. However, high switching power requirements, device variabilities, and considerable trade-offs between low operating voltages, high on/off ratios, and low leakage have limited their utility. In this work, we have addressed these issues by demonstrating the use of ultraporous dielectrics as a pathway for high-performance resistive memory devices. Using a modified atomic layer deposition based technique known as sequential infiltration synthesis, which was developed originally for improving polymer properties such as enhanced etch resistance of electron-beam resists and for the creation of films for filtration and oleophilic applications, we are able to create ∼15 nm thick ultraporous (pore size ∼5 nm) oxide dielectrics with up to 73% porosity as the medium for filament formation. We show, using the Ag/Al2O3 system, that the ultraporous films result in ultrahigh on/off ratio (>109) at ultralow switching voltages (∼±600 mV) that are 10× smaller than those for the bulk case. In addition, the devices demonstrate fast switching, pulsed endurance up to 1 million cycles. and high temperature (125 °C) retention up to 104 s, making this approach highly promising for large-scale neuromorphic and memory applications. Additionally, this synthesis methodology provides a compatible, inexpensive route that is scalable and compatible with existing semiconductor nanofabrication methods and materials.

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.

Multiscale higher-order TV operators for L1 regularization.

In the realm of signal and image denoising and reconstruction, ℓ 1 regularization techniques have generated a great deal of attention with a multitude of variants. In this work, we demonstrate that the ℓ 1 formulation can sometimes result in undesirable artifacts that are inconsistent with desired sparsity promoting ℓ 0 properties that the ℓ 1 formulation is intended to approximate. With this as our motivation, we develop a multiscale higher-order total variation (MHOTV) approach, which we show is related to the use of multiscale Daubechies wavelets. The relationship of higher-order regularization methods with wavelets, which we believe has generally gone unrecognized, is shown to hold in several numerical results, although notable improvements are seen with our approach over both wavelets and classical HOTV. These results are presented for 1D signals and 2D images, and we include several examples that highlight the potential of our approach for improving two- and three-dimensional electron microscopy imaging. In the development approach, we construct the tools necessary for MHOTV computations to be performed efficiently, via operator decomposition and alternatively converting the problem into Fourier space.

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.

Electron tomography and fractal aspects of MoS2 and MoS2/Co spheres.

A study was made by a combination of 3D electron tomography reconstruction methods and N2 adsorption for determining the fractal dimension for nanometric MoS2 and MoS2/Co catalyst particles. DFT methods including Neimarke-Kiselev's method allowed to determine the particle porosity and fractal arrays at the atomic scale for the S-Mo-S(Co) 2D- layers that conform the spherically shaped catalyst particles. A structural and textural correlation was sought by further characterization performed by x-ray Rietveld refinement and Radial Distribution Function (RDF) methods, electron density maps, computational density functional theory methods and nitrogen adsorption methods altogether, for studying the structural and textural features of spherical MoS2 and MoS2/Co particles. Neimark-Kiselev's equations afforded the evaluation of a pore volume variation from 10 to 110 cm3/g by cobalt insertion in the MoS2 crystallographic lattice, which induces the formation of cavities and throats in between of less than 29 nm, with a curvature radius r k < 14.4 nm; typical large needle-like arrays having 20 2D layers units correspond to a model consisting of smooth surfaces within these cavities. Decreasing D P , D B , D I and D M values occur when Co atoms are present in the MoS2 laminates, which promote the formation of smoother edges and denser surfaces that have an influence on the catalytic properties of the S-Mo-S(Co) system.

Recovering fine details from under-resolved electron tomography data using higher order total variation ℓ1 regularization.

Over the last decade or so, reconstruction methods using ℓ1 regularization, often categorized as compressed sensing (CS) algorithms, have significantly improved the capabilities of high fidelity imaging in electron tomography. The most popular ℓ1 regularization approach within electron tomography has been total variation (TV) regularization. In addition to reducing unwanted noise, TV regularization encourages a piecewise constant solution with sparse boundary regions. In this paper we propose an alternative ℓ1 regularization approach for electron tomography based on higher order total variation (HOTV). Like TV, the HOTV approach promotes solutions with sparse boundary regions. In smooth regions however, the solution is not limited to piecewise constant behavior. We demonstrate that this allows for more accurate reconstruction of a broader class of images - even those for which TV was designed for - particularly when dealing with pragmatic tomographic sampling patterns and very fine image features. We develop results for an electron tomography data set as well as a phantom example, and we also make comparisons with discrete tomography approaches.