Advanced Structural and Chemical Imaging最新文献

The automated detection of defects in high-angle annular dark-field Z-contrast (HAADF) scanning-transmission-electron microscopy (STEM) images has been a major challenge. Here, we report an approach for the automated detection and categorization of structural defects based on changes in the material’s local atomic geometry. The approach applies geometric graph theory to the already-found positions of atomic-column centers and is capable of detecting and categorizing any defect in thin diperiodic structures (i.e., “2D materials”) and a large subset of defects in thick diperiodic structures (i.e., 3D or bulk-like materials). Despite the somewhat limited applicability of the approach in detecting and categorizing defects in thicker bulk-like materials, it provides potentially informative insights into the presence of defects. The categorization of defects can be used to screen large quantities of data and to provide statistical data about the distribution of defects within a material. This methodology is applicable to atomic column locations extracted from any type of high-resolution image, but here we demonstrate it for HAADF STEM images.

Scanning transmission electron microscopy (STEM) at low energies (≤?30?keV) in a scanning electron microscope is well suited to distinguish weakly scattering materials with similar materials properties and analyze their microstructure. The capabilities of the technique are illustrated in this work to resolve material domains in PTB7:PC₇₁BM bulk-heterojunctions, which are commonly implemented for light-harvesting in organic solar cells. Bright-field (BF-) and high-angle annular dark-field (HAADF-) STEM contrast of pure PTB7 and PC₇₁BM was first systematically analyzed using a wedge-shaped sample with well-known thickness profile. Monte-Carlo simulations are essential for the assignment of material contrast for materials with only slightly different scattering properties. Different scattering cross-sections were tested in Monte-Carlo simulations with screened Rutherford scattering cross-sections yielding best agreement with the experimental data. The STEM intensity also depends on the local specimen thickness, which can be dealt with by correlative STEM and scanning electron microscopy (SEM) imaging of the same specimen region yielding additional topography information. Correlative STEM/SEM was applied to determine the size of donor (PTB7) and acceptor (PC₇₁BM) domains in PTB7:PC₇₁BM absorber layers that were deposited from solution with different contents of the processing additive 1,8-diiodooctane (DIO).

The standard technique for sub-pixel estimation of atom positions from atomic resolution scanning transmission electron microscopy images relies on fitting intensity maxima or minima with a two-dimensional Gaussian function. While this is a widespread method of measurement, it can be error prone in images with non-zero aberrations, strong intensity differences between adjacent atoms or in situations where the neighboring atom positions approach the resolution limit of the microscope. Here we demonstrate mpfit, an atom finding algorithm that iteratively calculates a series of overlapping two-dimensional Gaussian functions to fit the experimental dataset and then subsequently uses a subset of the calculated Gaussian functions to perform sub-pixel refinement of atom positions. Based on both simulated and experimental datasets presented in this work, this approach gives lower errors when compared to the commonly used single Gaussian peak fitting approach and demonstrates increased robustness over a wider range of experimental conditions.

Hole-free phase plates (HFPPs), also known as Volta phase plates, were already demonstrated to be well suited for in-focus transmission electron microscopy imaging of organic objects. However, the underlying physical processes have not been fully understood yet. To further elucidate the imaging properties of HFPPs, phase shift measurements were carried out under different experimental conditions. Both positive and negative phase shifts occur depending on the diameter of the zero-order electron beam and the HFPP film temperature. The analysis of Thon ring patterns of an amorphous carbon test sample reveals that the phase-shifting patch can be significantly larger than the size of the zero-order beam on the HFPP film. An HFPP was used for in-focus phase contrast imaging of carbon nanotube (CNT) bundles under positive and negative phase-shifting conditions. The comparison of experimental and simulated images of CNT bundles gives detailed information on the phase shift profile, which depends on the spatial frequency in the vicinity of the zero-order beam. The shape of the phase shift profile also explains halo-like image artifacts that surround the imaged objects.

STEM XEDS spectrum images can be drastically denoised by application of the principal component analysis (PCA). This paper looks inside the PCA workflow step by step on an example of a complex semiconductor structure consisting of a number of different phases. Typical problems distorting the principal components decomposition are highlighted and solutions for the successful PCA are described. Particular attention is paid to the optimal truncation of principal components in the course of reconstructing denoised data. A novel accurate and robust method, which overperforms the existing truncation methods is suggested for the first time and described in details.

Scanning precession electron diffraction involves the acquisition of a two-dimensional precession electron diffraction pattern at every probe position in a two-dimensional scan. The data typically comprise many more diffraction patterns than the number of distinct microstructural volume elements (e.g. crystals) in the region sampled. A dimensionality reduction, ideally to one representative diffraction pattern per distinct element, may then be sought. Further, some diffraction patterns will contain contributions from multiple crystals sampled along the beam path, which may be unmixed by harnessing this oversampling. Here, we report on the application of unsupervised machine learning methods to achieve both dimensionality reduction and signal unmixing. Potential artefacts are discussed and precession electron diffraction is demonstrated to improve results by reducing the impact of bending and dynamical diffraction so that the data better approximate the case in which each crystal yields a given diffraction pattern.

Accurate quantum mechanical scanning transmission electron microscopy image simulation methods such as the multislice method require computation times that are too large to use in applications in high-resolution materials imaging that require very large numbers of simulated images. However, higher-speed simulation methods based on linear imaging models, such as the convolution method, are often not accurate enough for use in these applications. We present a method that generates an image from the convolution of an object function and the probe intensity, and then uses a multivariate polynomial fit to a dataset of corresponding multislice and convolution images to correct it. We develop and validate this method using simulated images of Pt and Pt–Mo nanoparticles and find that for these systems, once the polynomial is fit, the method runs about six orders of magnitude faster than parallelized CPU implementations of the multislice method while achieving a 1???R² error of 0.010–0.015 and root-mean-square error/standard deviation of dataset being predicted of about 0.1 when compared to full multislice simulations.

Dislocations and stacking faults are important crystal defects, because they strongly influence material properties. As of now, transmission electron microscopy (TEM) is the most frequently used technique to study the properties of single dislocations and stacking faults. Specifically, the Burgers vector b of dislocations or displacement vector R of stacking faults can be determined on the basis of the g·b?=?n (g·R?=?n) criterion by setting up different two-beam diffraction conditions with an imaging vector g. Based on the reciprocity theorem, scanning transmission electron microscopy (STEM) can also be applied for defect characterization, but has been less frequently used up to now. In this work, we demonstrate g·b?=?n (g·R?=?n) analyses of dislocations and stacking faults in GaN by STEM imaging in a scanning electron microscope. The instrument is equipped with a STEM detector, double-tilt TEM specimen holder, and a charge-coupled-device camera for the acquisition of on-axis diffraction patterns. The latter two accessories are mandatory to control the specimen orientation, which has not been possible before in a scanning electron microscope.

One of the biggest bottlenecks for structural analysis of proteins remains the creation of high-yield and high-purity samples of the target protein. Cell-free protein synthesis technologies are powerful and customizable platforms for obtaining functional proteins of interest in short timeframes, while avoiding potential toxicity issues and permitting high-throughput screening. These methods have benefited many areas of genomic and proteomics research, therapeutics, vaccine development and protein chip constructions. In this work, we demonstrate a versatile and multiscale eukaryotic wheat germ cell-free protein expression pipeline to generate functional proteins of different sizes from multiple host organism and DNA source origins. We also report on a robust purification procedure, which can produce highly pure (>?98%) proteins with no specialized equipment required and minimal time invested. This pipeline successfully produced and analyzed proteins in all three major geometry formats used for structural biology including single particle analysis with electron microscopy, and both two-dimensional and three-dimensional protein crystallography. The flexibility of the wheat germ system in combination with the multiscale pipeline described here provides a new workflow for rapid production and purification of samples that may not be amenable to other recombinant approaches for structural characterization.

In the realm of signal and image denoising and reconstruction, (ell _1) regularization techniques have generated a great deal of attention with a multitude of variants. In this work, we demonstrate that the (ell _1) formulation can sometimes result in undesirable artifacts that are inconsistent with desired sparsity promoting (ell _0) properties that the (ell _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.