Ultramicroscopy最新文献

In this paper, a methodology is presented to count the number of atoms in heterogeneous nanoparticles based on the combination of multiple annular dark field scanning transmission electron microscopy (ADF STEM) images. The different non-overlapping annular detector collection regions are selected based on the principles of optimal statistical experiment design for the atom-counting problem. To count the number of atoms, the total intensities of scattered electrons for each atomic column, the so-called scattering cross-sections, are simultaneously compared with simulated library values for the different detector regions by minimising the squared differences. The performance of the method is evaluated for simulated Ni@Pt and Au@Ag core–shell nanoparticles. Our approach turns out to be a dose efficient alternative for the investigation of beam-sensitive heterogeneous materials as compared to the combination of ADF STEM and energy dispersive X-ray spectroscopy.

Scanning electrochemical microscopy (SECM) is a scanning probe microscope with an ultramicroelectrode (UME) as a probe. The technique is advantageous in the characterization of the electrochemical properties of surfaces. However, the limitations, such as slow imaging and many functions depending on the user, only allow us to use some of the possibilities. Therefore, we applied visual recognition and machine learning to detect micro-objects from the image and determine their electrochemical activity. The reconstruction of the image from several approach curves allows it to scan faster and detect active areas of the sample. Therefore, the scanning time and presence of the user is diminished. An automated scanning electrochemical microscope with visual recognition has been developed using commercially available modules, relatively low-cost components, design, software solutions proven in other fields, and an original control and data fusion algorithm.

Four-dimensional Scanning Transmission Electron Microscopy (4D-STEM) is a powerful technique for high-resolution and high-precision materials characterization at multiple length scales, including the characterization of beam-sensitive materials. However, the field of view of 4D-STEM is relatively small, which in absence of live processing is limited by the data size required for storage. Furthermore, the rectilinear scan approach currently employed in 4D-STEM places a resolution- and signal-dependent dose limit for the study of beam sensitive materials. Improving 4D-STEM data and dose efficiency, by keeping the data size manageable while limiting the amount of electron dose, is thus critical for broader applications. Here we introduce a general method for reconstructing 4D-STEM data with subsampling in both real and reciprocal spaces at high fidelity. The approach is first tested on the subsampled datasets created from a full 4D-STEM dataset, and then demonstrated experimentally using random scan in real-space. The same reconstruction algorithm can also be used for compression of 4D-STEM datasets, leading to a large reduction (100 times or more) in data size, while retaining the fine features of 4D-STEM imaging, for crystalline samples.

We demonstrate the use of a 4-dimensional scanning transmission electron microscope (4D-STEM) to extract atomic cross section information in amorphous materials. We measure the scattering amplitudes of 200 keV electrons in several representative specimens: amorphous carbon, silica, amorphous ice of pure water, and vitrified phosphate buffer solution. Diffraction patterns are recorded by 4D-STEM with or without energy filter at the zero-loss peak. In addition, Electron Energy Loss Spectroscopy (EELS) data are acquired at several thicknesses and energies. Mixed elastic and inelastic contributions for thick samples can be decoupled based on a convolution model. Measured differential cross sections between 1 and 3 mrad are due primarily to plasmon excitations and follow precisely a 1/θ² angular distribution. The measured intensities match Inokuti's calculations of total dipole matrix elements for discrete dipole transitions alone, i.e., transitions to bound states of the atom and not to continuum states. We describe the fundamental mechanism of plasmon excitation in insulators as a two-step interaction process with a fast electron. First, a target electron in the specimen is excited, the probability for which follows from the availability of atomic transitions, with a strong dependence on the column of the periodic table. Second, the dielectric response of the material determines the energy loss. The energy of the loss peak depends primarily on the valence electrons. Elastic scattering is dominant at higher angles, and can be fitted conveniently to 1/θ^3.7 with a linear dependence on atomic number for light atoms. In order to facilitate the interpretation of 4D STEM measurements in terms of material composition, we introduce two key parameters. Zeta is an analytical equivalent of classical STEM Z-contrast, determined by the ratio of elastic to inelastic scattering coefficients, while eta is the elastic coefficient divided by thickness. The two parameters may serve for identification of basic classes of materials in biological and other amorphous organic specimens.

Energy-Dispersive X-Ray Spectroscopy (EDS) is a technique frequently used in Scanning and Transmission Electron Microscopes to study the elemental composition of a sample. Briefly, high energy electrons of the incident electron beam may ionize an electron from a core shell. The decay of this excited state may result in the emission of a characteristic X-ray photon or Auger-Meitner electron. A solid-state EDS detector captures the X-ray photon and determines its energy. The energy spectrum thus contains information on the elemental make-up of the sample. Low Energy Electron Microscopy (LEEM) typically utilizes incident electrons with energies in the range 0–100 eV, insufficient for the generation of elemental X-rays. In general, LEEM does therefore not allow for elemental characterization of the sample under study. Here we show how relatively simple modifications and additions to the LEEM instrument make in-situ EDS spectroscopy possible, and how high-quality EDS spectra can be obtained, thus enabling elemental analysis in LEEM instruments for the first time.

The local misorientation, known as KAM, is affected by both the step size (representing the spacing of measurement points applied in orientation measurements) and the point distance (indicating the distance between the points used in the misorientation calculation). The point distance can be increased by selecting surrounding points that are far from the target point. This study proposed the concept of an equidistant local misorientation, for which surrounding points at the same point distance from the target point were selected to calculate misorientation. An arbitrary point distance can be set for the equidistant local misorientation regardless of the step size. The changes in equidistant local misorientation for various point distances were calculated for the crystal orientation datasets obtained with different step sizes and measurement grids (square or hexagonal) using Type 316 stainless steel specimens, in which plastic strain of about 5 % was induced. It was shown that the equidistant local misorientation was identical regardless of the step size and measurement grid when the same point distance was used. Then, it was concluded that the difference in the local misorientation which emanated from the difference in step size could be corrected by employing the equidistant local misorientation. Increasing the point distance improved the signal-to-noise (S/N) ratio in the mapping data of the equidistant local misorientation. However, the results suggested that the maximum point distance for enhancing the S/N ratio should be within 30 % of the average grain size. On the other hand, decreasing the step size by keeping the point distance constant was found not to improve the S/N ratio, while it enhanced the spatial resolution of the mapping data.

Selected area electron diffraction (SAED) is a widely used technique for characterizing the structure and measuring lattice parameters of materials. An autonomous analytic method has become an urgent demand for the large-scale SAED data produced from in-situ experiments. In this work, we realize the automatic processing for center identification with a proposed deep segmentation model named the multi-scale Transformer (MS-Trans) network. This algorithm enables robust segmentation of the central spots by combining a novel gated axial-attention module and multi-scale feature fusion. The proposed MS-Trans model shows high precision and robustness, enabling autonomous processing of SAED patterns without any prior knowledge. The application on in-situ SAED data of the oxidation process of FeNi alloy demonstrates its capability of implementing autonomous quantitative processing.

© 2017 Elsevier Inc. All rights reserved.

M-subshell X-ray production cross sections were indirectly measured for Ir and Bi targets irradiated with monoenergetic electron beams. The projectile energy range ran from 2.2 to 28 keV, impinging on Ir and Bi pure bulk targets in a scanning electron microscope. The resulting X-ray emission spectra were acquired with an energy dispersive spectrometer, and processed afterwards by means of a robust parameter optimization procedure developed previously. X-ray production cross sections were finally obtained through an approach involving an analytical prediction for the emission spectra, which relies on the ionization depth distribution function. The values obtained by this approach were compared with empirical and theoretical predictions, appealing to different relaxation data taken from the literature.

The association of scanning transmission electron microscopy (STEM) and detection of a diffraction pattern at each probe position (so-called 4D-STEM) represents one of the most promising approaches to analyze structural properties of materials with nanometric resolution and low irradiation levels. This is widely used for texture analysis of materials using automated crystal orientation mapping (ACOM). Herein, we perform orientation mapping in InP nanowires exploiting precession electron diffraction (PED) patterns acquired by an axial CMOS camera. Crystal orientation is determined at each probe position by the quantitative analysis of diffracted intensities minimizing a residue comparing experiments and simulations in analogy to x-ray structural refinement. Our simulations are based on the two-beam dynamical diffraction approximation and yield a high angular precision (∼0.03°), much lower than the traditional ACOM based on pattern matching algorithms (∼1°). We anticipate that simultaneous exploration of both spot positions and high precision crystal misorientation will allow the exploration of the whole potentiality provided by PED-based 4D-STEM for the characterization of deformation fields in nanomaterials.

The authors of this study develop an accurate and fast method for the localization of the pattern centers (PCs) in the electron backscatter diffraction (EBSD) technique by using the model of deformation of screen moving technology. The proposed algorithm is divided into two steps: (a) Approximation: We use collinear feature points to obtain the initial value of the coordinates of the PC and the zoom factor. (b) Subdivision: We then construct a deformation function containing the three parameters to be solved, select a large region for global registration, use the inverse compositional Gauss–Newton (ICGN) to optimize the objective function, and obtain the results of iteration of the PC and the zoom factor. The proposed algorithm was applied to simulated patterns, and yielded an accuracy of measurement of the PCs that was better than $4.6\times {10}^{-6}$ of their resolution while taking only 0.2 s for computations. Moreover, the proposed algorithm has a large radius of convergence that makes it robust to the initial estimate. We also discuss the influence of factors of mechanical instability on its results of calibration during the insertion of the detector, and show that errors in measurements caused by the tilt motion of the camera are related only to the tilt angle of its motion and the detector distance, and are unrelated to the distance moved by it.