Microscopy and Microanalysis最新文献

Recent work in ultra-high temperature in situ electron microscopy has presented the need for accurate, contact-free temperature determination at the microscale. Optical measurement based on thermal radiation (pyrometry) is an attractive solution but can be difficult to perform correctly due to effects, such as emissivity and optical transmission, that must be accounted for. Here, we present a practical guide to calibrating and using a spectral pyrometry system, including example code, using a Czerny-Turner spectrometer attached to a transmission electron microscope. Calibration can be accomplished using a thermocouple or commercial heated sample holder, after which arbitrary samples can be reliably measured for temperatures above ∼600∘C. An accuracy of 2% can be expected with the possibility of sub-second temporal resolution and sub-Kelvin temperature resolution. We then demonstrate this capability in conjunction with traditional microscopic techniques, such as diffraction-based strain measurement for thermal expansion coefficient, or live-video sintering evolution.

Mitochondrial division is a fundamental biological process essensial for cellular functionality and vitality. The prevailing hypothesis that dynamin related protein 1 (Drp1) provides principal control in mitochondrial division, in which it also involves the endoplasmic reticulum (ER) and the cytoskeleton, does not account for all the observations. Therefore. the hypothesis may be incomplete. Our previous study in HeLa cells led to a new hypothesis of mitochondrial division by budding. To follow-up our previous study, we employed in situ cryo-electron tomography to visualize mitochondrial budding in the intact healthy monkey kidney cells (BS-C-1 cells). Our findings reaffirm single and multiple mitochondrial budding, consistent with our observations in HeLa cells. Notably, the budding regions vary significantly in diameter and length, which may represent different stages of budding. More interestingly, neither rings nor ring-like structures, nor the wrapping of ER tubes was observed in the budding regions, suggesting mitochondrial budding is independent from Drp1 and ER. Meanwhile, we uncovered direct interactions between mitochondria and large vesicles that are distinct from small mitochondrial-derived vesicles and extracellular mitovesicles. We propose that these interacting vesicles may have mitochondrial origins.

The Bayesian genetic algorithm (BGA) is a powerful tool to reconstruct the 3D structure of mono-atomic single-crystalline metallic nanoparticles imaged using annular dark field scanning transmission electron microscopy. The number of atoms in a projected atomic column in the image is used as input to obtain an accurate and atomically precise reconstruction of the nanoparticle, taking prior knowledge and the finite precision of atom counting into account. However, as the number of parameters required to describe a nanoparticle with atomic detail rises quickly with the size of the studied particle, the computational costs of the BGA rise to prohibitively expensive levels. In this study, we investigate these computational costs and propose methods and control parameters for efficient application of the algorithm to nanoparticles of at least up to 10 nm in size.

Integrating deep learning into image analysis for transmission electron microscopy (TEM) holds significant promise for advancing materials science and nanotechnology. Deep learning is able to enhance image quality, to automate feature detection, and to accelerate data analysis, addressing the complex nature of TEM datasets. This capability is crucial for precise and efficient characterization of details on the nano-and microscale, e.g., facilitating more accurate and high-throughput analysis of nanoparticle structures. This study investigates the influence of batch normalization (BN) and instance normalization (IN) on the performance of deep learning models for semantic segmentation of high-resolution TEM images. Using U-Net and ResNet architectures, we trained models on two different datasets. Our results demonstrate that IN consistently outperforms BN, yielding higher Dice scores and Intersection over Union metrics. These findings underscore the necessity of selecting appropriate normalization methods to maximize the performance of deep learning models applied to TEM images.

Stacking faults (SFs) are important structural defects that play an essential role in the deformation of engineering alloys. However, direct observation of SFs at the atomic scale can be challenging. Here, we use the analytical field ion microscopy, including density functional theory-informed contrast estimation, to image local elemental segregation at SFs in a creep-deformed solid-solution single-crystal alloy of Ni-2 at% W. The segregated atoms are imaged brightly, and time-of-flight spectrometry allows for their identification as W. We also provide the first quantitative analysis of trajectory aberration, with a deviation of approximately 0.4 nm, explaining why atom probe tomography could not resolve these segregations. Atomistic simulations of substitutional W atoms at an edge dislocation in face-centered cubic Ni using an analytic bond-order potential indicate that the experimentally observed segregation is due to the energetic preference of W for the center of the SF, contrasting with, for example, Re segregating to partial dislocations. Solute segregation to SF can hinder dislocation motion, increasing the strength of Ni-based superalloys. Yet, direct substitution of Re by W, envisaged to lower the superalloys' costs, requires extra consideration in alloy design since these two solutes do not have comparable interactions with structural defects during deformation.

We introduce a new approach to the numerical simulation of Scanning Transmission Electron Microscopy images. The Lattice Multislice Algorithm takes advantage of the fact that the electron waves passing through the specimen have limited bandwidth and therefore can be approximated very well by a low-dimensional linear space spanned by translations of a well-localized function. Just like in the PRISM algorithm recently published by C. Ophus, we utilize the linearity of the Schrödinger equation but perform the approximations with functions that are well localized in real space instead of Fourier space. This way, we achieve a similar computational speedup as PRISM, but at a much lower memory consumption and reduced numerical error due to avoiding virtual copies of the probe waves interfering with the result. Our approach also facilitates faster recomputations if local changes are made to the specimen such as changing a single atomic column.

In this work, samples of chromia (Cr2O3) scale have been prepared for atom probe tomography and field evaporated with deep ultraviolet laser light (258 nm wavelength). The investigated range of laser energies spans more than three orders of magnitude between 0.03 and 90 pJ. Furthermore, the effects of detection rate and temperature were investigated. Simultaneous voltage and laser pulses were employed on additional needle specimens to reduce the standing voltage and minimize background noise during the measurement. Smooth evaporation with minimal mass spectrum peak tails was maintained over the whole range of measurement parameters. High laser energies result in significant underestimation of the oxygen content. Only laser energies below 1 pJ resulted in measured values near the expected oxygen content of 60 at%, the closest being about 58 at%.

In catalysis research, the amount of microscopy data acquired when imaging dynamic processes is often too much for nonautomated quantitative analysis. Developing machine learned segmentation models is challenged by the requirement of high-quality annotated training data. We thus substitute expert-annotated data with a physics-based sequential synthetic data model. We study environmental scanning electron microscopy (ESEM) data collected from isopropanol oxidation to acetone over cobalt oxide as an example. Upon applying a temperature program during the reaction a phase transition occurs, reducing the catalyst selectivity toward acetone. This is accompanied on the micrometer ESEM scale by the formation of cracks between the pores of the catalyst surface. We aim to generate synthetic data to train a neural network capable of semantic segmentation (pixel-wise labeling) of this ESEM data. This analysis will lead to insights into this phase transition. To generate synthetic data that approximates this transition, our algorithm composes the ESEM images of the room-temperature catalyst with dynamically evolving synthetic cracks satisfying physical construction principles, gathered from qualitative knowledge accessible in the ESEM data. We mimic the surface crack growth propagation along surface paths, avoiding close vicinity to nearby pores. This physics-based approach results in a lowered rate of false positives compared to a random approach.

High-frequency electric pulse signals are often applied to stimulate functional materials in devices. To investigate the relationship between materials structure and dynamic behavior under high-frequency electric excitation, the stroboscopic imaging mode is widely used in a transmission electron microscope (TEM). From a technical point of view, it is crucial to quantitatively determine high-frequency attenuation in an electric-pulse-excited stroboscopic TEM. Here, we propose the quantitative method to evaluate the voltage attenuation by using magnification variation of defocused bright-field transmission electron microscopy images in a stroboscopic mode when applying high-frequency electric pulse signals onto a model system of two opposite tungsten tips. The negative voltage excitation possesses higher high-frequency voltage attenuation than the positive voltage excitation due to the possible nonreciprocal transmission of the triangle waves within the circuit between the biasing sample holder and the arbitrary waveform generator. Our approach of high-frequency attenuation determination provides the experimental foundation for quantitative analysis on the dynamic evolution of materials structure and functionality under electric pulse stimuli.