Journal of microscopy最新文献

Highly oriented pyrolytic graphite (HOPG) is one of the most used host materials for obtaining and investigating graphite intercalated compounds, because of the high degree structural order of this polycrystal. Experiments on electrochemically intercalated HOPG in sulphuric acid have a model character, as the results obtained can be usefully generalised, not only with respect to other graphite compounds but also for the intercalation of other layered host lattices. In addition, the HOPG/H₂SO₄ system has an attractive potential for the possibility of electrochemically producing graphite oxide, ideally, by reversible oxidation/reduction cycles, which is of interest for energy storage and graphene production on an industrial scale. However, the oxidation/reduction cycles in such electrochemical intercalation process are not reversible and topotactic, so that the HOPG structure is considerably altered. This alteration may affect, for instance, the quality of the electrochemically produced graphene. In particular, the impact the electrochemical intercalation has on the conductivity of basal planes of HOPG, and so on graphene sheets, is still debated. In this work, we investigated both the macroscopic and microscopic electron transfer (ET) kinetics of the HOPG surface, before and after the intercalation of 1 M H₂SO₄ to obtain graphite intercalated compound, by using cyclic voltammetry (CV) and scanning electrochemical microscopy (SECM), respectively. The heterogeneous kinetic constant (k⁰) of the HOPG was evaluated quantitatively by using the redox systems [Fe(CN)₆]^3-/4- and [Ru(NH₃)₆]^3+/2+. The morphology of the samples was also investigated by atomic force microscopy (AFM), which revealed a widespread formation of blisters and precipitates during the HOPG intercalation process. The CV and SECM results indicate that, upon intercalation, the electrochemical behaviour of the HOPG changes sensibly and the ET decreases sensibly. However, this effect depends on the redox mediators employed and it results more dramatic for the [Fe(CN)₆]^3-/4- system, for which a decrease of k⁰ by orders of magnitude was obtained. The decrease of ET can be correlated to the blisters and precipitates, which occur during the HOPG intercalation, as observed by AFM.

Microscopy and image analysis play a vital role in parasitology research; they are critical for identifying parasitic organisms and elucidating their complex life cycles. Despite major advancements in imaging and analysis, several challenges remain. These include the integration of interdisciplinary data; information derived from various model organisms; and data acquired from clinical research. In our view, artificial intelligence-with the latest advances in machine and deep learning-holds enormous potential to address many of these challenges. This review addresses how artificial intelligence, machine learning and deep learning have been used in the field of parasitology-mainly focused on Apicomplexan, Diplomonad, and Kinetoplastid groups. We explore how gaps in our understanding could be filled by AI in future parasitology research and diagnosis in the field. Moreover, it addresses challenges and limitations currently faced in implementing and expanding the use of artificial intelligence across biomedical fields. The necessary increased collaboration between biologists and computational scientists will facilitate understanding, development, and implementation of the latest advances for both scientific discovery and clinical impact. Current and future AI tools hold the potential to revolutionise parasitology and expand One Health principles.

To enhance the indexing rate of conventional electron backscatter diffraction (EBSD), this study employed EBSD to collect and analyse the mapping data of cubic phase materials. Kikuchi bands were identified using Hough transform, and the pattern centre was optimised through a genetic algorithm. Four objective functions were designed to investigate the influence of varying population sizes on the convergence of the algorithm. The results revealed that the calculation stabilised when the population size reached 400, with the HMAE (H-mean angular error) objective function exhibiting superior performance in screening by integrating the number of matched Kikuchi bands and mean angular error (MAE). Furthermore, to address indexing errors resulting from overlapping Kikuchi patterns at grain boundaries, an indexing optimisation method based on pattern similarity matching was proposed, significantly improving the indexing rate of EBSD mapping data. Finally, neighbourhood search strategy was implemented to further refine the indexing process, ensuring high indexing accuracy while substantially reducing computational time. This study offers novel methodologies and insights for improving the efficiency and precision of EBSD mapping data acquisition and analysis.

Influenza B virus and SARS-CoV-2 virus are the two most representative respiratory infectious diseases. These two viruses not only show similarities in clinical symptoms but also have numerous similarities in microstructure, which is difficult to distinguish and poses great challenges for diagnosis. In this work, the three-dimensional structures and surface features of influenza B virus and SARS-CoV-2 virus were investigated using atomic force microscopy. The results indicated that there were substantial differences in surface morphology and structure between the two viruses. Specifically, the average diameter of SARS-CoV-2 virus particles was around 222.8 nm while that of influenza B virus particles is smaller at about 191.2 nm. The height of SARS-CoV-2 particles was also larger, averaging about 30–60 nm, while that of influenza B virus particles averaged around 10–30 nm. Additionally, the crown-like structure on the surface of the SARS-CoV-2 virus was sparser and more prominent than that of the influenza virus. These findings offer significant insights into the distinction between the two viruses, aiding in the accurate characterisation of SARS-CoV-2 and influenza viruses and facilitating timely and effective treatment strategies.

Energy storage technologies that are efficient are in constant demand. Supercapacitors have attracted much interest among these gadgets because of their superior cycle stability and high-power density. This work used a simple and cost-effective sonication-assisted electrodeposition approach to develop tin oxide nanoparticles on functionalised carbon foam substrate with different concentration ratios (1 mM, 3 mM, and 5 mM). FTIR, XRD, and SEM validated the chemical, structural, and morphological characteristics of all nanostructured electrodes. The tetragonal structure with spherical shape was the result of the fine crystallisation of the tin oxide nanoparticles. The electrochemical characteristics are evaluated by CV, EIS, and GCD testing. Among all electrodes, Sn₁@CF has a larger electrochemically active surface area, low internal resistance, and high specific capacitance. These findings underscore that the binder-free Sn₁@CF electrode is a promising candidate for high-efficiency supercapacitor applications.

X-rays, secondary electrons, and other emitted electrons need to be extracted at a large solid angle to enhance electron collection efficiency in transmission electron microscopy. The finite element method is employed to investigate the effects of different polepiece angles on the spherical aberration coefficient of polepiece. The research findings reveal that the azimuthal angle β of the upper polepiece has a substantial effect on the spherical aberration coefficient. When β = 30°, the minimum spherical aberration coefficient is achieved. When β ≥ 50°, the spherical aberration coefficient increases significantly, which adversely affects imaging. The aperture size of the upper polepiece has a relatively minor effect on the spherical aberration. The design of the large-angle polepiece offers novel design concepts for future emission X-ray/electron collection devices, while also offering a new reference for the design of objective lenses in transmission electron microscopy.

A successful scanning tunnelling microscopy (STM) experiment relies on both delicate sample preparation and measurement, and careful image filtering and analysis to provide clear and solid results. Processing and analysis of STM images may result in a tricky task, due to the complexity and specificity of the probed systems. In this paper, we introduce our recently developed, simple Python-based methods for filtering and analysing STM images, with the aim of providing a semi-quantitative treatment of the input data. Case studies will be presented using images obtained through electrochemical STM. Additionally, we propose a straightforward yet effective universal drift-correction tool for SPM image sequences.

Laser scanning fluorescence microscopy (LSFM) is a widely used imaging method, but image quality is often degraded by noise. Averaging techniques can enhance the signal-to-noise ratio (SNR), but while this can improve image quality, excessive frame accumulation can introduce photobleaching and may lead to unnecessarily long acquisition times. A classical software method called PerfectlyAverage is presented to determine the optimal number of frames for averaging in LSFM using SNR, photobleaching, and power spectral density (PSD) measurements. By assessing temporal intensity variations across frames in a time series, PerfectlyAverage identifies the point where additional averaging ceases to provide significant noise reduction. Experiments with fluorescently stained tissue paper and fibroblast cells validated the approach, demonstrating that up to a fourfold reduction in averaging time may be possible. PerfectlyAverage is open source, compatible with any LSFM data, and it is aimed at improving imaging workflows while reducing the reliance on subjective criteria for choosing the number of averages.

Propagation-based imaging (one method of X-ray phase contrast imaging) with microcomputed tomography (PBI-µCT) offers the potential to visualise low-density materials, such as soft tissues and hydrogel constructs, which are difficult to be identified by conventional absorption-based contrast µCT. Conventional µCT reconstruction produces edge-enhanced contrast (EEC) images which preserve sharp boundaries but are susceptible to noise and do not provide consistent grey value representation for the same material. Meanwhile, phase retrieval (PR) algorithms can convert edge enhanced contrast to area contrast to improve signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) but usually results to over-smoothing, thus creating inaccuracies in quantitative analysis. To alleviate these problems, this study developed a deep learning-based method called edge view enhanced phase retrieval (EVEPR), by strategically integrating the complementary spatial features of denoised EEC and PR images, and further applied this method to segment the hydrogel constructs in vivo and ex vivo. EVEPR used paired denoised EEC and PR images to train a deep convolutional neural network (CNN) on a dataset-to-dataset basis. The CNN had been trained on important high-frequency details, for example, edges and boundaries from the EEC image and area contrast from PR images. The CNN predicted result showed enhanced area contrast beyond conventional PR algorithms while improving SNR and CNR. The enhanced CNR especially allowed for the image to be segmented with greater efficiency. EVEPR was applied to in vitro and ex vivo PBI-µCT images of low-density hydrogel constructs. The enhanced visibility and consistency of hydrogel constructs was essential for segmenting such material which usually exhibit extremely poor contrast. The EVEPR images allowed for more accurate segmentation with reduced manual adjustments. The efficiency in segmentation allowed for the generation of a sizeable database of segmented hydrogel scaffolds which were used in conventional data-driven segmentation applications. EVEPR was demonstrated to be a robust post-image processing method capable of significantly enhancing image quality by training a CNN on paired denoised EEC and PR images. This method not only addressed the common issues of over-smoothing and noise susceptibility in conventional PBI-µCT image processing but also allowed for efficient and accurate in vitro and ex vivo image processing applications of low-density materials.