Journal of microscopy最新文献

As microscopy diversifies and becomes ever more complex, the problem of quantification of microscopy images has emerged as a major roadblock for many researchers. All researchers must face certain challenges in turning microscopy images into answers, independent of their scientific question and the images they have generated. Challenges may arise at many stages throughout the analysis process, including handling of the image files, image pre-processing, object finding, or measurement, and statistical analysis. While the exact solution required for each obstacle will be problem-specific, by keeping analysis in mind, optimizing data quality, understanding tools and tradeoffs, breaking workflows and data sets into chunks, talking to experts, and thoroughly documenting what has been done, analysts at any experience level can learn to overcome these challenges and create better and easier image analyses.

Low-vacuum scanning electron microscopy (low-vacuum SEM) is widely used for different applications, such as the investigation of noncoated specimen or the observation of biological materials, which are not stable to high vacuum. In this study, the combination of mineral building materials (concrete or clay plaster) with a biological composite (fungal mycelium composite) by using low-vacuum SEM was investigated.

Fungal biotechnology is increasingly gaining prominence in addressing the challenges of sustainability transformation. The construction industry is one of the biggest contributors to the climate crises and, therefore, can highly profit from applications based on regenerative fungal materials. In this work, a fungal mycelium composite is used as alternative to conventional insulating materials like Styrofoam. However, to adapt bio-based products to the construction industry, investigations, optimisations and adaptations to existing solutions are needed. This paper examines the compatibility between fungal mycelium materials with mineral-based materials to demonstrate basic feasibility. For this purpose, fresh and hardened concrete specimens as well as clay plaster samples are combined with growing mycelium from the tinder fungus Fomes fomentarius. The contact zone between the mycelium composite and the mineral building materials is examined by scanning electron microscopy (SEM).

The combination of these materials proves to be feasible in general. The use of hardened concrete or clay with living mycelium composite appears to be the favoured variant, as the hyphae can grow into the surface of the building material and thus a layered structure with a stable connection is formed.

In order to work with the combination of low-density organic materials and higher-density inorganic materials simultaneously, low-vacuum SEM offers a suitable method to deliver results with reduced effort in preparation while maintaining high capture and magnification quality. Not only are image recordings possible with SE and BSE, but EDX measurements can also be carried out quickly without the influence of a coating. Depending on the signal used, as well as the magnification, image-recording strategies must be adapted. Especially when using SE, an image-integration method was used to reduce the build-up of point charges from the electron beam, which damages the mycelial hyphae. Additionally using different signals during image capture is recommended to confirm acquired information, avoiding misinterpretations.

In this article, the optical and structural properties of iPP/TiO₂ nanocomposite fibres, considering three distinct extrusion speeds (25, 50 and 78 m/min) in addition to blank isotactic polypropylene samples were determined. Employing computed tomographic scans, localised optical defects in the nanocomposite fibres are unveiled, while refractive indices are examined by analysing transmitted intensity with incident light vibrating parallel and perpendicular to the fibre axis. The internal structure is further characterised through birefringence and density calculations. Mechanical properties, specifically stiffness, are probed by measuring elastic modulus values along the fibre. The investigation extends to the presence of TiO₂ nanoparticles in the isotactic polypropylene matrix, inspecting their influence on the uniform morphology along and across the fibre. While the addition of TiO₂ nanoparticles has many advantages, including enhanced properties, the study shows adverse effects on the morphological integrity of the fibres, particularly at higher extrusion rates. Micrographs are included to visually illustrate these findings, providing a comprehensive understanding of the complex interaction between extrusion rates, TiO₂ nanoparticle incorporation, and the resulting optical and structural properties in iPP fibres.

A new method is proposed to measure the linear coefficient of thermal expansion (CTE) of solid metals and ceramics of micron-sized dimensions. This approach uses a focused ion beam (FIB) to extract and transfer a slab of the sample, typically (15–20) ×10 × (3–5) µm onto a Micro-Electro-Mechanical Systems (MEMS) in situ heating holder inside a scanning electron microscope (SEM). CTE is thereafter calculated by image correlating the change of length (ΔL) between the fiducial marks on the slab as a function of temperature, taking advantage of the temperature calibration of the MEMS heating holder and nanometre resolution of the scanning electron microscope. The CTE results are validated to be consistent with standard copper and silicon. We further demonstrate the method on a graphene platelet reinforced copper composite and a graphite filler phase isolated from a bulk sample, these represent materials that cannot be practically synthesised or isolated at the macro-scale. Errors associated with the measurement are discussed.

Plants continuously face various environmental stressors throughout their lifetime. To be able to grow and adapt in different environments, they developed specialized tissues that allowed them to maintain a protected yet interconnected body. These tissues undergo specific primary and secondary cell wall modifications that are essential to ensure normal plant growth, adaptation and successful land colonization. The composition of cell walls can vary among different plant species, organs and tissues. The ability to remodel their cell walls is fundamental for plants to be able to cope with multiple biotic and abiotic stressors. A better understanding of the changes taking place in plant cell walls may help identify and develop new strategies as well as tools to enhance plants’ survival under environmental stresses or prevent pathogen attack. Since the invention of microscopy, numerous imaging techniques have been developed to determine the composition and dynamics of plant cell walls during normal growth and in response to environmental stimuli. In this review, we discuss the main advances in imaging plant cell walls, with a particular focus on fluorescent stains for different cell wall components and their compatibility with tissue clearing techniques.

Lay Description: Plants are continuously subjected to various environmental stresses during their lifespan. They evolved specialized tissues that thrive in different environments, enabling them to maintain a protected yet interconnected body. Such tissues undergo distinct primary and secondary cell wall alterations essential to normal plant growth, their adaptability and successful land colonization. Cell wall composition may differ among various plant species, organs and even tissues. To deal with various biotic and abiotic stresses, plants must have the capacity to remodel their cell walls. Gaining insight into changes that take place in plant cell walls will help identify and create novel tools and strategies to improve plants’ ability to withstand environmental challenges. Multiple imaging techniques have been developed since the introduction of microscopy to analyse the composition and dynamics of plant cell walls during growth and in response to environmental changes. Advancements in plant tissue cleaning procedures and their compatibility with cell wall stains have significantly enhanced our ability to perform high-resolution cell wall imaging. At the same time, several factors influence the effectiveness of cleaning and staining plant specimens, as well as the time necessary for the process, including the specimen's size, thickness, tissue complexity and the presence of autofluorescence. In this review, we will discuss the major advances in imaging plant cell walls, with a particular emphasis on fluorescent stains for diverse cell wall components and their compatibility with tissue clearing techniques. We hope that this review will assist readers in selecting the most appropri

The Ambassador Bridge between Detroit, Michigan, and Windsor, Ontario, has served for almost 100 years as North America's busiest international border crossing. But in 2025, the Ambassador will be replaced by the new Gordie Howe International Bridge. The Gordie Howe is a cable-stayed bridge, with two massive 220 m tall concrete piers on opposite banks of the St. Claire River, a single clear span of 853 m, and 42 m of clearance over this busy waterway. To ensure durability in this harsh freeze-thaw environment, air-entrained concrete is specified throughout. And, to ensure the quality of air entrainment, the ASTM C 457 Procedure C, Contrast Enhanced Method is employed. While a similar automated microscopic approach has been in use for well over a decade according to EN 480-11 Determination of air void characteristics in hardened concrete, this is the first large-scale application of automated air void assessment in North American infrastructure. According to the ASTM Procedure C, the air void characteristics are determined through digital image processing, while the paste content may be determined by either mix design parameters, manual point count, or ‘other means’. Of these three options, point counting is used for Gordie Howe; but in parallel, during each point count, the digital image coordinates and phase identifications for each evaluated stop are recorded. This allows for training of a neural network, for automated determination of paste content, as demonstrated here.

3D imaging via X-ray microscopy (XRM), a form of tomography, is revolutionising materials characterisation. Nondestructive imaging to classify grains, particles, interfaces and pores at various scales is imperative for our understanding of the composition, structure, and failure of building materials. Various workflows now exist to maximise data collection and to push the boundaries of what has been achieved before, either from singular instruments, software or combinations through multimodal correlative microscopy. An evolving area on interest is the XRM data acquisition and data processing workflow; of particular importance is the improvement of the data acquisition process of samples that are challenging to image, usually because of their size, density (atomic number) and/or the resolution they need to be imaged at. Modern advances include deep/machine learning and AI resolutions for this problem, which address artefact detection during data reconstruction, provide advanced denoising, improved quantification of features, upscaling of data/images, and increased throughput, with the goal to enhance segmentation and visualisation during postprocessing leading to better characterisation of samples. Here, we apply three AI and machine-learning-based reconstruction approaches to cements and concretes to assist with image improvement, faster throughput of samples, upscaling of data, and quantitative phase identification in 3D. We show that by applying advanced machine learning reconstruction approaches, it is possible to (i) vastly improve the scan quality and increase throughput of ‘thick’ cores of cements/concretes through enhanced contrast and denoising using DeepRecon Pro, (ii) upscale data to larger fields of view using DeepScout and (iii) use quantitative automated mineralogy to spatially characterise and quantify the mineralogical/phase components in 3D using Mineralogic 3D. These approaches significantly improve the quality of collected XRM data, resolve features not previously accessible, and streamline scanning and reconstruction processes for greater throughput.

Optical microscopy is an indispensable tool in life sciences research, but conventional techniques require compromises between imaging parameters like speed, resolution, field of view and phototoxicity. To overcome these limitations, data-driven microscopes incorporate feedback loops between data acquisition and analysis. This review overviews how machine learning enables automated image analysis to optimise microscopy in real time. We first introduce key data-driven microscopy concepts and machine learning methods relevant to microscopy image analysis. Subsequently, we highlight pioneering works and recent advances in integrating machine learning into microscopy acquisition workflows, including optimising illumination, switching modalities and acquisition rates, and triggering targeted experiments. We then discuss the remaining challenges and future outlook. Overall, intelligent microscopes that can sense, analyse and adapt promise to transform optical imaging by opening new experimental possibilities.

This paper reports on the development of an open-source image analysis software ‘pipeline’ dedicated to petrographic microscopy. Using conventional rock thin sections and images from a standard polarising microscope, the pipeline can classify minerals and subgrains into objects and obtain information about optic-axis orientation. Five metamorphic rocks were chosen to test and illustrate the method. Thin sections were imaged using reflected and cross- and plane-polarised transmitted light. Images were taken at different angles of the polariser and analyser (360° with 10° steps), both with and without the full-lambda plate. The resulting image stacks were analysed with a modular pipeline for optic-axis mapping (POAM). POAM consists of external and internal software packages that register, segment, classify, and interpret the visible light spectra using object-based image analysis (OBIAS). The mapped fields-of-view and grain orientation stereonets of interest are presented in the context of whole-slide images.

Two innovations are reported. First, we used hierarchical tree region merging on blended multimodal images to classify individual grains of rock-forming minerals into objects. Second, we assembled a new optical mineralogy algorithm chain that identifies the mineral slow axis orientation. The c-axis orientation results were verified with scanning electron microscopy electron backscattered diffraction (SEM-EBSD) data. For quartz (uniaxial) in a granite mylonite the test yielded excellent correspondence of c-axis azimuth and good agreement for inclination. For orthorhombic orthopyroxene in a deformed garnet harzburgite, POAM produced acceptable results for slow axis azimuth. In addition, the method identified slight anisotropy in garnet that would not be appreciated by traditional microscopy.

We propose that our method is ideally suited for two commonly performed tasks in mineralogy. First, for mineral grain classification of entire thin sections scans on blended images to provide automated modal abundance estimates and grain size distribution. Second, for prospective fields of view of interest, POAM can rapidly generate slow axis crystal orientation maps from multiangle image stacks on conventionally prepared thin sections for targeting detailed SEM-EBSD studies.