Journal of microscopy最新文献

Nanoscale optical imaging has unlocked unprecedented opportunities for exploring the structural, electronic, and optical properties of low-dimensional materials with spatial resolutions far beyond the diffraction limit. Techniques such as tip-enhanced, and tip-assisted photoluminescence (TEPL and TAPL), as well as scattering-type scanning near-field optical microscopy (s-SNOM) offer unique insights into local strain distributions, exciton dynamics, and dielectric heterogeneities that are inaccessible through conventional far-field approaches, however their combination within the same setup remains challenging. Here we present the realisation of correlative TEPL/TAPL and s-SNOM measurements within a single side-illuminated near-field optical microscope. We address the key experimental challenges inherent to the side-illumination geometry, including precise laser focus alignment, suppression of far-field background signals, and the mitigation of competing scattering pathways. Utilising monolayer WSe₂ as a model system, we demonstrate correlative imaging of material topography, strain-induced photoluminescence shifts, and dielectric function variations. We visualise nanoscale heterogeneities on a bubble-like structure, highlighting the complementary information from TAPL and s-SNOM. This correlative approach bridges the gap between nanoscale optical spectroscopy and near-field imaging, offering a powerful tool for probing local strain, doping, exciton behaviour, and dielectric inhomogeneities in low-dimensional materials.

Etch-pitting replication is a classical method to characterise the microstructure of ice crystals. In this method, a solution of polyvinyl formal (Formvar) is applied to a polished surface of ice. A plastic film, created after the solvent is dried, ‘replicates’ microstructural features of the ice. By examining the replica film, we can identify the orientation of crystals and existence of dislocations in ice. However, with the recent rise of advanced techniques such as cryo-EBSD (electron backscatter diffraction) analyses, this classical method has been left in the shadows, especially from the perspective of quantification of microstructural features in polycrystalline ice. In this study we revive and thoroughly re-examine the utility of the replication method to quantify crystal orientations and dislocation density of ice. We applied our optimised protocols of the replication method to several laboratory-fabricated and natural-glacier polycrystalline ice samples with various types of crystal preferred orientation (CPO) and various levels of strain. Using high-resolution scanning electron microscope (SEM) images of the obtained replica films, we quantified the extent of CPO and dislocation density of these ice samples. Our results of CPO patterns and dislocation density show good agreement with cryo-EBSD results from the same ice samples or samples at a similar strain level. Although further improvements are needed to make the present method more efficient, our results show promise for using this method to easily, quickly, and affordably quantify microstructural features in polycrystalline ice and to help interpret deformation mechanism of ice.

Transition metal dichalcogenides (TMDs) are promising two-dimensional materials whose properties are strongly influenced by substrate interactions. While conventional Raman spectroscopy probes these effects, its diffraction-limited resolution often averages out local variations such as strain, masking intrinsic behaviours. Here, we employ tip-enhanced Raman spectroscopy (TERS) to investigate the vibrational properties of monolayer $�{\mathrm{WS}}_2$ and $�{\mathrm{MoSe}}_2$ on top of glass and glass/hBN substrates. TERS offers nanometric spatial resolution, allowing direct correlation between Raman features and topographical inhomogeneities. Our results reveal that local variations in strain, doping, and dielectric screening that vary across the substrate interface are often accompanied by nanoscale structural features such as wrinkles, which locally modulate the vibrational response.

Pattern recognition in convolutional neural networks (CNNs) is computationally intensive due to its reliance on 2D convolutions, requiring significant processing power and time. This paper proposes an analogue optical hardware system to improve CNN efficiency, focusing on forward propagation tasks such as data preparation, correlation, and decision-making. By utilising the continuous properties of light waves for 2D convolutional operations, the system overcomes key limitations of von Neumann architectures around saving power and time. Optical wave operations allow for more efficient and instantaneous tasks like 2D Fourier transforms, which are crucial to pattern recognition. The paper validates these concepts through simulations using MATLAB and COMSOL. Overall, the presented approach paves the way for more efficient ML hardware. Future work will focus on extending the system to enable full CNN training, including backward propagation, as well as the development of commercially suitable hardware implementations.

In situ synchrotron X-ray computed tomography enables dynamic material studies. However, automated segmentation remains challenging due to complex imaging artefacts - like ring and cupping effects - and limited training data. We present a methodology for deep learning-based segmentation by transforming high-quality ex situ laboratory data to train models for segmentation of in situ synchrotron data, demonstrated through a metal oxide dissolution study. Using a modified SegFormer architecture, our approach achieves segmentation performance (94.7% IoU) that matches human inter-annotator reliability (94.6% IoU). This indicates the model has reached the practical upper bound for this task, while reducing processing time by 2 orders of magnitude per 3D dataset compared to manual segmentation. The method maintains robust performance over significant morphological changes during experiments, despite training only on static specimens. This methodology can be readily applied to diverse materials systems, enabling the efficient analysis of the large volumes of time-resolved tomographic data generated in typical in situ experiments across scientific disciplines.

The comprehensive characterisation of complex, irreplaceable cultural heritage artefacts presents significant challenges for traditional analytical methods, which can fall short in providing multi-scale, non-invasive analysis. Correlative Multimodal Microscopy (CoMic), an approach that integrates data from multiple techniques, offers a powerful solution by bridging structural, chemical, and topographical information across different length scales. This paper provides a comprehensive review of the evolution, current applications, and future trajectory of CoMic within the field of heritage science. We present a historical overview of microscopy in heritage studies and detail the principles and advances of key techniques, such as electron, X-ray, optical, and probe microscopies. This review presents practical applications through case studies on materials that include wood, pigments, ceramics, metals, and textiles. To aid CoMic uptake, we also provide user-centric guides for researchers with diverse expertise. This review also examines the challenges that currently limit the widespread adoption of CoMic, challenges that include sample preparation, data correlation accuracy, high instrumental and resource costs, and the need for specialised interdisciplinary expertise. Although CoMic is a transformative methodology for artefact analysis and conservation, its full potential will be realised through future developments in accessible instrumentation, standardised protocols, and the integration of AI-driven data analysis. This review serves as a critical resource and roadmap for researchers, conservators, and institutions looking to harness the power of correlative microscopy to preserve our shared cultural legacy.

Collagen, a key structural component of the extracellular matrix, assembles through a hierarchical process of fibrillogenesis. Despite extensive studies on mature collagen fibrils, intermediates such as protofibrils remain underexplored, particularly at the nanoscale. This study presents hyperspectral tip-enhanced Raman spectroscopy (TERS) imaging of collagen protofibrils, offering chemical and structural insights into early fibrillogenesis by acquiring nanoscale molecular profiles of collagen intermediates. TERS spectra, complemented by atomic force microscopy (AFM) images, reveal characteristic molecular vibrational modes, including the phenylalanine ring breathing mode, amide II and $�{\mathrm{CH}}_2$/$�{\mathrm{CH}}_3$ stretching vibrations, with distinct spectral signatures compared to mature fibrils.

Bioimaging has transformed our understanding of biological processes, yet extracting meaningful information from complex datasets remains a challenge, particularly for biologists without computational expertise. This paper proposes a simple general approach, to help identify which image analysis methods could be relevant for a given image dataset. We first categorise structures commonly observed in bioimage data into different types related to image analysis domains. Based on these types, we provide a list of methods adapted to the quantification of images from each category. Our approach includes illustrative examples and a visual flowchart, to help researchers define analysis objectives clearly. By understanding the diversity of bioimage structures and linking them with appropriate analysis approaches, the framework empowers researchers to navigate bioimage datasets more efficiently. It also aims to foster a common language between researchers and analysts, thereby enhancing mutual understanding and facilitating effective communication.

The contributions of coherent bright-field phase and incoherent dark-field amplitude contrast are investigated for thick biological specimens. A model for a T4 phage is constructed and images simulated for both TEM and STEM phase contrast using a multislice code. For TEM, the fraction of the illumination intensity available for phase contrast imaging is limited by the fraction of electrons in the zero loss peak, the plasmon peak, or the Landau distribution peak for very thick specimens. These were measured from electron energy loss spectra recorded from various thicknesses of vitreous ice. The incoherent amplitude contrast is simulated using the Penelope Monte Carlo code. Noise limits the features that can be distinguished under the low-dose conditions required for cryo-EM, even for high electron exposures of 100 electrons/Ų. Since in STEM post specimen optics are not used to form the image inelastically scattered electrons contribute to the recorded intensity. In principle STEM should have an advantage over TEM not just for incoherent amplitude contrast but also for coherent phase contrast beyond the limit of weak phase. The simulations suggest that it should be possible to image features in the phage embedded in 1 µm of vitreous ice when collection angles are optimised for bright or dark-field signals, with best contrast achieved for accelerating voltages of about 700 keV.