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/Å. 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.

Cellulose materials are suitable to replace plastic in food packaging. They are hydrophilic and may have poor barrier properties that affect the shelf life of the food due to the migration of contaminants. The wet lamination of microfibrillated cellulose films on cellulose materials appears as a promising way to improve their barrier properties by forming a bilayer material. These barrier properties depend on the microstructural properties (porosity, pores connectivity, specific surface area or contact surface area between the two layers) of both layers, notably the film, which are poorly known. Therefore, a multiscale approach is proposed to estimate such microstructural parameters by combining three 3D imaging methods: synchrotron X-ray micro-/nanotomography and FIB-SEM tomography. The 3D microstructure of two different bilayer materials obtained with two Microfibrillated Cellulose (MFC) grades is investigated. For the first time, a full 3D representation of such material is presented. Regardless of the scale under consideration, our results showed that both films present a dense structure with very low porosity and no pore connectivity along the thickness. The MFC film produced with the smallest MFC fibrils led to a more homogeneous and less porous layer with a larger contact surface to the paper.

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 microstructure distribution of multiphase porous media has important effects on its macroscopic properties. In this paper, we propose a framework for multiphase reconstruction based on entropy statistical descriptor, which is used as morphological information to perform on two-dimensional (2D) and three-dimensional (3D) reconstruction. The accuracy of the 2D reconstruction is evaluated using the lineal-path function and two-point cluster function, which reflect the connectivity of the microstructure. From the result of the 2D reconstructions, it is noted that the reconstructed three-phase microstructures have the similar morphological information with that of the original images and their lineal-path function and two-point cluster function are closely similar to each other. For the reconstructed 3D microstructures, the accuracy of the reconstructions is also quantified by the lineal-path function and two-point cluster function, which reflect the comprehensive information of the 3D connectivity of the system. The comparison of the lineal-path function and two-point cluster function between all the slices of the reconstructed 3D structures in three directions and that of the reference images shows that the proposed method can capture the prominent features of the original images. The lineal-path function and two-point cluster function of the reconstructed 3D structures are also compared with that of their original 3D structures, which shows that the reconstructed 3D structures have the similar distribution with that of the original 3D structures. This indicates that our proposed method has the ability to capture salient morphological information of multiphase system.

Expansion microscopy (ExM) is a powerful high-resolution imaging technique that enhances the spatial resolution of conventional light microscopy by physically enlarging biological specimens by embedding and cross-linking them in a swellable polymer network. This review explores the combination of ExM with commonly used advanced fluorescence imaging modalities, including light sheet fluorescence microscopy (LSFM), stimulated emission depletion (STED), structured illumination microscopy (SIM), single-molecule localisation microscopy (SMLM), and computational super-resolution radial fluctuations (SRRF) to push the boundaries of achievable resolution in biological imaging. By integrating ExM with these optical and analytical approaches, researchers can visualise subcellular structures and molecular complexes with unprecedented clarity, enabling the study of intricate biological processes that are otherwise inaccessible with conventional light microscopy methods. The review covers the theoretical resolutions attainable with each combined technique, example biological questions they can address, and key considerations for optimising their use. Together, these advancements offer novel insights into nanoscale cellular and subcellular structures, opening new avenues for exploration in fields such as neuroscience, cancer research, and developmental biology.

Multi-slice electron ptychography has attracted significant interest in recent years, thanks to notable experimental successes in ultra-high resolution, depth-resolved imaging of atomic structure. However, the theoretical dependence of depth of field on experimental parameters is not well understood. In this paper we use simulated data to compare the depth of field of through focal annular-dark field and multi-slice electron ptychography over a range of acceleration voltages and convergence angles. We show that at both low convergence angle and at low electron energy, multi-slice ptychography has significantly improved depth of field over through focal ADF imaging.

Localisation microscopy often relies on detailed models of point-spread functions. For applications such as deconvolution or PSF engineering, accurate models for light propagation in imaging systems with a high numerical aperture are required. Different models have been proposed based on 2D Fourier transforms or 1D Bessel integrals. The most precise ones combine a vectorial description of the electric field and accurate aberration models. However, it may be unclear which model to choose as there is no comprehensive comparison between the Fourier and Bessel approaches yet. Moreover, many existing libraries are written in Java (e.g., our previous PSF generator software) or MATLAB, which hinders their integration into deep learning algorithms. In this work, we start from the original Richards-Wolf integral and revisit both approaches in a systematic way. We present a unifying framework in which we prove the equivalence between the Fourier and Bessel strategies and detail a variety of correction factors applicable to both of them. Then, we provide a high-performance implementation of our theoretical framework in the form of an open-source library that is built on top of PyTorch, a popular library for deep learning. It enables us to benchmark the accuracy and computational speed of different models and allows for an in-depth comparison of the existing models for the first time. We show that the Bessel strategy is optimal for axisymmetric beams, while the Fourier approach can be applied to more general scenarios. Our work enables the efficient computation of a point-spread function on CPU or GPU, which can then be included in simulation and optimisation pipelines.

Some biological systems exhibit nanoscale constructions to produce optical effects. This study utilises Atomic Force Microscopy (AFM) and Tip-Enhanced Raman Spectroscopy (TERS) to study the complex bionanometric structure of cicada wings. Topographical irregularities of the wings due to -chitin nanopillars hinder the probe's approach to the sample, a crucial step in overcoming the light diffraction limit in TERS measurements. To mitigate this issue, graphene was deposited, promoting surface smoothing and ensuring a reliable TERS measurement. Combined analyses of AFM and TERS mapping revealed a significant enhancement of the graphene 2D band, particularly in the regions surrounding the nanometric pillars, while the characteristic -chitin Raman peaks are evident on top of the pillars, clarifying details of how light passed through the material.

We compare three different methods of X-ray analysis in a scanning electron microscope (SEM): energy-dispersive X-ray spectroscopy (EDX), wavelength-dispersive X-ray spectroscopy (WDX) and micro X-ray fluorescence (μXRF). These methods are all applied to the same gallium arsenide (GaAs) wafer with a 0.8 nm layer of indium arsenide (InAs) on top. All methods allow detection and quantification of the indium L-line intensity from the thin InAs layer. EDX is the easiest to perform, WDX is the most sensitive and μXRF a novel technique where a poly-capillary optics is used to focus an X-ray beam from a high-voltage X-ray tube onto a small spot several micrometres wide and the characteristic X-rays produced are detected by a solid-state silicon detector similar to that used in EDX. It is to our knowledge the first time a sub-nanometre layer is reliably detected and analysed using μXRF in an SEM.

Accurate detection of mitosis is crucial in automated cell analysis, yet many existing methods depend heavily on deep learning models or complex detection techniques, which can be computationally intensive and error-prone, particularly when segmentation is incomplete. This study presents a novel unsupervised method for mitosis detection, leveraging the geometric properties of the Cassini oval to reduce computational costs and enhance robustness. Our approach integrates a newly developed deep learning model, MaxSigNet, for initial cell segmentation. We subsequently employ the Cassini oval in its single-ring mode to detect mother cells in the initial frame and switch to double-ring mode in subsequent frames to identify daughter cells and confirm mitosis events. The success of this method hinges on the presence of equal non-zero foci values in the mother cell and distinct non-zero foci values in the daughter cells, which indicate accurate mitosis detection. The method was evaluated across six datasets from four different cell lines, achieving perfect F1, Recall and Precision scores on four datasets, with scores of 96% and 85% on the remaining two. Comparative analysis demonstrated that our method outperformed similar approaches in F1 and Recall metrics. Additionally, the method showed substantial robustness to incomplete segmentation, with only a 20% average drop in F1 scores when tested with older segmentation methods like K-means, Felzenszwalb and Watershed. The proposed method offers a significant advancement in mitosis detection by leveraging the Cassini oval's properties, providing a reliable and efficient solution for automated cell analysis systems. This approach promises to enhance the accuracy and efficiency of cellular behaviour studies, with potential applications in various biomedical research fields.

Expansion microscopy (ExM) enables superresolution imaging by embedding biological specimens in a swellable hydrogel, followed by optical clearing and physical expansion. Here we present a detailed protocol for applying ExM to embryonic stages of Paracentrotus lividus, a widely used model in developmental biology. Embryos at cleavage and gastrula stages, as well as pluteus larvae, were successfully expanded fourfold after proteinase digestion. Preexpansion Airyscan imaging provided only limited subcellular information due to autofluorescence, whereas post-expansion samples displayed markedly reduced background and resolved fine structures. To address distortions caused by the calcified skeleton in pluteus larvae, we incorporated a decalcification step with EDTA, which preserved morphology and enabled isotropic expansion. Distinct NHS ester dyes further allowed differential labelling of the fertilisation envelope and blastomeres, illustrating the versatility of this approach. Together, these adaptations establish a reproducible workflow for ExM in marine invertebrates, offering a valuable methodological resource and a foundation for future applications in developmental and environmental research.

The scanning helium microscope (SHeM) is a new technology that uses a beam of neutral helium atoms to image surfaces non-destructively and with extreme surface sensitivity. Here, we present the application of the SHeM to image bacterial biofilms. We demonstrate that the SHeM uniquely and natively visualises the surface of the extracellular polymeric substance matrix in the absence of contrast agents and dyes and without inducing radiative damage.

Fluorescence lifetime imaging microscopy (FLIM) translates the duration of excited states of fluorophores into lifetime information as an additional source of contrast in images of biological samples. This offers the possibility to separate fluorophores particularly beneficial in case of similar excitation spectra. Here, we demonstrate the distinction of fluorescent molecules based on FLIM phasor analysis, called lifetime separation, in live-cell imaging using open-source software for analysis. We showcase two applications using Caenorhabditis elegans as a model system. First, we separated the highly spectrally overlapping fluorophores mCherry and mKate2 to distinctively track tagged proteins in six-dimensional datasets to investigate cell division in the developing early embryo. Second, we separated fluorescence of tagged proteins of interest from masking natural autofluorescence in adult hermaphrodites. For FLIM data handling and workflow implementation, we developed the open-source plugin napari-flim-phasor-plotter to implement conversion, visualisation, analysis and reuse of FLIM data of different formats. Our work thus advances technical applications and bioimage data management and analysis in FLIM microscopy for life science research.

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.

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 WSe 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.

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.

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.

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 and 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.

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.

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 / stretching vibrations, with distinct spectral signatures compared to mature fibrils.