The tegument of Schistosoma mansoni is a key host-parasite interface and a major target for drug and vaccine development. Although ultrastructural studies of the tegument date back decades, methodological inconsistencies often complicate comparisons across studies. Here, we present a standardized, easy-to-use framework for investigating tegumental architecture using three complementary techniques: scanning electron microscopy (SEM), transmission electron microscopy (TEM), and confocal laser scanning microscopy (CLSM). We provide step-by-step protocols, highlight strengths and limitations of each method, and illustrate their outputs using adult male worms. We also outline a scheme for unbiased quantitative analysis of the resulting images and indicate how the protocols can be adapted to female worms, to other developmental stages and schistosome species, and to immunolocalization at the ultrastructural level. This integrative pipeline enables reproducible morphological characterization and facilitates downstream applications in pharmacology, immunology, and parasite biology.

The properties of polycrystalline materials are strongly influenced by the spatial arrangement and orientations of individual grains within the microstructure, making nanoscale characterization of grain orientation essential. This is also often the case for small grains in the nm regime explored using scanning transmission electron microscopy (STEM). Automated crystal orientation mapping (ACOM) is traditionally performed using spot-like diffraction patterns. In contrast, orientation mapping based on transmission Kikuchi diffraction (TKD) using an aberration-corrected (AC) convergent STEM probe remains relatively underexplored, despite its superior orientation sensitivity and higher spatial resolution. In this work, we present an open-source software-based template-matching approach for orientation mapping using AC-STEM TKD. A master pattern (a simulated angular distribution of Kikuchi band intensities on the unit sphere) is first generated through a dynamical simulation implemented in open-source software. This resulting pattern is subsequently imported into another open-source package for geometric simulations and orientation indexing. We demonstrate the capability of the proposed method by applying it to orientation mapping in BaZr0.4Ce0.4Y0.1Yb0.1O3-δ (BZCYYb4411) fuel-cell material and LiNiO2 (LNO) lithium-ion battery cathode material. The best-matched simulated patterns exhibit strong agreement with experimental data, even under the challenging conditions with limited diffraction space available for matching.

A cocoon is a protective outer casing that supports metamorphosis of lepidopteran insects. While some lepidopteran species build cocoons solely from silk, others may incorporate other surrounding materials or recycled body parts into their cocoons. Despite extensive knowledge of cocoons from commercially valuable species, information from species that secrete a low amount of silk remains scant. The lichen-feeding moth (Cyana) is an example of a lepidopteran genus that constructs its meshed cocoon from its larval-stage setae. In this study, we uncovered the lashing silk architecture of Cyana sp. on the setal joints, which served as the cocoon's main adhesive material and was responsible for the pupa hammock-like structure. Cocoon design reconstruction and electron microscopy observations (SEM and TEM) were conducted to obtain detailed structural information of the lashing silk. Results showed that the lichen-feeding moth secreted the silk for lashing setae together and creating joints. Cross-sectional analysis demonstrated two distinctive fibroin sizes and moldable sericin as components of the silk produced by this lichen-feeding moth. We also elucidated the detailed mooring silk linkage that involved hook-shaped structures in the hammocking pupal body. This study elucidated the structural foundation for further behavioral and bio-inspired design analysis of silk from low-producing silk lepidopterans.

High-resolution Atomic Force Microscopy (AFM) serves as a foundational smart sensing technology for nanoscale characterization, yet raw topographical data is heavily degraded by spatial distortions and scanning artifacts. Standard image correction workflows depend on manual intervention or generic algorithms that ignore the highly directional, physics-based continuity constraints of AFM height maps. To enable smart, high-fidelity data interpretation, this work introduces an AI-driven, multi-stage image restoration framework. The system employs a lightweight convolutional neural network to automatically classify scan quality and isolate defect-contaminated images. For degraded scans, an intelligent topography-aware Smart Flatten module that combines adaptive baseline estimation with structural exclusion is developed to suppress macroscopic background distortions. Localized scan artifacts are subsequently identified using structural orientation metrics and repaired via directional restoration, enforcing strict scan-line consistency and spatial continuity. Validated across diverse AFM height scan datasets, the framework effectively eliminates instrumentation artifacts while preserving genuine nanoscale topography. This unified, AI-driven pipeline advances high-throughput microscopy by enabling automated quality classification and data enhancement for autonomous topography characterization and intelligent materials discovery.

Localization of bacterial symbionts within host tissues and determination of their morphology are essential for unraveling the biology of chemosymbiotic marine invertebrates. Confocal microscopy offers the potential to visualize bacterial symbionts in whole-mount preparations of host animals or tissues with minimal sample preparation. Here, we investigated the applicability of SYBR stains for visualizing the distribution of symbionts in the gills of thyasirid bivalves and in the trophosome of the siboglinid Siboglinum fiordicum. We demonstrate that SYBR stains, particularly SYBR Gold and SYBR Safe, are suitable for visualizing extracellular symbionts in whole mounts of gills, as well as intracellular endosymbionts in cells of the trophosome in whole mounts of S. fiordicum. SYBR stains show potential for confocal microscopy of invertebrates with chemosynthetic symbionts, but their utility may depend on specific experimental design; for instance, in S. fiordicum, counterstaining with DAPI improved the visibility of symbionts. Contrary to previous descriptions, we show that in male S. fiordicum the trophosome is not confined to the posterior trunk but extends anteriorly to envelop the testes. We also document the gill structure of several thyasirid species, including the first published description of gill structure of Parathyasira dunbari. In general, SYBR Gold/Safe staining can be used as a quick screen for potential symbiont-bearing species in chemosynthetic environments or for monitoring symbiont populations in laboratory-maintained symbiont-dependent hosts.

Mosquito-borne illnesses, including dengue and chikungunya, pose significant global health risks, with Aedes aegypti resistance to synthetic insecticides increasingly limiting control efficacy. This study investigated an environmentally friendly approach to vector management via the green synthesis of zinc oxide nanoparticles (ZnO NPs) employing Piper betle leaf extract, and evaluated their mosquitocidal activity. Characterization of ZnO NPs was conducted using UV-Visible spectroscopy, Fourier Transform Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM), and Energy Dispersive X-ray (EDX) analysis. A prominent UV-Vis absorption peak at 420 nm indicated nanoparticle formation. FTIR spectra confirmed functional groups (hydroxyl, carboxyl, sulfonate) implicated in reduction and stabilization processes. SEM images showed uniformly distributed, rod-shaped ZnO NPs with sizes of 1-5 nm; EDX analysis verified zinc as the major elemental component. Larvicidal and pupicidal assessments against A. aegypti revealed concentration-dependent mortality: P. betle extract displayed LC50 values of 253.17-440.74 ppm for various larval instars, while ZnO NPs exhibited greater toxicity (LC50: 189-363 ppm). Pupal LC50 was 492.86 ppm for the extract and 464.4 ppm for ZnO NPs. High predation efficiency (93.1%) of Carassius auratus (goldfish) was maintained in control setups, with minor reduction post-nanoparticle treatment. Smoke toxicity assays showed P. betle-based coil formulations induced 57% adult mosquito mortality within 40 min, indicating strong fumigant potency. These findings suggest P. betle-mediated ZnO NPs as a stable, cost-effective, and eco-safe alternative for sustainable A. aegypti control, supporting their application in integrated vector management strategies.

Quantitative characterization of treatment-induced microstructural evolution in lignocellulosic biomass using scanning electron microscopy (SEM) is challenging because image-derived descriptors are affected by both processing-induced structural changes and imaging magnification, limiting direct comparison of measurements. This study presents an ROI-based multi-scale SEM image analysis methodology for evaluating process-induced microstructural evolution and magnification-dependent descriptor behavior, demonstrated using ball-milled mustard seed husk as a representative case study. GLCM (gray-level co-occurrence matrix) texture features, including entropy (spatial randomness in gray-level relationships) and contrast, and pore characteristics were quantified from SEM images obtained at different milling stages and magnifications. Increasing GLCM entropy, contrast, and pore count, together with decreasing mean pore area, indicated progressive surface fragmentation. Entropy showed relatively low magnification sensitivity, whereas contrast and pore count were strongly scale-dependent. Pore area showed convergence at selected scales, indicating improved measurement consistency. Two-way analysis of variance (ANOVA) showed significant effects of milling time and magnification on all descriptors (p<0.001); the interaction was significant for contrast, entropy, and pore count, but not for pore area (p=0.150). The proposed framework provides a standardized approach for quantitative SEM characterization of heterogeneous lignocellulosic microstructures by considering both processing duration and imaging magnification.

Scanning electron microscopy (SEM) methods were used for the first time to elucidate the external morphology of Patchiella reaumuri reaumuri (Kaltenbach, 1843) (Hemiptera: Aphididae: Pemphiginae). We examined and compared the external morphology of stem mothers and alate viviparous females from the parthenogenetic generation. Analysis identified diverse sensilla types, including coleoconic, campaniform, placoid, and trichoid sensilla, occurring on the antennae, mouthparts, and legs. Beyond the sensilla previously documented in the literature, we identified for the first time in aphids chaetic-type sensilla on the legs of alate viviparous females. In addition, species identity was confirmed by DNA barcoding based on the mitochondrial cytochrome c oxidase subunit I (COI) gene, and the obtained sequence was deposited in GenBank under accession number PP535559.

Stenalpheops anacanthus Miya, 1997 is a commensal alpheid shrimp that inhabits burrows shared with other crustaceans and possesses a distinctive caudal appendix. In this study, scanning electron microscopy was used to examine the morphology of this structure and its associated surface structures. The caudal appendix is narrow and lamellar, gradually tapering from base to tip and exhibiting incomplete longitudinal curling. Apparent segmentation results from periodic constrictions along the lateral margins. The surface features numerous circular pores and two types of projections-simple and conical-showing certain external morphological similarities to sensilla on the second antenna. These observations provide a morphological basis for future investigations of the functional significance of this unique appendage and contribute to understanding the evolution of similar appendage structures across different crustacean lineages.

Despite their weak out-of-plane interactions, two-dimensional materials such as WS2 can retain appreciable contamination on their surfaces, complicating characterization in the electron microscope. Polymers and other heavy molecular weight compounds have been historically challenging to remove despite many conventional cleaning procedures attempted. We investigate the influence of sample exfoliation, sample transfer, and sample cleaning methods on contamination levels. While no one method was effective, a combination of solvent, vacuum-oven annealing at high temperatures, and indirect air-plasma cleaning removes pervasive WS2 sheet contaminants. At sufficiently clean levels, electron microscopy characterization such as contamination-sensitive ptychography is performed on these materials.

The point-spread function of the probe-forming optics (PSFoptics) is reported in an uncorrected (without multipole correctors) scanning electron microscope (SEM). In an SEM, information about the electron probe is generally lost as the beam interacts with the specimen. Here, we demonstrate an approach for recovering probe phase information from reconstructed probe intensity estimates. Controlled defocus was used to acquire a focal series of SEM images of 28.5,nm gold (Au) nanoparticles (NPs) on a carbon (C) film. These images were used to reconstruct their respective probe intensities, generating a focal series of probe intensities that served as input to the phase retrieval pipeline. Using the complete description (intensity and phase) of the electron probe wavefunction at the specimen plane, we estimate the PSFoptics for multiple data sets at a beam energy of E=20,keV. These results represent an important step in demonstrating the possibility of experimental aberration quantification in uncorrected SEMs.

Accurate 3D topographical metrology of nanoscale semiconductor line patterns is essential for advanced process control. However, inferring 3D depth from single top-down Scanning Electron Microscopy (SEM) images is an inherently ill-posed inverse problem due to the fundamental loss of spatial information. In this paper, we propose a proof-of-concept, physics-driven multi-channel 1D deep learning framework designed to infer 3D line roughness and surface morphologies using models trained exclusively on Monte Carlo electron yield simulations. By integrating a localized multi-line spatial context window into an efficient 1D U-Net architecture, the proposed computational approach successfully mitigates depth ambiguity while bridging the gap between 1D computational efficiency and 2D spatial awareness. Quantitative evaluations on synthetic SEM datasets demonstrate the model's extrapolation robustness across out-of-distribution roughness parameters. Furthermore, frequency-domain analysis via Power Spectral Density (PSD) confirms that our framework effectively mitigates over-smoothing artifacts, faithfully preserving the essential high-frequency stochastic roughness signatures. Finally, we demonstrate the qualitative feasibility of the framework by applying it to experimental SEM images. While generating physically plausible 3D surface profiles, we identify and discuss the synthetic-to-experimental domain shift caused by unmodeled physical phenomena, establishing a clear pathway for future calibration strategies. Ultimately, this research establishes a scalable, non-destructive, and high-throughput computational foundation for 3D morphological profiling, paving the way toward fully quantitative in-line 3D line roughness metrology in next-generation semiconductor manufacturing.

At present, the hopping mode of scanning ion conductance microscopy (SICM) is the mainstream method for probing complex sample topography. However, conventional SICM typically relies on built-in electrical sensors for Z-axis feedback control, and the dynamic performance of this feedback path can be limited by sensor bandwidth, readout delay, and system-level disturbances during high-speed scanning. As a result, the probe is more prone to overshoot during rapid approach, which increases the risk of probe-sample collision and restricts imaging speed and stability. To address this problem, an optical feedback system based on a long-range optical lever was proposed. This system utilizes a mechanical-optical synergistic amplification mechanism to convert the tiny linear Z-axis displacement of the probe into measurable spot displacement on a high-speed image sensor, providing a displacement measurement more directly related to probe-side motion. In terms of image processing and displacement calculation, a step-down sampling and constant-parameter Gaussian analytical algorithm was adopted to achieve the low-latency and rapid extraction of sub-pixel spot displacement on FPGA hardware. Experimental results show an overall displacement-tracking linearity of up to R[2] = 0.9995 and a static measurement resolution better than 2.06 nm. PDMS imaging experiments further demonstrate that, compared with conventional closed-loop control, the proposed optical feedback system achieves lower ion-current overshoot, reduced repeated-imaging error, and higher allowable approach speed under comparable image quality. These results indicate that the proposed method provides an effective feedback route for improving the dynamic performance and imaging stability of SICM.

This study investigates the interaction and deformation of air bubbles in contact with microplastic particles - specifically polyethylene (PE) and polystyrene (PS) - to advance understanding of flotation-based removal of microplastics from various aquatic environments. A range of NaCl concentrations was examined using atomic force microscopy (AFM) and a numerical model based on the augmented DLVO Young-Laplace equation, incorporating extended DLVO (eDLVO) forces. Key findings show that increasing NaCl concentrations (from 1 mM to 1000 mM) reduce the magnitude of the repulsive van der Waals interaction due to decreasing Hamaker constants. Similarly, the zeta potentials of both PE and PS particles decrease with salinity, indicating weakened electrostatic repulsion within the electrical double layer (EDL). Disjoining pressure profiles reveal a peak in repulsive pressure at a separation distance of ∼20 nm, which declines as salt concentration increases. The AFM experiments using PE colloidal probes partially agree with the numerical predictions. At low NaCl concentrations (<50 mM), no attachment was observed experimentally - contrary to model results - while at 500 mM, both approaches indicated bubble-particle attachment. These discrepancies are attributed to hydration forces, which act repulsively at low ionic strengths but can become attractive at higher concentrations. Overall, this study provides deeper insight into bubble-microplastic interactions under saline conditions, with implications for environmental remediation and materials science applications.

Stochastic Optical Reconstruction Microscopy (STORM) enables nanoscale mapping of molecular components beyond the diffraction limit; however, its reproducible implementation in hydrophobic polymer matrices remains challenging because fluorescence-labeling specificity, fluorophore photoswitching, three-dimensional localization, chromatic registration, and quantitative image analysis must be carefully controlled. This protocol presents a standardized experimental workflow for dual-color labeling, astigmatism-based three-dimensional STORM acquisition, and quantitative analysis of protein-associated and phospholipid-associated structures in natural rubber (NR). The workflow covers sample pretreatment, Cy5 NHS ester labeling of protein-associated primary amines, DiI labeling of phospholipid-rich domains, STORM imaging-buffer preparation, three-dimensional single-molecule localization, dual-channel registration, generation of standardized xy projections, aggregate-size analysis, and projected lateral spatial correlation assessment. Reproducibility is supported by defined acquisition and localization criteria, three independent sample preparations with at least five fields of view analyzed per condition, and unlabeled, single-color, dye-only matrix, and processing-associated Cy5 controls. Mean lateral localization precisions of 11.8 ± 2.3 nm for Cy5 and 13.5 ± 2.9 nm for DiI were obtained, while two-dimensional Fourier ring correlation analysis of the xy projections yielded effective lateral image resolutions of approximately 25 and 28 nm, respectively. Image-based particle segmentation and localization-coordinate-based density-based spatial clustering of applications with noise (DBSCAN) were applied to standardized xy projections as complementary quantitative approaches. Application of the protocol to untreated, centrifuged, and protease-treated NR samples demonstrated treatment-associated changes in the detected abundance and projected size distributions of protein- and phospholipid-associated aggregates, together with a non-monotonic change in their projected lateral spatial correlation. These observations describe alterations in nanoscale organization but do not, by themselves, establish stable protein-phospholipid complex formation. Unlike previous studies that primarily demonstrated the feasibility of STORM imaging in rubber materials, the principal contribution of this work is an end-to-end, step-by-step protocol incorporating defined controls, three-dimensional localization, image-quality metrics, chromatic-registration procedures, and complementary quantitative-analysis pipelines for non-expert users. The workflow may be adaptable to other hydrophobic polymers and soft-material systems after appropriate optimization and validation.