Shimane University

Tire and road wear particles (TRWP) are the largest source of microplastics. These particles are generated through friction between tires and road surfaces during use, enter aquatic environments, and may affect aquatic organisms. In this study, TRWP in sediments from 11 different water bodies in Japan were analyzed using pyrolysis gas chromatography/mass spectrometry. TRWP were detected in 32 out of 36 samples, with concentrations up to 4260 µg/g dry weight. TRWP concentrations exceeded the estimated predicted no-effect concentration threshold at 30 out of 36 sites, indicating that potential ecological risks cannot be ruled out. A correlation was found between TRWP concentration and mud content, total organic carbon, or terrestrial organic carbon (terrOC) at sites where mud accumulates. The correlation between TRWP and terrOC was significant even at sites with a notable contribution of marine-derived mud. These findings suggest that TRWP are transported with terrestrial mud and organic matter in aquatic environments. Moreover, sedimentation patterns, as deduced from sediment properties such as mud content and elemental composition, may serve as indicators of TRWP distribution. This study contributes to transport modeling, environmental risk assessments, and development of future pollution control strategies for TRWP.

The successful development of a magnetic field-free objective lens for high-resolution imaging has enabled the acquisition of atomic-resolution scanning transmission electron microscopy (STEM) images under magnetic field-free conditions around the sample. Utilizing this magnetic field-free objective lens for conventional transmission electron microscopy (TEM) observations is expected to offer advantages for the comprehensive characterization of magnetic materials. This approach is particularly significant in the context of in-situ observations. To obtain conventional TEM images, such as bright- and dark-field images, it is important to position the objective lens aperture in a diffraction plane, typically the back focal plane of the objective lens. However, positioning the objective lens aperture around the back focal plane, which is surrounded by multiple magnetic poles, is not feasible for the magnetic field-free objective lens. In this study, we describe the development of an image-forming system that can position the aperture in a diffraction plane conjugate to the back focal plane. In addition, the development of a wide-gap pole piece for the magnetic field-free objective lens has enabled the use of sample holders with thick tips for in-situ observations. The magnetic field-free electron microscope, which integrates a newly developed pole piece and image-forming system with higher-order aberration correctors, offers not only atomic-resolution TEM/STEM observations but also a versatile approach for the characterization of magnetic materials in a magnetic field-free environment.

Starch is a fundamental carbohydrate with nutritional and physicochemical properties governed by relative proportions of amylose and amylopectin. Variations in amylose-to-amylopectin ratio significantly influence starch digestibility, texture, glycemic response and dietary fiber functionality. However, conventional techniques such as iodine binding, enzymatic assays and chromatographic separation are often destructive, time-consuming and unable to provide spatially resolved molecular information. Here, we present a non-destructive, label-free approach combining Raman micro-spectroscopy with machine learning to simultaneously classify and quantify amylose and amylopectin within single starch granules. Raman spectra were collected from seven starch varieties and analyzed using multivariate techniques and machine learning including Principal Component Analysis, Linear Discriminant Analysis, Logistic Regression and Support Vector Machines, which enabled accurate discrimination based on spectral features. Key Raman marker bands including 856 and 941 cm^-1 for amylose (α-1,4 linkages) and 871 cm^-1 for amylopectin (α-1,6 branching) were identified and used in a semi-supervised Multivariate Curve Resolution analysis to resolve overlapping signals and extract pure molecular profiles. Spatial mapping and compositional estimation revealed cultivar-dependent variation, with specific amylose and amylopectin content. This integrated analytical pipeline provides a powerful tool for insitu starch characterization and molecular profiling with potential in food quality assessment, crop selection and industrial starch optimization.