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Platinum (Pt)-based multicomponent alloys have been widely regarded as the best catalysts for oxygen reduction reaction (ORR) in proton exchange membrane fuel cells (PEMFCs). Herein, a set of Pt-based medium entropy alloys with precisely controllable composition and structure have been designed theoretically. The obtained medium entropy alloys like PtCuSn or PtCuW tend to form an atomistic thick Pt-skin structure with W or Sn atomic segregation at the subsurface. Based on oxygen reduction kinetic current calculations, the PtCuSn exhibits an onset potential of 1.09 V and half-wave potential of 0.86 V and also displays increased vacancy energy, revealing remarkably enhanced intrinsic activity and structural stability, compared with the other tested Pt-based catalysts. The improved ORR activity can be attributed to unique electronic structure, compressive microgeometry, and low work function. This work can shed a light on the design of novel medium-, high-entropy nanoalloys as next-generation catalysts for PEMFCs. • The medium entropy Pt-based alloys designed firstly at atomic level. • ORR kinetics current over entropy alloys determined quantitatively by DFT. • Insight into entropy-dependent electronic and microgeometric structures. [ABSTRACT FROM AUTHOR]

Microplastics are difficult to degrade and widespread environmental pollutants. Coastal areas are hardest hit of microplastic pollution as they receive significant amounts of microplastics discharged from inland sources. Golden pompano (Trachinotus blochii) is a high commercial valuable marine aquaculture fish species, most of the golden pompano are raised in coastal areas, which means they are at significant risk of exposure to microplastics. Therefore, we exposed golden pompano to 10 μg/L, 100 μg/L and 1000 μg/L of 5 μm spherical polystyrene microplastics and conducted a 14-day stress experiment. Histopathology results showed the intestinal villi shrank. The 16s sequencing analysis revealed that microplastics significantly impacted the abundance and community structure of intestinal microorganisms, which may affect the metabolic function of the gastrointestinal tract. Metabolomics sequencing of the intestinal contents showed that microplastics caused disruptions in lipid, glucose, and amino acid metabolism, thus compromising the normal digestion and absorption functions in the intestinal system. In addition, the activation of various pathways, including the intestinal endocrine system, proline metabolism, and signal transduction, which can lead to the occurrence of several diseases. This study combined various methods to investigate the adverse effects of microplastics on intestinal digestion and absorption, and provided new insights into the toxic mechanisms of microplastics. [Display omitted] • MPs damage the intestinal structure, affecting digestion and absorption functions. • MPs alter the diversity, structure and function of intestinal microbiota. • MPs inhibit intestinal enzyme activity and interfere with energy metabolism. [ABSTRACT FROM AUTHOR]

Deep learning-based object detection methods using ultrasound images to assist clinicians in rapid and accurate diagnosis of thyroid nodules has been an attractive area of research. Despite the great progress made by object detectors, these may still produce unsatisfactory performance due to certain factors, such as the presence of abundant background information and significant nodule size variations in ultrasound images of thyroid nodules. In this study, we draw on the idea of a Transformer and design a powerful thyroid nodule ultrasound imaging detection model, namely Thyroid-DETR. The proposed Thyroid-DETR has the following features: (i) a deformable convolution v2 is introduced flexibly capture small nodules of irregular shapes and small sizes, (ii) a multi-head self-attention structure is designed to acquire and learn broader correlated information in the shallow layer, (iii) a dual-stream training structure with BatchFormerV2 is developed to fully capture the effective information of medical images; (iv) fine-tuning methods to optimize the model are used to improve the diagnostic accuracy for detecting thyroid nodules. To evaluate the efficacy and effectiveness of the proposed approach. Extensive experiments and comprehensive comparisons are conducted on real thyroid nodule ultrasound images from the hospital. The results show that our method significantly outperforms the state-of-the-art methods. • Using the Transformer's self-attention mechanism, it accurately identifies small abnormalities in ultrasound images, aiding early disease detection reliably. • End-to-end thyroid ultrasound detection with DN-DETR model that considers all objects in the image in a global perspective. • The proposed Thyroid-DETR is compared with state-of-the-art detection algorithms and the experimental results prove its effectiveness and superiority. • The successful demonstration of the transformer technology for ultrasound image detection supports future exploration in other medical imaging applications. [ABSTRACT FROM AUTHOR]