Influence of average radii of RE3+ ions on phase structures and thermal expansion coefficients of high-entropy pyrosilicates

High-entropy pyrosilicate element selection is relatively blind, and the thermal expansion coefficient (CTE) of traditional β-type pyrosilicate is not adjustable, making it difficult to meet the requirements of various types of ceramic matrix composites (CMCs). The following study aimed to develop a universal rule for high-entropy pyrosilicate element selection and to achieve directional control of the thermal expansion coefficient of high-entropy pyrosilicate. The current study investigates a high-entropy design method for obtaining pyrosilicates with stable β-phase and γ-phase by introducing various rare-earth (RE) cations. The solid-phase method was used to create 12 different types of high-entropy pyrosilicates with 4–6 components. The high-entropy pyrosilicates gradually transformed from β-phase to γ-phase with an increase in the average radius of RE3+ ions (r¯(RE3+)). The nine pyrosilicates with a small r¯(RE3+) preserve β-phase or γ-phase stability at room temperature to the maximum of 1400 ℃. The intrinsic relationship between the thermal expansion coefficient, phase structure, and RE–O bond length has also been found. This study provides the theoretical background for designing high-entropy pyrosilicates from the perspective of r¯(RE3+). The theoretical guidance makes it easier to synthesize high-entropy pyrosilicates with stable β-phase or γ-phase for the use in environmental barrier coatings (EBCs). The thermal expansion coefficient of γ-type high-entropy pyrosilicate can be altered through component design to match various types of CMCs

OphGLM: Training an Ophthalmology Large Language-and-Vision Assistant based on Instructions and Dialogue

Large multimodal language models (LMMs) have achieved significant success in general domains. However, due to the significant differences between medical images and text and general web content, the performance of LMMs in medical scenarios is limited. In ophthalmology, clinical diagnosis relies on multiple modalities of medical images, but unfortunately, multimodal ophthalmic large language models have not been explored to date. In this paper, we study and construct an ophthalmic large multimodal model. Firstly, we use fundus images as an entry point to build a disease assessment and diagnosis pipeline to achieve common ophthalmic disease diagnosis and lesion segmentation. Then, we establish a new ophthalmic multimodal instruction-following and dialogue fine-tuning dataset based on disease-related knowledge data and publicly available real-world medical dialogue. We introduce visual ability into the large language model to complete the ophthalmic large language and vision assistant (OphGLM). Our experimental results demonstrate that the OphGLM model performs exceptionally well, and it has the potential to revolutionize clinical applications in ophthalmology. The dataset, code, and models will be made publicly available at https://github.com/ML-AILab/OphGLM.Comment: OphGLM:The first ophthalmology large language-and-vision assistant based on instructions and dialogu

Local atomic structure studies of Zr55Cu35Al10 alloy around T g

Abstract As a result of examining the structure of Zr55Cu35Al10 alloy around the glass transition temperature (T g ) using the classical molecular dynamics simulations, it was proven that the atomic bonds in the interconnecting zones (i-zones) became loose with the small amount of energy absorption, and it became free volumes easily when the temperature approached T g . Instead of i-zones, when clusters were largely separated by free volume networks, the solid amorphous structure was converted into supercooled liquid state, resulting in a sharp strength reduce and the great plasticity change from a limited plastic deformation to superplasticity

FNeXter: A Multi-Scale Feature Fusion Network Based on ConvNeXt and Transformer for Retinal OCT Fluid Segmentation

The accurate segmentation and quantification of retinal fluid in Optical Coherence Tomography (OCT) images are crucial for the diagnosis and treatment of ophthalmic diseases such as age-related macular degeneration. However, the accurate segmentation of retinal fluid is challenging due to significant variations in the size, position, and shape of fluid, as well as their complex, curved boundaries. To address these challenges, we propose a novel multi-scale feature fusion attention network (FNeXter), based on ConvNeXt and Transformer, for OCT fluid segmentation. In FNeXter, we introduce a novel global multi-scale hybrid encoder module that integrates ConvNeXt, Transformer, and region-aware spatial attention. This module can capture long-range dependencies and non-local similarities while also focusing on local features. Moreover, this module possesses the spatial region-aware capabilities, enabling it to adaptively focus on the lesions regions. Additionally, we propose a novel self-adaptive multi-scale feature fusion attention module to enhance the skip connections between the encoder and the decoder. The inclusion of this module elevates the model’s capacity to learn global features and multi-scale contextual information effectively. Finally, we conduct comprehensive experiments to evaluate the performance of the proposed FNeXter. Experimental results demonstrate that our proposed approach outperforms other state-of-the-art methods in the task of fluid segmentation

Quaternized Chitosan as an Antimicrobial Agent: Antimicrobial Activity, Mechanism of Action and Biomedical Applications in Orthopedics

Chitosan (CS) is a linear polysaccharide with good biodegradability, biocompatibility and antimicrobial activity, which makes it potentially useful for biomedical applications, including an antimicrobial agent either alone or blended with other polymers. However, the poor solubility of CS in most solvents at neutral or high pH substantially limits its use. Quaternary ammonium CS, which was prepared by introducing a quaternary ammonium group on a dissociative hydroxyl group or amino group of the CS, exhibited improved water solubility and stronger antibacterial activity relative to CS over an entire range of pH values; thus, this quaternary modification increases the potential biomedical applications of CS in the field of anti-infection. This review discusses the current findings on the antimicrobial properties of quaternized CS synthesized using different methods and the mechanisms of its antimicrobial actions. The potential antimicrobial applications in the orthopedic field and perspectives regarding future studies in this field are also considered