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Developing a high-performance electrocatalyst for synthesizing ammonia from nitrate represents a promising solution for addressing wastewater pollution and achieving sustainable ammonia production. However, it remains a formidable challenge. Herein, an intermetallic AuCu3 electrocatalyst with high-density active sites is designed and prepared for an efficient nitrate electroreduction to generate ammonia. Remarkably, the Faraday efficiency and yield rate of ammonia at -0.9 V are 97.6% and 75.9 mg h-1 cm-2, respectively. More importantly, after 10 cycles of testing, the removal rate of nitrate can still reach 95.2%. Electrochemical in situ Fourier transform infrared analysis indicates that AuCu3 IM can promote the adsorption of nitrate and enhance ammonia production from nitrate. *NH3, *NO, and *NO2 have been proven to be active intermediates. Theoretical and experimental studies show that the Au site can provide a large amount of *H for nitrate reduction, and the Cu site is conducive to the reduction of nitrate to produce nitrogen-containing products. Meanwhile, AuCu3 intermetallic compounds (AuCu3 IM) can inhibit the dimerization of *H. The power density and ammonia yield of the assembled Zn-nitrate battery reached 2.17 mW cm-2 and 71.2 mg h-1 cm-2, respectively.
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Exosomes are double phospholipid membrane vesicles that are synthesized and secreted by a variety of cells, including T cells, B cells, dendritic cells, immune cells, are extracellular vesicles. Recent studies have revealed that exosomes can play a significant role in under both physiological and pathological conditions. They have been implicated in regulation of inflammatory responses, immune response, angiogenesis, tissue repair, and antioxidant activities, particularly in modulating immunity in autoimmune diseases (AIDs). Moreover, variations in the expression of exosome-related substances, such as miRNA and proteins, may not only offer valuable perspectives for the early warning, and prognostic assessment of various AIDs, but may also serve as novel markers for disease diagnosis. This article examines the impact of exosomes on the development of AIDs and explores their potential for therapeutic application.
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The classification of surrounding rock quality is critical for the dynamic construction and design of tunnels. However, obtaining complete parameters for predicting the surrounding rock grades is always challenging in complex tunnel geological environment. In this study, a new method based on Bayesian networks is proposed to predict the probability for the classification of surrounding rock quality of tunnel with incomplete data. A database is collected with 286 cases in 10 tunnels, involving nine parameters: rock hardness, weathering degree, rock mass integrity, rock mass structure, structural plane integrity, in-situ stress, groundwater, rock basic quality, and surrounding rock level. Moreover, the Bayesian network structure is built using the collected database and quantitatively verified by strength analysis. Then, the accuracy, precision, recall, F-measure and receiver operating characteristic (ROC) curves are utilized for model evaluation. The average values of accuracy, precision, recall, F-measure, and area under the curve (AUC) are approximately 89.2%, 91%, 92%, 91%, and 0.98, respectively. These results indicate that the established classification model has high accuracy, even with small sample size and imbalanced samples. Ten additional sets of tunnel cases (incomplete data) are also used for verification. The results reveal that compared with the traditional Q-system (Q) and rock mass rating (RMR) classification methods, the proposed classification model has the lowest error rate and is capable of using incomplete data to predict sample results. Finally, sensitivity analysis suggests that the rock hardness and rock mass integrity have the strongest impact on the quality of tunnel surrounding rock. Overall, the findings of this study can serve as a useful reference for future rock mass quality evaluation in tunnels, underground powerhouses, slopes, etc.
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Diabetic retinopathy (DR) is the most common complication of diabetes mellitus (DM), which can lead to visual impairment and even blindness in severe cases. DR is generally considered to be a microvascular disease but its pathogenesis is still unclear. A large body of evidence shows that the development of DR is not determined by a single factor but rather by multiple related mechanisms that lead to different degrees of retinal damage in DR patients. Therefore, this article briefly reviews the pathophysiological changes in DR, and discusses the occurrence and development of DR resulting from different factors such as oxidative stress, inflammation, neovascularization, neurodegeneration, the neurovascular unit, and gut microbiota, to provide a theoretical reference for the development of new DR treatment strategies.