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2D black phosphorus (b‐P) possesses several remarkable properties, including ambipolar transport, high carrier mobility, in‐plane anisotropy, polarization sensitivity, a narrow direct bandgap that can be tuned with the number of layers, and highly compatible with silicon‐based technologies. These characteristics make it a promising material for photodetection in the near‐infrared to mid‐infrared range. However, to date, most of the reviews on b‐P are centered around electronic and optoelectronic devices, with few specifically addressing infrared detection. Herein, the recent research progress on b‐P infrared detectors is summarized in this review. This article introduces the principle of optoelectronic detection, the main properties of 2D b‐P, the development history of b‐P fabrication methods, presents and discusses the performance and characteristics of various infrared photodetectors based on different structures of 2D b‐P that have been researched in recent years. Finally, the challenges that may be faced by black phosphorus‐based infrared photoelectric detectors are briefly introduced, and the potential application directions are discussed from the perspective of large‐scale production and practical application. This article provides an in‐depth analysis and evaluation of the future development prospects of 2D b‐P materials as a potential excellent candidate of infrared photodetectors. [ABSTRACT FROM AUTHOR]

This work presents a comprehensive investigation into the synthesis, characterization, and thermal stability of SiGeSn nanowires (NWs) leveraging the vapor–liquid–solid growth mechanism. Utilizing plasma‐enhanced chemical vapor deposition with Sn as the catalyst and a combination of SiH4 and GeH4 as precursors, this research synthesizes tapered SiGeSn NWs of high crystalline quality. Utilizing high‐angle annular dark‐field scanning transmission electron microscopy and energy‐dispersive X‐ray spectroscopy, the study confirms the inhomogeneous distribution of Si, Ge, and Sn along the NWs’ growth axis. It is observed that the concentrations of Si and Ge are significantly influenced by the NW diameter, a phenomenon attributed to the Gibbs–Thomson effect. A straightforward mathematical model is developed. This model examines the impact of the catalyst's shape and the presence of Sn on the NW surface on the internal Sn concentration and its variation along the NWs’ growth axis. Additionally, the study investigates how thermal annealing at temperatures of 300 and 600 °C induces compositional changes within the NWs. These changes are markedly influenced by the heterogeneous distribution of Si, Ge, and Sn elements, leading to varying levels of compositional alterations in different segments of the NWs postannealing at distinct temperatures. [ABSTRACT FROM AUTHOR]

Metabolic disorders, including obesity, dyslipidemia, diabetes, nonalcoholic fatty liver disease, and metabolic syndrome, are characterized by insulin resistance, abnormalities in circulating cholesterol and lipid profiles, and hypertension. The most common pathophysiologies of metabolic disorders are glucose/lipid metabolism dysregulation, insulin resistance, inflammatory response, and oxidative stress. Although several agents have been approved for the treatment of metabolic disorders, there is still a strong demand for more efficacious drugs with less side effects. Natural products have been critical sources of drug research and discovery for decades. However, the usefulness of bioactive natural products is often limited by incomplete understanding of their direct cellular targets. In this review, we highlight the current understanding of the established and emerging molecular mechanisms of metabolic disorders. We further summarize the therapeutic effects and underlying mechanisms of natural products on metabolic disorders, with highlights on their direct cellular targets, which are mainly implicated in the regulation of glucose/lipid metabolism, insulin resistance, metabolic inflammation, and oxidative stress. Finally, this review also covers the clinical studies of natural products in metabolic disorders. These progresses are expected to facilitate the application of these natural products and their derivatives in the development of novel drugs against metabolic disorders. [ABSTRACT FROM AUTHOR]

To address the ambiguity, uncertainty, and imprecision inherent in failure mode and effect analysis (FMEA) assessments, we propose a probabilistic interval-value term set (PIVTS) to express decision-makers' evaluations. This approach allows decision makers to express their quantitative assessments using interval-value terms with probabilities, providing a more comprehensive and expressive representation of people's preferences than exact numbers. In a probabilistic interval-valued environment, we propose an enhanced failure mode ranking method based on elimination and choice translating reality-III (ELECTRE III). Our ranking method includes a comprehensive computing framework for PIVTS to assess the importance of risk factors. Additionally, we provide rules for addition, subtraction, and division when comparing the disparity between two PIVTSs. Based on these rules, we also introduce the concordance index, discordance index, and outranking degrees. To demonstrate the effectiveness of our approach, we provide an example of its application to the steel production process. The advantages of PIVTS as a judgment technique and ELECTRE III as a decision method make our approach a better alternative for addressing complex uncertainties and determining the risk ranking of failure modes. [ABSTRACT FROM AUTHOR]

Up to today, there is no effective, specific and non-invasive evaluation method to assess the endometrial receptivity. This study aimed to establish a non-invasive and effective model with the clinical indicators to evaluate endometrial receptivity. Ultrasound elastography can reflect the overall state of the endometrium. Ultrasonic elastography images from 78 hormonally prepared frozen embryo transfer (FET) patients were assessed in this study. Meanwhile, the clinical indicators reflecting endometrium in the transplantation cycle were collected. The patients were received to transfer only one high-quality blastocyst. A novel code rule that can generate a large number of 0–1 symbols was designed to collect data on different factors. At the same time, a logistic regression model of the machine learning process with an automatic combination of factors was designed for analysis. The logistic regression model was based on age, body mass index, waist-hip ratio, endometrial thickness, perfusion index (PI), resistance index (RI), elastic grade, elastic ratio cutoff value, serum estradiol level and 9 other indicators. The accuracy rate of predicting pregnancy outcome of the logistic regression model was 76.92%. Elastic ultrasound can reflect the endometrial receptivity of patients in FET cycles. We established a prediction model including ultrasound elastography and the model precisely predicted the pregnancy outcome. The predictive accuracy of endometrial receptivity by the predictive model is significantly higher than that of the single clinical indicator. The prediction model by integrating the clinical indicators to evaluate endometrial receptivity may be a non-invasive and worthwhile method for evaluating endometrial receptivity. [ABSTRACT FROM AUTHOR]