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The key skeleton that could allow to access structurally intriguing mohangic acids A−E has been synthesized in stereoselective manner. The salient features of this synthesis include Evans syn‐aldol reaction to construct the C‐11 and C‐12 centers, HWE olefination to construct C14−C15 bond, Evans asymmetric methylation to install C‐14 center and Crimmins acetate aldol to generate C‐15 center whereas Wittig and Takai olefinations to prepare the C8–C9 & C6–C7 alkenes, respectively, and Heck reaction to couple C1–C5 & C6–C17 segments of the molecules. The developed route is highly modular and useful for accessing diverse natural product like molecules for future investigations. [ABSTRACT FROM AUTHOR]

The prevalence and mobility of smartphones make these a widely used tool for environmental health research. However, their potential for determining aggregated air quality index (AQI) based on PM2.5 concentration in specific locations remains largely unexplored in the existing literature. In this paper, we thoroughly examine the challenges associated with predicting location-specific PM2.5 concentration using images taken with smartphone cameras. The focus of our study is on Dhaka, the capital of Bangladesh, due to its significant air pollution levels and the large population exposed to it. Our research involves the development of a Deep Convolutional Neural Network (DCNN), which we train using over a thousand outdoor images taken and annotated. These photos are captured at various locations in Dhaka, and their labels are based on PM2.5 concentration data obtained from the local US consulate, calculated using the NowCast algorithm. Through supervised learning, our model establishes a correlation index during training, enhancing its ability to function as a Picture-based Predictor of PM2.5 Concentration (PPPC). This enables the algorithm to calculate an equivalent daily averaged AQI index from a smartphone image. Unlike, popular overly parameterized models, our model shows resource efficiency since it uses fewer parameters. Furthermore, test results indicate that our model outperforms popular models like ViT and INN, as well as popular CNN-based models such as VGG19, ResNet50, and MobileNetV2, in predicting location-specific PM2.5 concentration. Our dataset is the first publicly available collection that includes atmospheric images and corresponding PM2.5 measurements from Dhaka. Our codes and dataset are available at https://github.com/lepotatoguy/aqi. [ABSTRACT FROM AUTHOR]

In this work, mesoporous TiO2 nanoparticle photocatalyst has been prepared by hydrothermal method using titanium tetrachloride as precursor. The resulting photocatalysts have been characterized using X-ray diffraction (XRD), nitrogen adsorption-desorption analyses, RAMAN spectroscopy, Transmission Electron Microscopy (TEM) and UV-vis diffuse reflectance spectroscopy (UV-vis DRS). The results showed that the synthesized OiT2 was anatase phase and mesoporous with specific surface 133.45 m²/g. The decomposition of methylene blue dye under the influence of ultraviolet light was used to test the photocatalytic performance of the produced TiO2 photocatalyst. The photocatalytic activity of the TiO2 photocatalyst for the breakdown of methylene blue dye was significantly higher than that of the commercial TiO2 P25 used as a reference. Moreover, the prepared photocatalyst was stable and reusable. The larger surface area of hydrothermally produced TiO2 was the reason for this enhanced photocatalytic activity. As a result, it is expected that this approach of photocatalytically active semiconducting nanocatalysts will be used in industry to eliminate undesired organics from the environment. [ABSTRACT FROM AUTHOR]