Photochemical Upcycling/Modification of Polystyrene-based Plastic Waste

The escalating accumulation of plastic waste in landfills and marine environments has become a pressing concern to society. Among all plastic-based waste, polystyrenes are widely utilized as a commodity plastic and present very low recyclability. To improve this scenario, photocatalysis has recently become one of the viable techniques which can be performed under mild conditions. In this concise review, we have highlighted recent advancements in the valorization of polystyrene-based plastic waste by mainly focusing on the selective functionalization of the C–H bonds. This strategy clearly holds strong promise for the sustainable and efficient conversion of polystyrene-based waste and contributes to the reduction of waste and resource conservation

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Photochemical upcycling/modification of polystyrene-based plastic waste

Abstract: The escalating accumulation of plastic waste in landfills and marine environments has become a pressing concern to society. Among all plastic-based waste, polystyrenes are widely utilized as a commodity plastic and present very low recyclability. To improve this scenario, photocatalysis has recently become one of the viable techniques which can be performed under mild conditions. In this concise review, we have highlighted recent advancements in the valorization of polystyrene-based plastic waste by mainly focusing on the selective functionalization of the C-H bonds. This strategy clearly holds strong promise for the sustainable and efficient conversion of polystyrene-based waste and contributes to the reduction of waste and resource conservation

Fully Deformable Convolutional Network for Ship Detection in Remote Sensing Imagery

In high spatial resolution remote sensing imagery (HRSI), ship detection plays a fundamental role in a wide variety of applications. Despite the remarkable progress made by many methods, ship detection remains challenging due to the dense distribution, the complex background, and the huge differences in scale and orientation of ships. To address the above problems, a novel, fully deformable convolutional network (FD-Net) is proposed for dense and multiple-scale ship detection in HRSI, which could effectively extract features at variable scales, orientations and aspect ratios by integrating deformable convolution into the entire network structure. In order to boost more accurate spatial and semantic information flow in the network, an enhanced feature pyramid network (EFPN) is designed based on deformable convolution constructing bottom-up feature maps. Additionally, in considering of the feature level imbalance in feature fusion, an adaptive balanced feature integrated (ABFI) module is connected after EFPN to model the scale-sensitive dependence among feature maps and highlight the valuable features. To further enhance the generalization ability of FD-Net, extra data augmentation and training methods are jointly designed for model training. Extensive experiments are conducted on two public remote sensing datasets, DIOR and DOTA, which then strongly prove the effectiveness of our method in remote sensing field

Dynamic changes in marker components during the stir-frying of Pharbitidis Semen, and network analysis of its potential effects on nephritis

Introduction: Pharbitidis Semen (PS) has been widely used in traditional Chinese medicine to treat several diseases such as nephritis. PS is usually stir-fried to enhance its therapeutic efficacy before use in clinical practice. However, the changes in phenolic acids during stir-frying and the mechanisms of their therapeutic effects on nephritis are still unclear.Methods: Here, we studied the processing-induced chemical changes and elucidated the mechanism of PS in the treatment of nephritis. We determined the levels of the 7 phenolic acids in raw PS (RPS) and stir-fried PS (SPS) using high-performance liquid chromatography, analyzed the dynamic compositional changes during stir-frying, and used network analysis and molecular docking to predict and verify compound targets and pathways corresponding to nephritis.Results: The dynamic changes in the 7 phenolic acids in PS during stir-frying are suggestive of a transesterification reaction. Pathway analysis revealed that the targets of nephritis were mainly enriched in the AGE-RAGE, hypoxia-inducible factor-1, interleukin-17, and tumor necrosis factor signaling pathways among others. Molecular docking results showed that the 7 phenolic acids had good binding ability with the key nephritic targets.Discussion: The potential pharmaceutical basis, targets, and mechanisms of PS in treating nephritis were explored. Our findings provide a scientific basis for the clinical use of PS in treating nephritis

An Intelligent Detection Method for Small and Weak Objects in Space

In the case of a boom in space resource development, space debris will increase dramatically and cause serious problems for the spacecraft in orbit. To address this problem, a novel context sensing-YOLOv5 (CS-YOLOv5) is proposed for small and weak space object detection, which could realize the extraction of local context information and the enhancement and fusion of spatial information. To enhance the expression ability of feature information and the identification ability of the network, we propose the cross-layer context fusion module (CCFM) through multiple branches in parallel to learn the context information of different scales. At the same time, to map the small-scale features sequentially to the features of the previous layer, we design the adaptive weighting module (AWM) to assist the CCFM in further enhancing the expression of features. Additionally, to solve the problem that the spatial information of small objects is easily lost, we designed the spatial information enhancement module (SIEM) to adaptively learn the weak spatial information of small objects that need to be protected. To further enhance the generalization ability of CS-YOLOv5, we propose a contrast mosaic data augmentation to enrich the diversity of the sample. Extensive experiments are conducted on self-built datasets, which strongly prove the effectiveness of our method in space object detection

Straightforward synthesis of functionalized \u3b3-Lactams using impure CO\u2082 stream as the carbon source

Abstract: Direct utilization of CO2 into organic synthesis finds enormous applications to synthesize pharmaceuticals and fine chemicals. However, pure CO2 gas is essential to achieve these transformations, and the purification of CO2 is highly cost and energy intensive. Considering this, we describe a straightforward synthetic route for the synthesis of gamma-lactams, a pivotal core structure of bioactive molecules, by using commercially available starting materials (alkenes and amines) and impure CO2 stream (exhaust gas is collected from the car) as the carbon source. This blueprint features a broad scope, excellent functional group compatibility and application to the late-stage transformation of existing pharmaceuticals and natural products to synthesize functionalized gamma-lactams. We believe that our strategy will provide direct access to gamma-lactams in a very sustainable way and will also enhance the Carbon Capture and Utilization (CCU) strategy

Straightforward synthesis of functionalized γ-Lactams using impure CO2 stream as the carbon source

Abstract Direct utilization of CO2 into organic synthesis finds enormous applications to synthesize pharmaceuticals and fine chemicals. However, pure CO2 gas is essential to achieve these transformations, and the purification of CO2 is highly cost and energy intensive. Considering this, we describe a straightforward synthetic route for the synthesis of γ-lactams, a pivotal core structure of bioactive molecules, by using commercially available starting materials (alkenes and amines) and impure CO2 stream (exhaust gas is collected from the car) as the carbon source. This blueprint features a broad scope, excellent functional group compatibility and application to the late-stage transformation of existing pharmaceuticals and natural products to synthesize functionalized γ-lactams. We believe that our strategy will provide direct access to γ-lactams in a very sustainable way and will also enhance the Carbon Capture and Utilization (CCU) strategy