A photocatalyst of nanometer TiO2/conjugated polymer complex was successfully synthesized and characterized by spectroscopic methods and photocatalytic experiments. The complex photocatalyst could be activated by abso...A photocatalyst of nanometer TiO2/conjugated polymer complex was successfully synthesized and characterized by spectroscopic methods and photocatalytic experiments. The complex photocatalyst could be activated by absorbing both ultraviolet and visible light （λ = 190-800 nm）. Methylene blue （MB） could be degraded more efficiently on the complex photocatalyst than on the TiO2 under natural light. The conjugated polymer played a promoting role in the photocatalytic degradation of MB. The calcination temperature had an important effect in degradation of dye and could be summarized as 260℃ 〉 300 ℃ 〉 340 ℃ 〉 220 ℃ 〉 180 ℃.展开更多
Natural products,with remarkable chemical diversity,have been extensively investigated for their anticancer potential for more than a half-century.The collective efforts of the community have achieved the tremendous a...Natural products,with remarkable chemical diversity,have been extensively investigated for their anticancer potential for more than a half-century.The collective efforts of the community have achieved the tremendous advancements,bringing natural products to clinical use and discovering new therapeutic opportunities,yet the challenges remain ahead.With remarkable changes in the landscape of cancer therapy and growing role of cutting-edge technologies,we may have come to a crossroads to revisit the strategies to understand nature products and to explore their therapeutic utility.This review summarizes the key advancements in nature product-centered cancer research and calls for the implementation of systematic approaches,new pharmacological models,and exploration of emerging directions to revitalize natural products search in cancer therapy.展开更多
Deep learning has been increasingly recognized as a promising tool in solving kinds of physical problems beyond powerful approximations. A multi-domain physics-informed neural network(mPINN) is proposed to solve the n...Deep learning has been increasingly recognized as a promising tool in solving kinds of physical problems beyond powerful approximations. A multi-domain physics-informed neural network(mPINN) is proposed to solve the non-uniform heat conduction and conjugate natural convection with the discontinuity of temperature gradient on the interface. Local radial basis function method(LRBF) is applied to compute the case without the analytical solution and is regarded as the benchmark solver.Each physical domain matches a private neural network and all neural networks are connected by the shared information of temperature and heat flux on the interface. Joint training and separate training are utilized to minimize the loss function, which usually consists of the residual of boundary conditions, interface conditions and governing equations. Joint training minimizes the sum of all losses from neural networks with one shared optimizer, while separate training owns its private optimizer. Local adaptive activation function(LAAF) is used to accelerate the convergence and acquire a lower loss value when compared with its fixed counterpart. The numerical experiments on three types of residual points, uniform, Gauss-Lobatto and random, are conducted and it can be concluded that the uniform residual points can obtain the most accurate solution than the random and Gauss-Lobatto. Joint training is more accurate than the separate training when the number of residual points is relatively small,while the separate training performs better than the joint training for the large number of residual points. Numerous test cases on multi-domain heat transfer and fluid flow show the accuracy of the proposed m PINN. Local and global heat transfer rates show good agreements with the results from LRBF. Excepting the forward problems, the thermal conductivity ratio, the constant source and the characteristic parameters of natural convection are accurately learned from sparsely distributed data points.展开更多
以PVC和ZnC l2为前驱物,利用一种简单、快速的方法制备了ZnO/共轭高分子(P)复合微粒,并通过XRD、IR和UV-V is等技术对其进行了表征。结果表明,其中的P为具有活性基团和不同长度共轭链的高分子,且与ZnO以Zn-O-C相键合;该复合材料的吸收...以PVC和ZnC l2为前驱物,利用一种简单、快速的方法制备了ZnO/共轭高分子(P)复合微粒,并通过XRD、IR和UV-V is等技术对其进行了表征。结果表明,其中的P为具有活性基团和不同长度共轭链的高分子,且与ZnO以Zn-O-C相键合;该复合材料的吸收拓宽至整个紫外-可见光区。前驱物(m(ZnC l2)∶m(PVC)=3∶1)经200℃热处理15 m in制得的ZnO/P复合微粒5 m in内可将亚甲基(MB)彻底脱色,并且该复合催化剂重复使用6次后对MB的脱色率依然可达90%以上。展开更多
文摘A photocatalyst of nanometer TiO2/conjugated polymer complex was successfully synthesized and characterized by spectroscopic methods and photocatalytic experiments. The complex photocatalyst could be activated by absorbing both ultraviolet and visible light （λ = 190-800 nm）. Methylene blue （MB） could be degraded more efficiently on the complex photocatalyst than on the TiO2 under natural light. The conjugated polymer played a promoting role in the photocatalytic degradation of MB. The calcination temperature had an important effect in degradation of dye and could be summarized as 260℃ 〉 300 ℃ 〉 340 ℃ 〉 220 ℃ 〉 180 ℃.
文摘Natural products,with remarkable chemical diversity,have been extensively investigated for their anticancer potential for more than a half-century.The collective efforts of the community have achieved the tremendous advancements,bringing natural products to clinical use and discovering new therapeutic opportunities,yet the challenges remain ahead.With remarkable changes in the landscape of cancer therapy and growing role of cutting-edge technologies,we may have come to a crossroads to revisit the strategies to understand nature products and to explore their therapeutic utility.This review summarizes the key advancements in nature product-centered cancer research and calls for the implementation of systematic approaches,new pharmacological models,and exploration of emerging directions to revitalize natural products search in cancer therapy.
基金supported by the National Natural Science Foundation of China (Grant Nos. 12102331 and 52130603)
文摘Deep learning has been increasingly recognized as a promising tool in solving kinds of physical problems beyond powerful approximations. A multi-domain physics-informed neural network(mPINN) is proposed to solve the non-uniform heat conduction and conjugate natural convection with the discontinuity of temperature gradient on the interface. Local radial basis function method(LRBF) is applied to compute the case without the analytical solution and is regarded as the benchmark solver.Each physical domain matches a private neural network and all neural networks are connected by the shared information of temperature and heat flux on the interface. Joint training and separate training are utilized to minimize the loss function, which usually consists of the residual of boundary conditions, interface conditions and governing equations. Joint training minimizes the sum of all losses from neural networks with one shared optimizer, while separate training owns its private optimizer. Local adaptive activation function(LAAF) is used to accelerate the convergence and acquire a lower loss value when compared with its fixed counterpart. The numerical experiments on three types of residual points, uniform, Gauss-Lobatto and random, are conducted and it can be concluded that the uniform residual points can obtain the most accurate solution than the random and Gauss-Lobatto. Joint training is more accurate than the separate training when the number of residual points is relatively small,while the separate training performs better than the joint training for the large number of residual points. Numerous test cases on multi-domain heat transfer and fluid flow show the accuracy of the proposed m PINN. Local and global heat transfer rates show good agreements with the results from LRBF. Excepting the forward problems, the thermal conductivity ratio, the constant source and the characteristic parameters of natural convection are accurately learned from sparsely distributed data points.
文摘以PVC和ZnC l2为前驱物,利用一种简单、快速的方法制备了ZnO/共轭高分子(P)复合微粒,并通过XRD、IR和UV-V is等技术对其进行了表征。结果表明,其中的P为具有活性基团和不同长度共轭链的高分子,且与ZnO以Zn-O-C相键合;该复合材料的吸收拓宽至整个紫外-可见光区。前驱物(m(ZnC l2)∶m(PVC)=3∶1)经200℃热处理15 m in制得的ZnO/P复合微粒5 m in内可将亚甲基(MB)彻底脱色,并且该复合催化剂重复使用6次后对MB的脱色率依然可达90%以上。
