Under solvothermal conditions,six new coordination polymers(CPs)[Mn(L)(phen)(H_(2)O)]_(n)(1),[Co(L)(phen)(H_(2)O)]_(n)(2),[Cu(L)(phen)(H_(2)O)]_(n)(3),[Zn_(2)(L)_(2)(phen)2(H_(2)O)]_(n)(4),[Zn(L)(phen)]_(n)(5),and[Cd(...Under solvothermal conditions,six new coordination polymers(CPs)[Mn(L)(phen)(H_(2)O)]_(n)(1),[Co(L)(phen)(H_(2)O)]_(n)(2),[Cu(L)(phen)(H_(2)O)]_(n)(3),[Zn_(2)(L)_(2)(phen)2(H_(2)O)]_(n)(4),[Zn(L)(phen)]_(n)(5),and[Cd(L)(phen)2]_(n)(6)were synthesized by reactions of dicarboxylate ligand 2,2'-(1,2-phenylenebis(methylene))bis(sulfanediyl)dinobutyric acid(H_(2)L)and 1,10-phenanthroline(phen)with the corresponding metal salts.Complexes 1-6 have been structurally characterized by single-crystal X-ray diffraction analyses,elemental analysis,IR,thermogravimetric analysis,and powder X-ray diffraction.The structures of 1-6 are 1D chains,which are further connected by hydrogen bonding interac-tions to form 3D supramolecular structures.Among them,1 and 2 are isomorphic with L2-of syn-conformation,while L2-shows anti-conformation in 3-6.In addition,the solid-state photoluminescence property of 4-6 was investigated.展开更多
文摘Under solvothermal conditions,six new coordination polymers(CPs)[Mn(L)(phen)(H_(2)O)]_(n)(1),[Co(L)(phen)(H_(2)O)]_(n)(2),[Cu(L)(phen)(H_(2)O)]_(n)(3),[Zn_(2)(L)_(2)(phen)2(H_(2)O)]_(n)(4),[Zn(L)(phen)]_(n)(5),and[Cd(L)(phen)2]_(n)(6)were synthesized by reactions of dicarboxylate ligand 2,2'-(1,2-phenylenebis(methylene))bis(sulfanediyl)dinobutyric acid(H_(2)L)and 1,10-phenanthroline(phen)with the corresponding metal salts.Complexes 1-6 have been structurally characterized by single-crystal X-ray diffraction analyses,elemental analysis,IR,thermogravimetric analysis,and powder X-ray diffraction.The structures of 1-6 are 1D chains,which are further connected by hydrogen bonding interac-tions to form 3D supramolecular structures.Among them,1 and 2 are isomorphic with L2-of syn-conformation,while L2-shows anti-conformation in 3-6.In addition,the solid-state photoluminescence property of 4-6 was investigated.
文摘星系的光谱包含其内部恒星的年龄和金属丰度等信息,从观测光谱数据中测量这些信息对于深入了解星系的形成和演化至关重要.LAMOST(Large Sky Area Multi-Object Fiber Spectroscopic Telescope)巡天发布了大量的星系光谱,这些高维光谱与它们的物理参数之间存在着高度的非线性关系.而深度学习适合于处理多维、海量的非线性数据,因此基于深度学习技术构建了一个8个卷积层+4个池化层+1个全连接层的卷积神经网络,对LAMOST Data Release 7(DR7)星系的年龄和金属丰度进行自动估计.实验结果表明,使用卷积神经网络通过星系光谱预测的星族参数与传统方法基本一致,误差在0.18dex以内,并且随着光谱信噪比的增大,预测误差越来越小.实验还对比了卷积神经网络与随机森林回归模型、深度神经网络的参数测量结果,结果表明卷积神经网络的结果优于其他两种回归模型.
