配合物Ni(CSH)_2(EtOH)_2的合成与晶体结构

合成了一个新的含有肉桂醛缩水杨酰腙(CSH)的单核镍配合物Ni(CSH)2(EtOH)2。配合物晶体属于正交晶系,空间群为Pbca,a=18.587(7),b=7.914(3),c=23.395(9),V=3441(2)3,Z=4,F(000)=1432,GOF=1.074,R1=0.0621。配合物分子呈中心对称,中心镍(Ⅱ)形成扭魡曲的八面体构型。     

[Cu（DPA）μ-NO3]n·3nH2O（DPA=2,2＇-联吡啶胺）的晶体结构和非等温热分解动力学的研究

本文对配合物[Cu(DPA)μ-NO3]n·3nH2O(1)(DPA=2,2'-联吡啶胺)合成、结构和光谱性能进行了描述.(1)属于单斜晶系,P21/c空间群.中心金属离子CuII处于扭曲的四方锥的配位环境中:一个DPA上的两个N原子和一个NO3-的两个O原子位于四方锥底部平面,轴向位置由另一NO3-的O原子占据.每一个NO3-的两个氧原子与CuII鳌合,且另一氧原子与相邻的CuII配位形成一维Z型链结构.链与链之间靠分子间的相互作用力在bc面形成二维结构.此外,我们运用TG-DTG热分析技术结合红外光谱对其热分解行为和热稳定性进行了探讨,并对其热分解反应进行了非等温动力学计算,得到了相应的动力学参数.     

一维线性谐振子热力学性质

根据玻耳兹曼公式,导出了经典力学和量子力学中一维线性谐振子的热力学函数和平均能量,并对这两种情况下得到的固体比热进行了比较。     

一维配位聚合物[Cu（tda）（phen）（H2O）]n的水热合成及晶体结构

采用水热法合成了一维配位聚合物[cu（tda）（phen）（H2O）]N（H2tda=噻吩-2,5-二甲酸,phen=1,10-邻菲哆啉）．对其进行了元素分析、红外光谱表征和X射线单晶衍射测定．该配合物属于单斜晶系,空间群P21／c,晶胞参数a=10．4938（15）,b=16．893（2）,c=10．4938（15）A,β=108．69°,V=1762．2（4）λ^3,C18H12CuN205S,Mr=431．90,Dc=1．628g／cm^3,μ（MoKa）=1．391mm^-1,F（000）=876,Z=4,R1=0．0307,wR2=0．0766．     

醇胺桥连钴(Ⅱ)簇二维配合物合成和晶体结构

报道利用N-甲基二乙醇胺(N-methyldiethanolamine简写为:MedeaH2)分别和1,4-萘二酸(1,4-naphthalenedicarboxylic Acid,简写为:1,4-H2ndc)、噻吩-2,5-二羧酸(Thiophene-2,5-dicarboxylic Acid,简写为:H2tda)合成的两个钴配位聚合物,并通过红外光谱、热重分析、X-射线单晶衍射对其结构进行分析,得知两种配合物均为二维拓扑结构,但其孔道尺寸不同.     

配合物[Cd_2(bipy)_4(NO_3)_3(H_2O)]NO_3的水热合成与晶体结构

水热合成了配合物[Cd2(bipy)4(NO3)3(H2O)]NO3(bipy=2,2’-联吡啶),并用单晶X-射线衍射测定了其晶体结构.配合物晶体属正交晶系,Pbca空间群,a=1.4361(3)nm,b=1.7215(2)nm,c=3.5099(5)nm,Z=8,Dc=1.708g/cm3,R1=0.0352,ωR2=0.0504.配合物中每一个镉离子均由两个2,2’-联吡啶的四个氮原子和两个氧原子配位构成变形八面体结构.     

双正、双负和单负介质光子晶体能带的比较研究

利用传输矩阵法理论,对双正、双负和单负介质构成的对称结构光子晶体能带谱进行了对比研究,结果得到：由不同介质材料构成的对称结构光子晶体,其能带谱存在很大的差异,其中以单负介质光子晶体禁带宽度最宽,双负介质光子晶体次之,双正介质光子晶体的禁带宽度为最窄;当周期数变大时,光子晶体禁带中的透射峰带宽均逐渐变小并趋于尖锐,且以单负介质的光子晶体透射峰带宽减小的速度为最快;介质厚度dB对单负介质光子晶体的调制效果要优于对双正和双负光子晶体的调制效果;入射角对单负介质光子晶体能带谱的影响要大于对双正和双负介质光子晶体.不同介质材料构成的对称结构光子晶体的这些光传输特性,可为镜像对称结构光子晶体的设计以及窄带或是宽带光子晶体光学滤波器件等提供理论指导.     

Automated monitoring of online news accuracy with change classification models

In the past decade, news consumption has shifted from printed news media to online alternatives. Although these come with advantages, online news poses challenges as well. Notable here is the increased competition between online newspapers and other online news providers to attract readers. Hereby, speed is often favored over quality. As a consequence, the need for new tools to monitor online news accuracy has grown. In this work, a fundamentally new and automated procedure for the monitoring of online news accuracy is proposed. The approach relies on the fact that online news articles are often updated after initial publication, thereby also correcting errors. Automated observation of the changes being made to online articles and detection of the errors that are corrected may offer useful insights concerning news accuracy. The potential of the presented automated error correction detection model is illustrated by building supervised classification models for the detection of objective, subjective and linguistic errors in online news updates respectively. The models are built using a large news update data set being collected during two consecutive years for six different Flemish online newspapers. A subset of 21,129 changes is then annotated using a combination of automated and human annotation via an online annotation platform. Finally, manually crafted features and text embeddings obtained by four different language models (TF-IDF, word2vec, BERTje and SBERT) are fed to three supervised machine learning algorithms (logistic regression, support vector machines and decision trees) and performance of the obtained models is subsequently evaluated. Results indicate that small differences in performance exist between the different learning algorithms and language models. Using the best-performing models, $F_2$-scores of 0.45, 0.25 and 0.80 are obtained for the classification of objective, subjective and linguistic errors respectively.