过渡金属Mn（Ⅲ）,Fe（Ⅲ）,Rh（Ⅱ）和Pd（Ⅱ）卟啉配合物的共振拉…

讨论了金属离子Ｍｎ（Ⅲ），Ｆｅ（Ⅲ），Ｒｈ（Ⅲ）和Ｐｄ（Ⅱ）原卟啉Ⅸ－二甲酯（ＰＰ）和四苯基卟啉（ＴＰＰ）配合物及Ｐｄ（Ⅱ）间氯四苯基卟啉（ＴＰＰｍ－ｃｌ）配合物的共振拉曼光谱。利用金属离子外层ｄ电子与卟啉环的作用，解释了配合物结构灵敏带的变化规律，同时讨论了不同卟啉及卟啉环上取代基团对结构灵敏带的影响。     

六亚甲基桥连的希夫碱及其锰（Ⅲ）络合物催化剂的红外光谱研究

研究了六亚甲基桥连的希夫碱及其锰（Ⅲ）络合物催化剂的红外光谱，对一些主要谱带进行了经验归属。配体的分子内氢键谱带出现在～３４３８ｃｍ－１（以ＬＨ为例），νＣ＝Ｎ谱带在～１６３０ｃｍ－１（以ＣＨ为例，下同），苯的特征谱带在～１５９８，～１４６９和～７５５ｃｍ－１，νＭｎ—Ｎ为主的复合振动在～３６９ｃｍ－１，δＭｎ—Ｎ为主的复合振动在～２４７ｃｍ－１，νＭｎ—Ｏ为主的复合振动在～３２９ｃｍ－１，δＭｎ—Ｏ为主的复合振动在～２７５ｃｍ－１，νＭｎ—Ｃｌ特征谱带在～３０３ｃｍ－１。与苯基锰（Ⅲ）卟啉络合物相比，νＭｎ—Ｎ特征谱带向高波数位移，νＭｎ—Ｃｌ特征谱带则向低波数有较大位移。     

糠醛水杨酰腙锌（Ⅱ）和铅（Ⅱ）配合物的合成与光谱特性

由糠醛与水杨酰肼反应合成糠醛水杨酰腙席夫碱,然后再将其分别与过渡金属锌（Ⅱ）和铅（Ⅱ）作用合成两种新型的酰腙席夫碱配合物,并通过元素分析、紫外光谱、红外光谱等方法对所制备配合物进行表征。     

苹果酸合锰(Ⅱ)的合成及光谱表征

本文合成了苹果酸合锰（Ⅱ），并用AAS法对中心原子锰进行了测定，回收率为97．6％，相对标准偏差为0．98％，并用紫外，红外光谱法对其进行了表征。     

Eu2（DBM）6 4,4‘—BIPY的合成,光谱及铕（Ⅲ）格位

合成了配合物Ｅｕ２（ＤＢＭ）６４，６｀－ＢＩＰＹ，ＩＲ和Ｒａｍａｎ光谱都证实了配合物的生成，７７Ｋ正派激发高分辨激光激发光谱和荧光光谱表明配合物存在＾５Ｄ０能级能量差别为１５ｃｍ＾－１的两种Ｅｕ（Ⅲ）格位，两种Ｅｕ（Ⅲ）离子均不位于对称中心，由＾５Ｄ０→＾７Ｄ０．１，２跃迁荧光光谱峰数判断，两种Ｅｕ（Ⅲ）格位的区域对称性均为Ｃ１或Ｃ２或Ｃ，两种格位Ｅｕ（Ⅲ）离子的发光寿命分别为０．３７ｍｓ和０．３     

化学发光分析法测定锰（Ⅱ）与锰（Ⅶ）

基于MnO_4~-对碱性鲁米诺溶液的氧化发光作用,建立了测定Mn~(2+)和MnO_4~-的化学发光法。方法具有较高的灵敏度,线性范围达3个数量级,且选择性好,操作程序简便。已将该法应用于实际水样中锰(Ⅱ)和锰(Ⅶ)的测定,结果满意。     

甲硝唑与钴（Ⅱ）、镍（Ⅱ）配合物的合成及与DNA作用

甲硝唑与过渡金属钴（Ⅱ）、镍（Ⅱ）的卤盐反应得到配合物[Co（C6H9O3N3）2Br2]（1）和[Ni（C6H9O3N3）2Cl2]·2H2O（2）,通过元素分析、红外光谱分析及热重分析对配合物可能的组成进行了表征.并进一步对两种配合物与DNA的相互作用进行研究,光谱分析及黏度分析表明两者均以部分插入的方式与DNA的相互作用.     

Eu_2（DBM）_64，4＇－BIPY的合成、光谱及铕（Ⅲ）格位

合成了配合物Ｅｕ２（ＤＢＭ）６４，４＇－ＢＩＰＹ．ＩＲ和Ｒａｍａｎ光谱都证实了配合物的生成．７７Ｋ下激光激发高分辨激发光谱和离光光谱表明配合物存在５Ｄ０能级能量差别为１５ｃｍ－１的两种Ｅｕ（Ⅲ）格位，两种Ｅｕ（Ⅲ）离子均不位于对称中心．由５Ｄ０→７Ｆ０、１、２跃迁荧光光谱峰数判断，两种Ｅｕ（Ⅲ）格位的区域对称性均为Ｃ１或Ｃ２或Ｃ２．两种格位Ｅｕ（Ⅲ）离子的发光寿命分别为０．３７ｍｓ和０．３２ｍｓ．     

一维链状席夫碱锰（Ⅱ）配合物的合成及结构表征

以1S,2S-环己二胺和乙酰丙酮进行缩合得到N,N′-双（乙酰丙酮）-1S,2S-环己二胺的Schiff碱配体L,再将配体L与MnCl2.4H2O进行配位反应,得到了配合物[MnL2Cl2.L]n（1）,并用元素分析和X射线单晶衍射进行了表征。结果表明,配合物1属于正交晶系,空间群是P212121,晶体参数：a=1.56630（15）nm,b=1.59373（16）nm,c=2.1963（2）nm,V=5.4827（9）nm3,Z=4,Dc=1.164g.cm-3,R1=0.0548,wR2=0.1368。在配合物1中,每个Mn（）的配位环境都是三角双锥。配合物中双齿配体通过其两臂乙酰丙酮亚胺单元的端基氧原子同2个金属离子配位桥联形成一维螺旋链结构。     

人工神经网络分类鉴别苦丁茶红外光谱

为了分类鉴别苦丁茶,采用竞争神经网络(CNN)和反向传播人工神经网络(BP网络)两种模式的人工神经网络(ANN)分别分析了各种苦丁茶的红外谱图。作者采用25个样本作训练集,11个样本作检验集,用两种网络进行了训练。结果表明,CNN网络和BP网络均能够有效地实现苦丁茶产地的鉴别,但CNN网络能够进一步地区分苦丁茶的级别。实验表明,CNN速度快,预测结果准确,可望用竞争神经网络(CNN)和红外光谱法结合分类鉴别苦丁茶。     

LM-BP神经网络应用于光度法同时测定邻苯二酚、间苯二酚和对苯二酚三组分

应用人工神经网络原理,以Levenberg-marquardt back propagation(LM-BP)算法,对光谱严重重叠的邻苯二酚、间苯二酚和对苯二酚3组分进行同时测定。挑选283.5, 279.5, 276.5 nm处3波长为3种苯二酚的测量波长,按正交设计表L₂₅(56)配置25组标准混合溶液,对3种苯二酚进行训练,平均平方误差(MSE)达到最小值0.083 114 3;同时预测了模拟混合样本中的邻苯二酚、间苯二酚和对苯二酚含量,3种苯二酚异构体在低浓度时误差稍大,绝大部分分析结果相对误差小于5%,特别是对吸收光谱严重重叠的邻苯二酚、间苯二酚有令人满意的分析结果。     

Luminescence spectra of Mn(II) in different symmetries

Luminescence emission and excitation spectra of high-spin Mn(II) were studied in crystals with different site symmetries of the divalent ion. For cubic or uniaxial site symmtries only one emission band is observed with a maximum asymmetry of 20% and larger width on the low energy side. Lower site symmetries may result in spectra of considerable complexity. A second emission band of lower intensity near 14 000 cm^-1 was found in systems with hexa- as well as tetra-coordinated Mn(II). It evidently originates from the same Mn(II) that gives rise to the stronger emissions at higher energies. A qualitative explanation for its occurence on the basis of close-lying split components of the ⁴T₁(G) or ⁴T₂(G) states is presented. In two systems emission bands from higher excited states and shifts of emission maxima with excitation energy were observed. The shifts are explained on the basis of overlapping bands and variable intensity ratios for these unresolved components. The results indicate that the degree of distortion is more important for the occurrence of these complications than the actual site symmetry of Mn(II).     

La（Ⅲ）,Gd（Ⅲ）,Tb（Ⅲ）8—羟基喹啉固体配合物的光声光谱与 …

本文合成Ｌａ（Ⅲ）、Ｇｄ（Ⅲ）、Ｔｂ（Ⅲ）８－羟基喹啉固体配合物，通过对其光声光谱的分析，研究了８－羟基喹啉稀土配合物的荧光性质和驰豫过程。     

反向传播人工神经网络分光光度法同时测定环境水样中的苯酚、间苯二酚和间氨基酚

本文应用人工神经网络原理 ,采用误差反向传播算法 ,对环境水样中的苯酚、间苯二酚和间氨基酚可以用分光光度法不经分离进行了同时测定。三种酚类的平均回收率分别为 98 0 % ,99 6 %和 99 7%。实验证明 ,反向传播 人工神经网络方法应用在本体系中进行结果校正 ,结果令人满意。     

Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization

Blasting is an inseparable part of the rock fragmentation process in hard rock mining. As an adverse and undesirable effect of blasting on surrounding areas, airblast-overpressure (AOp) is constantly considered by blast designers. AOp may impact the human and structures in adjacent to blasting area. Consequently, many attempts have been made to establish empirical correlations to predict and subsequently control the AOp. However, current correlations only investigate a few influential parameters, whereas there are many parameters in producing AOp. As a powerful function approximations, artificial neural networks (ANNs) can be utilized to simulate AOp. This paper presents a new approach based on hybrid ANN and particle swarm optimization (PSO) algorithm to predict AOp in quarry blasting. For this purpose, AOp and influential parameters were recorded from 62 blast operations in four granite quarry sites in Malaysia. Several models were trained and tested using collected data to determine the optimum model in which each model involved nine inputs, including the most influential parameters on AOp. In addition, two series of site factors were obtained using the power regression analyses. Findings show that presented PSO-based ANN model performs well in predicting the AOp. Hence, to compare the prediction performance of the PSO-based ANN model, the AOp was predicted using the current and proposed formulas. The training correlation coefficient equals to 0.94 suggests that the PSO-based ANN model outperforms the other predictive models.