CuxC60薄膜紫外-可见吸收光谱研究

近来,有关C60的研究主要集中在有关晶格动力学、电子结构和MxC60（M代表碱金属或碱土金属）的超导电性研究。但由于MxC60在大气中不能稳定存在,制约了MxC60的深入研究和实际应用。最近,Masterov等人报导了他们对Cu/C60的超导特性研究,认为其转变温度Tc在80－120K之间,这个转变温度比现有的MxC60的转变温度Tc-40K）要高得多,但有关更进一步的研究未见报导,因此,我们拟对CuxC60体系作较为详尽的研究,这对于进一步研究其超导机理是有必要的,本工作是在成功地制备了CuxC60薄膜的基础上,对其紫外－可见吸收光谱进行了初步的研究。定性地分析了CuxC60薄膜的电子结构,利用XPS谱分析了CuxC60薄膜中Cu的化学价态。     

Cu_xC_(60)薄膜紫外-可见吸收光谱研究

近来,有关 C60的研究主要集中在有关晶格动力学 [1]、电子结构 [2～ 4]和 MxC60（ M代表碱金属或碱土金属）的超导电性研究 [5].但由于 MxC60在大气中不能稳定存在,制约了 MxC60的深入研究和实际应用 .最近, Masterov等人报导了他们对 Cu/C60的超导特性研究 [6～ 7],认为其转变温度 Tc在 80～ 120 K之间,这个转变温度比现有的 MxC60的转变温度（ Tc～ 40 K）要高得多 .但有关更进一步的研究未见报导 .因此,我们拟对 CuxC60体系作较为详尽的研究,这对于进一步研究其超导机理是有必要的 .本工作是在成功地制备了 CuxC60薄膜的…     

用紫外-可见吸收光谱研究微过氧化物酶-8与La3+相互作用机理

首次用紫外-可见(UV-Vis)吸收光谱技术研究了微过氧化物酶-8(MP-8)与La³⁺的相互作用的机理。发现La³⁺优先与MP-8分子中血红素卟啉环上的两个丙酸基团的羧基氧发生键合作用,使血红素卟啉环的非平面性增加,π-π^*跃迁所需要的能量减少,但几率降低。La³⁺与MP-8肽链上的羰基氧发生弱相互作用,因此对卟啉环的结构基本上没有影响。     

用紫外-可见吸收光谱研究微过氧化物酶-11与Yb3+相互作用机理

用紫外-可见吸收光谱研究微过氧化物酶-11与Yb3+相互作用机理;微过氧化物酶-11;紫外-可见吸收光谱;Yb3+     

紫外-可见吸收光谱结合高斯多峰拟合技术测定甲基红酸离解常数

在一定pH值范围内,甲基红(MR)水溶液紫外-可见吸收光谱特征是酸式甲基红(HMR)最大吸收峰((530±15)nm)与碱式甲基红(MR-)最大吸收峰((435±20)nm)叠合在一起.本文用高斯多峰拟合技术实现了HMR和MR-叠合峰的分峰拟合计算.拟合计算输出两个吸收峰的积分面积即峰强度A1和A2,A1和A2之比与MR-和HMR浓度之比.进而计算甲基红水溶液酸离解平衡常数pKa.用本方法测量298.15K时的pKa值为4.76.拟合优度高,拟合度R2、拟合优度χ2分别达到0.998和10-5以下.深入探讨了表面活性剂十二烷基硫酸钠(SDS)、十六烷基三甲基溴化铵(CTAB)聚集行为对甲基红pKa的影响.与传统分光光度测量方法相比,紫外-可见吸收光谱结合高斯多峰拟合技术结果更可靠,测量步骤和数据处理过程更简单,更具有普适性.     

原位紫外-可见光谱和电化学方法研究苯胺聚合过程中的降解

用电化学及光谱电化学研究了硫酸溶液中苯胺在玻碳电极上的聚合降解过程 ,在 - 0 2～ 1 0V电位范围 ,循环伏安图显示了四对氧化还原峰 .通过比较聚苯胺 (PAn)膜在硫酸空白溶液和含对氨基苯酚 (PAP)的硫酸溶液中的循环伏安曲线及电化学聚合时溶液的紫外 可见吸收光谱 ,证明了PAn膜的降解产物PAP的存在 ,同时原位现场紫外 可见吸收光谱法初步研究了苯胺聚合降解的动力学过程 .     

计算化学在紫外可见吸收光谱教学中的应用

以L-酪氨酸为案例,结合计算化学手段,设计了采集紫外可见吸收光谱的教学方案,并探讨了计算化学在紫外可见吸收光谱教学中的具体应用。通过对该分子紫外可见光谱的模拟与指认,以及利用多种方法对其不同电子激发过程开展分析,不仅可以有效解释实验光谱吸收峰的来源,还能获取一系列具有教学意义的等值面图形与数据,来帮助学生加强对轨道跃迁类型、电子转移与空穴-电子分布等电子激发特性方面的感性认识,从而使得教学内容既有趣生动,又富有理论深度。     

β-环糊精交联树脂-紫外可见吸收光谱分析对硝基酚

以合成的β-环糊精交联树脂为吸附剂,紫外可见光谱分析微量对硝基酚.在碱性条件下,考察了对硝基酚在β-环糊精交联树脂上的吸附行为.在0.02 mol/L NaOH 介质中,室温下吸附30 min,树脂能有效分离富集对硝基酚,以甲醇-水(1:1, V/V)溶液为洗脱液,树脂能重复利用.此方法线性范围是0.5～90 mg/L,检出限为0.15 mg/L,测定结果令人满意.     

茜素与AcO~-阴离子的超分子作用及紫外-可见吸收光谱的理论研究

采用密度泛函方法(DFT/B3LYP),在6-31+G水平上分别优化茜素以及AcO-阴离子复合物的几何构型.从几何结构参数、电荷布居和前线轨道能量等方面探讨了复合物形成过程中主体分子的构象变化,以及主客体间的超分子作用.用含时密度泛函方法(TD-B3LYP/6-31+G)分别计算了主体分子以及与阴离子形成复合物的紫外-可见吸收光谱.根据所得复合物的特征吸收峰波长红移情况,从理论上较好地解释了主体分子通过氢键与AcO-形成稳定阴离子复合物的实验事实.结果表明,乙腈溶剂中茜素对AcO-具有较强的超分子作用和选择性识别能力.     

手性氨基酸卟啉化合物的CD光谱及紫外-可见光谱研究

在合成的手性氨基酸卟啉化合物ThrTPPZn和SerTPPZn(Thr:苏氨酸,Ser:丝氨酸)中,氨基酸残基与卟啉单元相互作用使氨基酸残基的构象相对固定,卟啉化合物在Soret区产生分裂的CD光谱.而在LeuTPPZn中氨基酸残基与卟啉单元的相互作用很弱,卟啉化合物的CD光谱很弱.在室温下,手性氨基酸卟啉化合物对映体的紫外-可见吸收光谱相同,当温度降低时,ThrTPP,SerTPP及其锌配合物在240 nm处的吸收光谱随着温度的降低吸收值减小,在278 K时,L-SerTPP,L-ThrTPP,D-ThrTPPZn 和D-SerTPPZn在275 nm处有强、宽的吸收峰,这是由于在低温下分子的内能减小,氨基酸残基的旋转受到限制,分子内的相互作用增大,羰基与卟啉环之间的共轭增大所致.     

基于近红外光谱的人工神经网络研究STR基因座分型方法

以D16S539基因座的3种(9-9、9-11、11-11)基因型为例,设计引物扩增包含该多态性位点的1段DNA片段,获得了3种基因型建模样本各50个.基于近红外光谱(NIRS)结合误差反向传播人工神经网络(BPANN)建立了测定短串联重复序列(STR)基因型的判别模型,所建立的判别模型的校正均方根残差和预测集均方根误差分别为0.082 5、0.072 5,预测准确率均为100%.该方法不需任何前处理,只需一步PCR扩增和NIRS检测即可实现STR基因型判别,具有简单、快速、低成本等优点.     

应用人工神经网络鉴定高效液相色谱峰纯度

首次应用人工神经网络（ａｒｔｉｆｉｃｉａｌｎｅｕｒａｌｎｅｔｗｏｒｋ,简称ＡＮＮ）鉴定色谱峰纯度,根据判定指标,采用改良反向传播算法对系统进行描述和预报,结果正确,收敛较快,具有一定的理论和实用价值。     

主成分-人工神经网络在近红外光谱定量分析中的应用

近红外光谱的主成分由非线性迭代偏最小二乘法（ＮＩＰＡＬＳ）求出。主成分作标准化处理后,作为Ｂ－Ｐ神经网络的输入结点进行非线性迭代。该法的优点是,充分利用了全光谱的数据,得到消除噪声后的最佳主成分,能建立非线性模型,Ｂ－Ｐ神经网络迭代时间显著缩短。用该法对大麦中的淀粉含量进行了定量分析研究。结果为：校准和预测的相关系数分别为０．９８１和０．９５３,校准和预测的相对标准偏差分别为１．７０％和２．４８％。     

Classification of Chinese Herbal Medicine by Laser-Induced Breakdown Spectroscopy with Principal Component Analysis and Artificial Neural Network

Chinese herbal medicine has attracted increasing attention because of the unique and significant efficacy in various diseases. In this paper, three types of Chinese herbal medicine, the roots of Angelica pubescens, Codonopsis pilosula, and Ligusticum wallichii with different places of origin or parts, are analyzed and identified using laser-induced breakdown spectroscopy (LIBS) combined with principal component analysis (PCA) and artificial neural network (ANN). The study of the roots of A. pubescens was performed. The score matrix is obtained by principal component analysis, and the backpropagation artificial neural network (BP-ANN) model is established to identify the origin of the medicine based on LIBS spectroscopy of the roots of A. pubescens with three places of origin. The results show that the average classification accuracy is 99.89%, which exhibits better prediction of classification than linear discriminant analysis or support vector machine learning methods. To verify the effectiveness of PCA combined with the BP-ANN model, this method is used to identify the origin of C. pilosula. Meanwhile, the root and stem of L. wallichii are analyzed by the same method to distinguish the medicinal materials accurately. The recognition rate of C. pilosula is 95.83%, and that of L. wallichii is 99.85%. The results present that LIBS combined with PCA and BP-ANN is a useful tool for identification of Chinese herbal medicine and is expected to achieve automatic real-time, fast, and powerful measurements.     

Multi‐Channel Piezoelectric Quartz Crystal Sensor with Principal Component Analysis and Back‐Propagation Neural Network for Organic Pollutants from Petrochemical Plants

A multi‐channel piezoelectric quartz crystal gas sensor comprising arrays coated with various organic materials and a home‐made computer interface for data processing were prepared and employed to detect six kinds of common organic pollutants from petrochemical plants including benzene, styrene, chloroform, octane, hexene and hexyne. The principal component analysis (PCA) method was employed to select six kinds of appropriate coating materials for these organic pollutants from 22 adsorbents onto piezoelectric crystals. After performing a PCA assay, six representative coating materials, namely Polyisobutylene, Poly(dimethylsiloxane) (SE30), 4‐tert‐Butylcalix[6]arene, Cholesteryl chloroformate, C60‐Polyphenyl acetylene (C60‐PPA) and Ag(I)/cryptand‐2,2/Ethylene diamine/NH₃/Polyvinyl chloride were selected. Moreover, effects of coating load of adsorbents and concentration of pollutants were also investigated. Three kinds of recognition techniques including 2D PCA score map, radar plot and back‐propagation neural network (BPN) were employed for qualitative analysis of these organic pollutants, and a quantitative analysis method could be established by creating calibration curves for each organic pollutant. This homemade multi‐channel piezoelectric quartz crystal gas sensor showed a good detection limit of 0.068‐1.127 mg/L for these organic pollutants. The multi‐channel piezoelectric gas sensor exhibited good reproducibility with a relative standard deviation (RSD) of 1.1‐9.6%. Furthermore, this multi‐channel piezoelectric crystal detection system with BPN recognition technique was also utilized to successfully distinguish and identify each component of the mixture of organic gas samples. Multivariate linear regression (MLR) analysis was employed to quantitatively compute the concentration of species in the organic mixtures.     

Simultaneous Spectrophotometric Determination of Iron and Cobalt in Micellar Medium by Using a Principal Component Artificial Neural Network and Multivariate Calibration

A simple and reliable method for simultaneous spectrophotometric determination of iron(II) and cobalt(II) has been established. The method is based on complex formation with 1‐(2‐pyridylazo)‐2‐naphtol (PAN) in a micellar medium. Despite a spectral overlap, Fe²⁺ and Co²⁺ have been simultaneously determined with chemometric approaches involving principal component artificial neural network (PC‐ANN), principal component regression (PCR) and partial least squares (PLS). Various synthetic mixtures of iron and cobalt were assessed and the results obtained by the applications of these chemometric approaches were evaluated and compared. It was found that the PC‐ANN method afforded relatively better precision than that of PCR or PLS. The proposed method permits detection limits of 0.05 and 0.07 ng mL^?1 for Co and Fe, respectively. The influences of pH, ligand amount, solvent percentage and time on the absorbance were also investigated. The proposed method was also applied satisfactorily for the determination of Fe(II) and Co(II) in real and synthetic samples.     

人工神经网络用于近红外光谱测定柴油闪点

采用主成分－人工神经网络对不同留程柴油的近红外光谱进行校正,预测其闪点。采用监控集控制网络训练过程,以避免过训练。探讨了人工神经网络（ＡＮＮ）、直接线性连接人工神经网络（ＬＡＮＮ）的校正效果,并与局部权重回归（ＬＷＲ）、主成分回归（ＰＣＲ）及偏最小二乘（ＰＬＳ）等校正方法进行了比较,认为人工神经及直接线性关联的较好手段。     

神经网络方法在血管紧张素转换酶抑制剂定量构效关系建模中的应用

对20个ACEI化合物用量子化学方法进行结构优化并计算出10个参数,用9种不同隐含层节点数的BP神经网络研究了ACEI的定量构效关系,建立了节点为10/6/1的三层BP神经网络模型。结果表明:以量化理论计算所得参数可以构建合理的ACEI定量构效关系模型,神经网络模型M6的r2=0.995,S=0.050,6个验证集化合物的残差平方和为0.002,预测能力明显强于多元线形回归模型,亦优于同类文献报道,可作为ACEI研发领域中预测先导化合物活性的理论工具。     

Multi‐Channel Surface Acoustic Wave (SAW) Sensor Based on Artificial Back Propagation Neural (BPN) Network and Multivariate Linear Regression Analysis (MLR) for Organic Vapors

A six‐channel surface acoustic wave (SAW) detection system with a 315 MHz one‐port quartz resonator and a homemade computer interface for signal acquisition and data processing was developed to detect various organic vapors. The oscillating frequency of the SAW quartz crystal decreased due to the adsorption of organic molecules on the coating materials. Polyethylene glycol, 18 crown 6 (18C6), Cr³⁺/cryptand‐22, stearic acid, polyvinylpyrrolidene and triphenyl phosphine coated quartz crystals were used as sensors. An artificial back propagation neural (BPN) network was used to recognize various organic gases such as hexane, 1‐hexene, 1‐hexyne, 1‐propanol, propionaldehyde, propionic acid and 1‐propylamine. It showed not only the distinction of unity of organic vapors but also mixtures of gases. The learning rate and the hidden unit of a neural network system for BPN analysis were investigated. Furthermore, the concentrations of these organic vapors were computed with about 10% error by multivariate linear regression analysis (MLR). MLR analysis with a multichannel SAW sensor was applied to determine the concentration of each component in a mixture of 1‐hexene, 1‐hexyne and propionaldehyde.     

神经元网络用于PCDD定量构效关系的研究

研究了不同PCDD（全名Polychlorinateddioxin）同系物分子结构的表达及特征参数的选择,应用神经元网络方法对其分子结构与色谱保留值进行了关联。对49种PCDD同系物在DWS往上不同温度下保留时间进行了预测,结果95％以上的数据点相对误差小于10％,而80％以上的数据点相对误差小于5％。