硫化物中微量金的X射线能谱分析

介绍了一种用加热去硫来制备硫化物能谱试样的方法;将黄铁矿在400-500℃下加热3-10min制备成X射线能谱试样,可以保持试样的原始结构,提高硫化物中包体金的X射线能谱峰背比,了解矿物之间的共生关系;并对黄铁矿中微量的包体金作了X射线能谱半定量分析;该法简便、有效。     

基于蒙特卡洛方法的X射线探测器能谱响应研究

为解决X射线荧光测量过程中的仪器刻度和校正难题,通过理论研究并运用蒙特卡洛方法对X射线荧光仪进行建模,得到了X射线探测器能谱响曲线。结果表明:模拟的X射线探测器能谱响应全谱与实测情况基本一致,而主成份谱线则高度一致,特征峰半高宽相对误差均在5%以内。方法为X射线荧光测量的仪器刻度和校正提供重要的研究信息,不仅为元素分析工作节省大量人力物力,也为无标样X射线荧光分析提供了有力的理论支撑。方法也可用于X射线荧光分析过程中的其他研究领域。     

软X射线光谱的第一性原理模拟(英文)

软X射线光谱是通过核激发或去激发以探测分子、表面及各种化合物的电子结构和化学结构的有效的测量技术。本文对基于密度泛函理论描述X射线吸收、发射的各种不同过程的计算方法进行了综述。重点讨论了各种方法的基本原理、实际操作和具体应用。提供了K边X射线光电子能谱、吸收和发射光谱详细的模拟细节以及一些代表性体系的算例(包括分子、富勒烯、碳纳米管、单层石墨和DNA链)。     

电子能谱在金属腐蚀与防腐蚀中的应用

电子能谱法,是用单色光源或电子束等去照射样品,使其原子或电子受激而发射出来,测量这些电子的能量分布,从而获得所需的信息。因此,根据激发样品的能源不同,就可分为:X射线光电子能谱(XPS)、紫外光电子能谱(UPS)以及俄歇电子能谱(AES)。这是一门新近发展起来的技术。过去,在金属腐蚀与防腐蚀领域中的研究方法,大都是采用模拟     

乙烯-四氟乙烯共聚物的结构表征

利用傅里叶变换红外光谱(溴化钾压片法与衰减全反射法)、X射线衍射和X射线光电子能谱法对粉末状和薄板状两种形态的乙烯-四氟乙烯共聚物(ETFE)进行了结构表征。傅里叶变换红外光谱法分析结果表明:ETFE粉末有明显的-CH2和-CF2吸收峰;X射线衍射法分析结果表明:ETFE薄板的分辨率优于ETFE粉末;X射线光电子能谱法分析结果表明:ETFE粉末与ETFE薄板的-CH2和-CF2基团含量基本一致。X射线光电子能谱分析结果显示:ETFE薄板存在有机硅类脱膜剂的污染,同时ETFE粉末存在更多吸附氧。     

气态肼的三种电子能谱和量子化学研究

本文应用X射线光电子能谱技术测得了气态肼的内层电子能谱、Auger电子能谱和价带电子能谱,对其电子结构进行了系统研究。又采用了不同能量的X射线为激发源,对比了Mg Ka 和 Zr M:价带能谱,定性地获得了肼的分子轨道的组成。本文对肼电子能谱进行了量子化学研究,用从头计算法给出了分子轨道波函数的成分,并指认了能谱,实验结果和计算值之间的一致性很好。     

碲化银纳米晶的室温合成及表征

在室温下制备了碲化银纳米粒子,制备过程中选用了新型的还原剂多聚甲醛。所得产物用X射线光电子能谱、X射线粉末衍射和透射电镜进行了表征。     

室温合成Cu0.8Ag1.2S和Cu0.5Ag1.5Se纳米晶及X射线光电子能谱表征

X射线光电子能谱;银铜硫三元化合物;银铜硒三元化合物     

稀土三元催化剂对二氧化碳/环氧化合物共聚反应的催化机理研究

采用X射线光电子能谱(XPS)技术研究了二氧化碳与环氧丙烷共聚反应的稀土三元催化剂,探讨了中心金属的X射线光电子能谱与催化活性间的关系,指出稀土三元催化剂的活性中心在于配位活化后的锌氧键,稀土盐通过与烷氧基锌形成双金属桥键,改变了活性中心的电子云分布,从而提高了反应活性.     

可见光响应光催化剂Ag/AgCl@NH2-UiO-66的制备及其光催化性能

采用气相扩散-沉积-光还原法制备了可见光响应复合光催化剂Ag/AgCl@NH_2-UiO-66。通过X射线衍射(XRD)、电子扫描显微镜(SEM)、X射线能谱(EDS)、紫外可见漫反射光谱及X射线光电子能谱(XPS)等对催化剂的结构进行了表征。结果表明Ag/AgCl@NH_2-UiO-66具有优良的光催化活性,可见光条件下(λ≥420nm)对20mg·L~(-1)罗丹明的降解效率14min即可达到98%。     

小波变换方法的比较──红外光谱数据压缩

介绍了小波变换和多分辨分析的基本理论以及常用小波变换压缩数据的3种方法:(1)只保留模糊信号;(2)全部保留模糊信号及锐化信号中的较大值;(3)保留模糊信号及锐化信号中的较大值.将紧支集小波和正交三次B-样条小波压缩4-苯乙炔基-邻苯二甲酸酐的红外光谱数据进行了对比,计算表明正交三次B-样条小波变换方法效果较好,而在全部保留模糊信号及只保留锐化信号中数值较大的系数时,压缩比大而重建光谱数据与原始光谱数据间的均方差较小.     

Wavelet transforms in separation science for denoising and peak overlap detection

Wavelet transform is a versatile time‐frequency analysis technique, which allows localization of useful signals in time or space and separates them from noise. The detector output from any analytical instrument is mathematically equivalent to a digital image. Signals obtained in chemical separations that vary in time (e.g., high‐performance liquid chromatography) or space (e.g., planar chromatography) are amenable to wavelet analysis. This article gives an overview of wavelet analysis, and graphically explains all the relevant concepts. Continuous wavelet transform and discrete wavelet transform concepts are pictorially explained along with their chromatographic applications. An example is shown for qualitative peak overlap detection in a noisy chromatogram using continuous wavelet transform. The concept of signal decomposition, denoising, and then signal reconstruction is graphically discussed for discrete wavelet transform. All the digital filters in chromatographic instruments used today potentially broaden and distort narrow peaks. Finally, a low signal‐to‐noise ratio chromatogram is denoised using the procedure. Significant gains (>tenfold) in signal‐to‐noise ratio are shown with wavelet analysis. Peaks that were not initially visible were recovered with good accuracy. Since discrete wavelet transform denoising analysis applies to any detector used in separation science, researchers should strongly consider using wavelets for their research.     

Wavelet transform applications in analytical chemistry

The wavelet transform has been established with the Fourier transform as a data-processing method in analytical chemistry. The main fields of application in analytical chemistry are related to denoising, compression, variable reduction, and signal suppression. Analytical applications were selected showing prospects and limitations of the wavelet transform. An important aspect consists in showing the advantage of wavelet transform over Fourier transform with respect to dual localization of a signal in both the original and the transformed domain enabling principal new application fields in comparison with Fourier transform.     

Development of wavelet transform voltammetric analyzer

An on-line wavelet transform algorithm and development of voltammetric analyzer with the on-line wavelet transform (WT-voltammetric analyzer) are described. Because the on-line wavelet transform decomposes the sampled signal simultaneously with the progress of sampling, the WT-voltammetric analyzer gives all the components contained in the sampled voltammogram. Applications of the WT-voltammetric analyzer in linear sweep voltammetric analysis of mixtures of Pb(II) and Tl(I) and in square wave voltammetric analysis of mixture of Cd(II) and In(III) were investigated. Results showed that the overlapping peaks of Pb(II) and Tl(I) can be separated easily, and the peak position after the on-line wavelet transform does not change. The linearity of the calibration curves for Cd(II) and In(III) in the overlapping square wave voltammetric curves were kept after the on-line wavelet transform. Quantitative determination of Cd(II) and In(III) in mixture samples were investigated. The recoveries are between 92.5 and 107.1%.     

Ensemble wavelet modelling for determination of wheat and gasoline properties by near and middle infrared spectroscopy

The wavelet transform has been shown to be a useful tool for multivariate calibration. However, the choice of wavelet transform settings (wavelet family, length and number of decomposition levels) for a given application is still an open problem. The present paper proposes an alternative approach, which consists of generating an ensemble model by combining individual models obtained with different wavelet transform settings. The advantages of the proposed method are demonstrated in two analytical problems, namely the determination of moisture and protein in wheat by near infrared spectroscopy and the determination of specific mass and three distillation temperatures (T10, T50, T90) in gasoline by middle infrared spectroscopy. In these problems, the results varied considerably among individual models, which underlines the risk associated to an inadequate choice of wavelet transform settings. In contrast, the ensemble model always provided adequate results in terms of prediction error and noise sensitivity. The proposed method can be seen as an advantageous alternative for multivariate calibration in the wavelet domain, as it frees the analyst from the need to choose a particular configuration for the wavelet transform.     

The Application of Complex Wavelet Transform to Spectral Signals Background Deduction

The accuracy of spectrograms may be affected by baseline excursion or drift when infrared spectrometers are used in the analyses of gases. Background deduction or baseline correction is one of the effective pretreatment methods that can improve measurement accuracy. This paper presents a novel methodology based on complex wavelet transform algorithm to perform background deduction. The complex wavelet transform methodology establishes a complex wavelet filter to decompose the spectral signals first, and set the decomposition coefficients in the high-frequency section to zero, and then reconstruct the background signals; finally, the background deduction can be realized by deducting the background signals. In this study, the complex wavelet established by Daubechies was selected to demonstrate background deduction aiming at simulative spectral signals with different backgrounds and the real spectral signal of SF6 decomposition gases. Compared with the results done by the real wavelet transform in the same conditions, the results indicate that complex wavelet transform methodology can perform background deduction more efficiently than real wavelet transform methodology, thus improving the effectiveness and precision of spectrogram measurements greatly, which is useful for SF6 gas decomposition compositions analysis     

The Application of Complex Wavelet Transform to Spectral Signals Background Deduction

The accuracy of spectrograms may be affected by baseline excursion or drift when infrared spectrometers are used in the analyses of gases. Background deduction or baseline correction is one of the effective pretreatment methods that can improve measurement accuracy. This paper presents a novel methodology based on complex wavelet transform algorithm to perform background deduction. The complex wavelet transform methodology establishes a complex wavelet filter to decompose the spectral signals first, and set the decomposition coefficients in the high-frequency section to zero, and then reconstruct the background signals; finally, the background deduction can be realized by deducting the background signals. In this study, the complex wavelet established by Daubechies was selected to demonstrate background deduction aiming at simulative spectral signals with different backgrounds and the real spectral signal of SF6 decomposition gases. Compared with the results done by the real wavelet transform in the same conditions, the results indicate that complex wavelet transform methodology can perform background deduction more efficiently than real wavelet transform methodology, thus improving the effectiveness and precision of spectrogram measurements greatly, which is useful for SF6 gas decomposition compositions analysis

     

A novel method to calculate the approximate derivative photoacoustic spectrum using continuous wavelet transform

A novel method based on continuous wavelet transform (CWT) using Haar wavelet function for approximate derivative calculation of analytical signals is proposed and successfully used in processing the photoacoustic signal. An approximate nth derivative of an analytical signal can be obtained by applying n times of the wavelet transform to the signal. The results obtained from four other different methods--the conventional numerical differentiation, the Fourier transform method, the Savitzky-Golay method, and the discrete wavelet transform (DWT) method--were compared with the proposed CWT method; it was demonstrated that all the results are almost the same for signals without noise, but the proposed CWT method is superior to the former four methods for noisy signals. The approximate first and second derivative of the photoacoustic spectrum of Pr(Gly)3Cl3.3H2O and PrCl3.6H2O were obtained using the proposed CWT method; the results are satisfactory.     

能量色散X射线荧光光谱背景扣除方法的探讨

能量色散X射线荧光分析使得特征X射线的全能峰叠加在背景之上,针对能量色散X射线荧光谱线背景扣除算法的研究,简述了剥峰法和小波变换法扣除背景的原理,并将这两种方法用于对实际谱线的处理,取得了良好的效果.同时对两种方法的算法和处理效果进行了对比研究,表明剥峰法相对简单,而小波变换法需要考虑小波基和分解层次等的影响,但是对整个谱线的综合处理效果相对要好一些.     

在线小波变换用于伏安分析仪的研制

将在线小波变换应用于伏安信号的在线处理,研制了具有小波变换功能的伏安分析仪。该系统利用在线小波变换对采集到所数据进行实时处理,具有控制灵活,分辨率高,数据处理方便等特点。通过对低浓度Ｃｙ＾２＋和Ｆｅ＾３＋混合样品的阶梯斜坡扫描伏安珠滤噪及定性,定量分析,结果表明可获得满意的分析结果。