Deconvolution of very low primary energy SIMS depth profiles

In this paper, the deconvolution of SIMS profiles analysed at very low primary energy (0.5 keV/O₂⁺) is addressed. The depth resolution function (DRF) of the SIMS analysis in presence of roughness is established and a deconvolution procedure is implemented without or in presence of roughness on samples containing delta-doped layers of boron in silicon. It is shown that the deconvolution procedure can lead to a great improvement of the full width at half maximum (FWHM) of the measured peaks in the case where no roughness in detected in the crater bottom. In the case where it is present, the conditions required to use a deconvolution procedure are discussed, and the deconvolution is implemented using precise and restrictive assumptions.     

Characterization of the behaviour of flavour microcapsules in cotton fabrics

Nanotechnology processes have recently been introduced into the textile field. One of these processes involves encapsulated nanoparticles or nanoproducts, known as microcapsules. They are available with a wide range of products which confer different properties related to the nature of the encapsulated product. The composition of the wall material determines the release of the product into the fabric and is the object of study in the present work. The behaviour of microencapsulated peppermint was analyzed on cotton fabrics.Different application methods have been tested, impregnation versus bath exhaustion. As impregnation turned out to be the most efficient, some recipes were assayed and the effect of microcapsules on maintenance conditions was evaluated in order to determine the most suitable method with the longest lasting effect on fabrics.The effect of microcapsules is usually measured by the presence of a property such as odour measurements when flavours are encapsulated. The aim of this work is to explain the behaviour of the cotton fabrics with microcapsules bound to their fibers, to determine the relationship between their macroscopic properties and microscopic characteristics. A procedure based in FTIR spectroscopy is also proposed to quantify the presence of microcapsules in the fabrics.IR spectra of the textiles were obtained to determine the presence of microcapsules, which was corroborated by SEM techniques.     

Synthesis of polysilanes bearing pendant carbosilyl groups and Si-NMR probe on the tacticity using side chain silicon atoms

New functional polysilanes [R₂R¹Si(CH₂)₂SiH]_n (R=Me, R¹=H (1); R=R¹=Et (2); R=Me, R¹=Ph (3)) bearing carbosilyl side chains have been synthesized by catalytic dehydropolymerization of precursor carbosilanes R₂R¹SiCH₂CH₂SiH₃ using Cp₂TiCl₂–BuLi as a catalyst. These polymers are characterized by ¹H, ¹³C, ²⁹Si, {¹H–²⁹Si} HSQC NMR spectroscopy, GPC and TGA. Attempts to delineate the tacticity from the analysis of deconvoluted ²⁹Si{¹H}-NMR signals associated with the side chain silicon atoms reveal that the triad concentration ratio follows a Bernoullian statistical model for polymers 1 and 2 only.     

Multivariate analysis of non-homogeneous peaks in liquid chromatography

Summary The effects of concentration, separation and spectral similarity as factors influencing the accuracy of iterative target testing factor analysis (ITT-FA) are investigated for three component systems by the application of analysis of variance (ANOVAR). ANOVAR is applied over a range of peak separations to map the changing effects of the three factors with increasing overlap. Two error responses were measured and analysed, (a) Relative cluster error (RCE) a measure of the error over all peaks in a cluster and (b) Relative peak error (RPE) the error of an individual peak. Multicomponent analysis (MCA) a method requiringa priori spectral information, is used as a referee method for ITT-FA.     

A new method for the automated selection of the number of components for deconvolving overlapping chromatographic peaks

Mathematical deconvolution methods can separate co-eluting peaks in samples for which (chromatographic) separation fail. However, these methods often heavily rely on manual user-input and interpretation. This is not only time-consuming but also error-prone and automation is needed if such methods are to be applied in a routine manner.     

Raman and IR Spectroscopy of a Mixed CH3CN-H2O Solvent in the Cn Stretching Region

Raman and IR spectroscopy have been used to elucidate the solvation process in a mixed water-acetonitrile solvent in the CN stretching region. The number and positions of the components forming the spectral contour are established by Fourier deconvolution and Factor analysis and their areas are determined by fitting. The forms of existence of acetonitrile in the mixed solvent are discussed.     

自动化质谱图解卷积和鉴定系统用于鉴定含痕量杂原子的石油馏分（英文）

Here we present a simple yet effective gas chromatography-mass spectrometry (GC-MS) identification approach for the detection of heteroatom-containing compounds (HACCs) in petroleum fractions. The MS/AMDIS (Automated Mass Spectral Deconvolution and Identification System) program was used to identify parts per million (ppm) HACC concentrations in petroleum fractions in place of traditional techniques (extraction and standard injection). Polycyclic aromatic sulfur heterocycles (S-PAHs) were used as model compounds to confirm the validity of the AMDIS identifiers, which were compared with extracted results using the off-line X-calibur software. AMDIS was able to identify ppm concentrations of S-PAHs in oil condensate. There was good agreement between experimental and AMDIS identification results for S-PAHs in oil condensate. AMDIS was also used to detect nitrogen-containing compounds (NCCs) and alkylphenols in oil condensate. Our results confirmed the presence of 2-methylbenzothiazole, carbazole, and 2,4-ditertbutyl phenol. In a crude oil sample, AMDIS identification of m/z=191 biomarkers was consistent with empirical results. Therefore, AMDIS can help to reduce the number of experimental steps in identification protocols.     

气相色谱-质谱数据的后处理

气相色谱-质谱(gaschromatography/massspectrometry)以其优异的分离定性特点,被广泛地应用于分析复杂混合物中的挥发性组分。然而,其质谱图会包含一些来自于离子源污染物、柱流失物、基质干扰物所产生的离子,导致定性准确性降低。在分析复杂基质中的痕量物质时,这一现象尤为突出。例如,在食品中农药多残留的分析中,为了确保各种极性的农药都有很好的回收,样本中的基质就会不可避免地被引入检测过程中,对目标化合物的质谱图产生严重的干扰。同时某些保留时间相近的化合物的质谱图也会叠加在一起,互相干扰。这些问题的存在使农药确认变得更为困难。因此,通过对质谱数据的后处理,将目标化合物的质谱图从原始谱图中提取出来,根据新建的"纯净"的质谱图对目标化合物进行确证将更有实际应用价值。     

迭代退卷积法去除光谱中的系统噪声

在测量光谱时,由于光学系统装置响应,会给光谱测量引入噪声,因此对光谱去噪声处理是必须的.本文对乘型迭代算法进行改进,提出了加法迭代退卷积.用加法迭代退卷积去除由于系统装置引入的噪声,得到真实的测量谱.