Comparative study of catalytic activity of Pd loaded hydroxyapatite and fluoroapatite in butan-2-ol conversion and methane oxidation

Palladium loaded calcium-hydroxyapatite, Pd(z)/CaHAp, and calcium-fluoroapatite, Pd(z)/CaFAp, were synthesised and characterised by TEM, XRD, IR and UV–vis–NIR spectroscopies. Introduction of palladium does not change the structure of CaHAp and CaFAp. The average size of PdO particles was found to be around 4–5 nm on Pd(1)/CaHAp but larger (6–7 nm) on Pd(1)/CaFap. The acid–base properties of the supports and of the catalysts were studied using butan-2-ol conversion. On CaHAp and CaFAp, the butenes yield (dehydration reaction) is very low either in the absence or in the presence of oxygen. The methyl ethyl ketone yield (dehydrogenation reaction) is significant only in the presence of oxygen and higher over CaFAp. Conversely, the performances of Pd(z)/CaHAp are better than those of Pd(z)/CaFAp below 180 °C. Above 180 °C, buta-2-ol combustion is favoured on Pd/CaHAp but not on Pd/CaFAp.

In methane oxidation, Pd(z)/CaHAp showed also a much larger activity than Pd(z)/CaFAp. On 2 wt% Pd loaded CaHAp, the methane oxidation reaches a conversion of almost 100% at 350 °C, which is comparable with the performance of conventional Pd/Al₂O₃ catalysts. The reducibility of PdO under methane–oxygen mixtures is lower on Pd(z)/CaHAp. For both reactions, the lower activity of Pd(z)/CaFAp is related to its higher acidity, resulting from the substitution of OH⁻ by F⁻, and to the larger PdO particle size.     

NaBr对C12-s-C12•2Br/氯仿体系中反胶团增溶水能力的影响

近红外(NIR)光谱技术可用于表征氯仿体系中反胶团增溶水的能力. 对于C12-s-C12•2Br (s=2, 3, 4, 5, 6, 8)系列, 不论体系是否含有NaBr电解质, 由于具有较短联接链的表面活性剂易形成较大的反胶团, 其增溶水的能力随着联接链长度增加而降低. 与未含NaBr电解质的体系相比, 当体系中存在NaBr电解质时所形成的反胶团增溶水能力降低.     

Simultaneous measurement of glucose and glutamine in aqueous solutions by near infrared spectroscopy

A method is described for measuring the concentrations of both glucose and glutamine in binary mixtures from near infrared (NIR) absorption spectra. Spectra are collected over the range from 5000–4000/cm (2.0–2.5μm) with a 1-mm optical path length. Glucose absorbance features at 4710, 4400, and 4300/cm and glutamine features at 4700, 4580, and 4390/cm provide the analytical information required for the measurement. Multivariate calibration models are generated by using partial least squares (PLS) regression alone and PLS regression combined with a preprocessing digital Fourier filtering step. The ideal number of PLS factors and spectral range are identified separately for each analyte. In addition, the optimum Fourier filter parameters are established for both compounds. The best overall analytical performance is obtained by combining Fourier filtering and PLS regression. Glucose measurements are established over the concentration range from 1.66–59.91 mM, with a standard error of prediction (SEP) of 0.32 mM and a mean percent error of 1.84%. Glutamine can be measured over the concentration range from 1.10–30.65 mM with a SEP of 0.75 mM and a mean percent error of 6.67%. These results demonstrate the analytical utility of NIR spectroscopy for monitoring glucose and glutamine levels in mammalian and insect cell cultures.     

Application of online near infrared spectroscopy to study the kinetics of anionic polymerization of butadiene

Online Fourier-transform near infrared (FT-NIR) spectroscopy in combination with a fiber optic probe was utilized to study the kinetics of the anionic polymerization of butadiene. The conversion of the butadiene to methylene protons in the polymer was monitored in solution polymerization conditions by monitoring the absorbance at 1632 nm. The off-line gel permeation chromatography, GPC, technique was used to validate the online FT-NIR spectroscopy method. Butadiene polymerization kinetics in cyclohexane initiated with n-butyllithium at different initiator concentrations and temperatures was studied. A phenomenological kinetic expression for the anionic polymerization of butadiene in cyclohexane initiated with n-butyllithium was determined. The predictions of the kinetic expression were in agreement with kinetic data reported in the literature.     

Detector comparison for sulfur and chlorine detection with laser induced breakdown spectroscopy in the near-infrared-region

Laser-induced breakdown spectroscopy has been employed for the investigation of the sulfur and chlorine content of building materials. Both, chloride and sulfate ions are major damaging species affecting the stability and lifetime of a structure. Chlorine and sulfur are mostly detected in the VUV and the NIR. In case of building materials the main elements like calcium or iron have many strong spectral lines over the whole spectral range, so that trace elements can only be detected in spectral windows unaffected from these lines. With regard to a preferably simply, robust against dust and vibrations and portable setup only the NIR spectral features are used for civil engineering applications.

Most detectors, mainly CCD cameras have rapidly decreasing quantum efficiency in the NIR. Also the quantum efficiency of the photocathode of CCD-Detectors with image intensifier is decreasing in the NIR. Different CCD-detectors were tested with respect to high quantum efficiency and high dynamic range, which is necessary for simultaneous detection of weak spectral lines from trace elements and intense spectral lines from main elements.

The measurements are made on reference samples consisting of cement, hydrated cement, cement mortar and concrete with well-defined amounts of the trace elements. Experimental conditions are chosen for an optimum intensity of the trace element spectral lines. The detector systems are compared by limit of detections and the signal to noise ratio.     

Coal analysis by diffuse reflectance near-infrared spectroscopy: Hierarchical cluster and linear discriminant analysis

An extensive study was carried out in coal samples coming from several origins trying to establish a relationship between nine coal properties (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal/kg), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) and the corresponding near-infrared spectral data. This research was developed by applying both quantitative (partial least squares regression, PLS) and qualitative multivariate analysis techniques (hierarchical cluster analysis, HCA; linear discriminant analysis, LDA), to determine a methodology able to estimate property values for a new coal sample. For that, it was necessary to define homogeneous clusters, whose calibration equations could be obtained with accuracy and precision levels comparable to those provided by commercial online analysers and, study the discrimination level between these groups of samples attending only to the instrumental variables. These two steps were performed in three different situations depending on the variables used for the pattern recognition: property values, spectral data (principal component analysis, PCA) or a combination of both. The results indicated that it was the last situation what offered the best results in both two steps previously described, with the added benefit of outlier detection and removal.     

复合近红外光谱法在牛奶测定中的应用

研究了用于快速测定牛奶主要成分含量的近红外透射光谱法和漫反射光谱法,分析了两种方法在实际测量中的差异,并提出将两种方法结合形成复合光谱法用于牛奶成分含量的检测。结果表明:复合光谱法的应用有效地提高了多变量回归分析中牛奶成分含量的预测精度。     

Feasibility study for the use of near infrared spectroscopy in the qualitative and quantitative analysis of green tea, Camellia sinensis (L.)

This paper indicates the possibility to use near infrared spectroscopy (NIR) combined with PLS as a rapid method to estimate the quality of green tea. NIR is used to build calibration models to predict the content of caffeine, epigallocatechin gallate (EGCG) and epicatechin (EC) and for the prediction of the total antioxidant capacity of green tea. For the determination of the total antioxidant capacity, the trolox equivalent antioxidant capacity (TEAC) method is used. Until now, the prediction of the antioxidant capacity as such by use of NIR has not been reported. For caffeine and TEAC, models are build for the whole green tea leaves and also for the ground leaves. For the polyphenols (EGCG and EC), only models for the whole leaves are investigated. A partial least squares (PLS) algorithm is used to perform the calibration. To decide upon the number of PLS factors included in the PLS model, the model with the lowest root mean square error of cross-validation (RMSECV) for the training set is chosen. The correlation coefficient (r) between the predicted and the reference results for the test set is used as an evaluation parameter for the models: for the TEAC results r=0.90 for the model with the whole leaves, r=0.86 for the model with the powdered leaves are obtained. The caffeine prediction model has a correlation coefficient r=0.96 for the whole leaves and r=0.93 for the ground leaves. The correlation coefficient for the EGCG and the EC content models are, respectively 0.83 and 0.44.     

A method for calibration and validation subset partitioning

This paper proposes a new method to divide a pool of samples into calibration and validation subsets for multivariate modelling. The proposed method is of value for analytical applications involving complex matrices, in which the composition variability of real samples cannot be easily reproduced by optimized experimental designs. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. The proposed technique is illustrated in a case study involving the prediction of three quality parameters (specific mass and distillation temperatures at which 10 and 90% of the sample has evaporated) of diesel by NIR spectrometry and PLS modelling. For comparison, PLS models are also constructed by full cross-validation, as well as by using the Kennard-Stone and random sampling methods for calibration and validation subset partitioning. The obtained models are compared in terms of prediction performance by employing an independent set of samples not used for calibration or validation. The results of F-tests at 95% confidence level reveal that the proposed technique may be an advantageous alternative to the other three strategies.     

基于遗传算法的波长选择方法在绿茶近红外光谱分析模型中的应用

利用遗传算法(GA)提取茶叶的近红外吸收特征波长的方法,研究建立了绿茶水分和氨基酸的近红外分析模型,并对波长选择前后两种成分的模型进行了比较分析。结果表明,经遗传算法波长选择后,简化了分析模型,同时模型的稳健性增强。氨基酸预测集的均方根误差(SEP)减小82.1%,水分预测集的均方根误差减小(SEP)76.6%,它们在波长选择前后对应的分析波长数之比分别为995∶7和1990∶33。