The new layered ternary compound Nb3GexTe6 (x ? 0.90) was prepared by direct combination of the elements taken in the stoichiometric proportions 3 : 1 : 6, heated at 1 000 °C for 10 days in silica tubes and quenched to room temperature. The phase crystallizes in the orthorhombic symmetry, space group Pnma (#62), with the following single crystal refined parameters: a = 643.18(5) pm, b = 1391.98(11)pm and c = 1 154.07(5) pm, with Z = 4. The structure was refined to an R of 3.4% (Rw = 4.6%), with 1969 independent reflexions and 49 parameters. The structure is based on the close stacking of trigonal prismatic (TP) slabs in the AA/BB mode. The slabs can be seen as built up from face sharing biprisms, which are filled either by one or by two niobium cations situated in the middle of the trigonal prisms. The germanium is located in the middle of the common face of two prisms, leading to a rather unusual anionic square coordination. The refinements showed that this latter cation does not fill completely its square site. No cation was found in the van der Waals gap between the slabs. The mean dGe? Te distance (276.5 pm) is in agreement with GeII cations, while some Te …? Te distances (from 333.84 to 361.65pm) are too short for anions in a simple contact. These bonding distances, already mentionned in some MTe2 compounds, are to be ascribed to charge transfer in the structure, with a partial oxidation state for the tellurium anions. Short Nb? Nb and Nb? Ge distances (292.0 and 281.3 pm, respectively) imply intercationic bonding within the slabs. 相似文献
This article discusses problems of validating classification models especially in datasets where sample sizes are small and the number of variables is large. It describes the use of percentage correctly classified (%CC) as an indicator for success of a classification model. For small datasets, %CC should not be used uncritically and its interpretation depends on sample size. It illustrates the use of a common classification method, discriminant partial least squares (D-PLS) on a randomly generated dataset of 200 samples and 200 variables.
An aim of the classifier is to determine whether the null hypothesis (there is no distinction between two classes) can be rejected. Autoprediction gives an 84.5% CC. It is shown that, if there is variable selection, it must be performed independently on the training set to obtain a CC close to 50% on the test set; otherwise, over-optimistic and false conclusions can be reached about the ability to classify samples into groups.
Finally, two aims of determining the quality of a model are frequently confused, namely optimisation (often used to determine the most appropriate number of components in a model) and independent validation; to overcome this, the data should be split into three groups.
There are often difficulties with model building if validation and optimisation have been done on different groups of samples, especially using iterative methods, each group being modelled using properties, such as a different number of components or different variables. 相似文献
In this study, chemometric techniques such as cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and partial least squares (PLS) were used to analyse the wastewater dataset to identify the factors which affect the composition of sewage of domestic origin, spatial and temporal variations, similarity/dissimilarity among the wastewater characteristics of cis- and trans-drains and discriminating variables. Samples collected from 24 wastewater drains in Lucknow city and from three sites on Gomti river in the month of January/February, May, August and November during the period of 5 years (1994-1999) were characterized for 32 parameters. The multivariate techniques successfully described the similarities/dissimilarities among the sewage drains on the basis of their wastewater characteristics and sources signifying the effect of routine domestic/commercial activities in respective drainage areas. Spatial and seasonal variations in wastewater composition were also determined successfully. CA generated six groups of drains on the basis of similar wastewater characteristic. PCA provided information on seasonal influence and compositional differences in sewage generated by domestic and industrial waste dominated drains and showed that drains influenced by mixed industrial effluents have high organic pollution load. DA rendered six variables (TDS, alkalinity, F, TKN, Cd and Cr) discriminating between cis- and trans-drains. PLS-DA showed dominance of Cd, Cr, NO3, PO4 and F in cis-drains wastewater. The results suggest that biological-process based STPs could treat wastewater both from the cis- as well as trans-drains, however, prior removal of toxic metals will be required from the cis-drains sewage. Further, seasonal variations in wastewater composition and pollution load could be the guiding factor for determining the STPs design parameters. The information generated would be useful in selection of process type and in designing of the proposed sewage treatment plants (STPs) for safe disposal of wastewater. 相似文献
We present a method for computing classical Newtonian trajectories that minimize the path length or transit time from reactant
to product. Our approach is based on a generalization of the fast-marching method, which allows us to construct the solution
of the Hamilton-Jacobi equation for the action that optimizes the desired quantity. The resulting “reactive paths” can be
interpreted as reaction coordinates but, unlike more conventional choices, they contain dynamical information about the chemical
system of interest. 相似文献
Water quality data set from the alluvial region in the Gangetic plain in northern India, which is known for high fluoride levels in soil and groundwater, has been analysed by chemometric techniques, such as principal component analysis (PCA), discriminant analysis (DA) and partial least squares (PLS) in order to investigate the compositional differences between surface and groundwater samples, spatial variations in groundwater composition and influence of natural and anthropogenic factors. Trilinear plots of major ions showed that the groundwater in this region is mainly of Na/K-bicarbonate type. PCA performed on complete data matrix yielded six significant PCs explaining 65% of the data variance. Although, PCA rendered considerable data reduction, it could not clearly group and distinguish the sample types (dug well, hand-pump and surface water). However, a visible differentiation between the water samples pertaining to two watersheds (Khar and Loni) was obtained. DA identified six discriminating variables between surface and groundwater and also between different types of samples (dug well, hand pump and surface water). Distinct grouping of the surface and groundwater samples was achieved using the PLS technique. It further showed that the groundwater samples are dominated by variables having origin both in natural and anthropogenic sources in the region, whereas, variables of industrial origin dominate the surface water samples. It also suggested that the groundwater sources are contaminated with various industrial contaminants in the region. 相似文献