Application of TLSER method in predicting the aqueous solubility and n-octanol/water partition coefficient of PCBs,PCDDs and PCDFs

The theoretical linear solvation energy relationship(TLSER) approach was adopted to predict the aqueous solubility and n -octanol/water partition coefficient of three groups of environmentally important chemicals-polychlorinated biphenyls(PCBs), polychlorinated dibenzodioxins and dibenzofurans(PCDDs and PCDFs). For each compound, five quantum parameters were calculated using AM1 semiempirical molecular orbital methods and used as structure descriptors: average molecular polarizability(α), energy of the lowest unoccupied molecular orbit( E _ LUMO ), energy of the highest occupied molecular orbit( E _ HOMO ), the most positive charge on a hydrogen atom( q _+), and the most negative atomic partial charge( q _-) in the solute molecule. Then standard independent variables in TLSER equation was extracted and two series of quantitative equations between these quantum parameters and aqueous solubility and n -octanol/water partition coefficient were obtained by stepwise multiple linear regression(MLR) method. The developed equations have both quite high accuracy and explicit meanings. And the cross-validation test illustrated the good predictive power and stability of the established models. The results showed that TLSER could be used as a promising approach in the estimation of partition and solubility properties of macromolecular chemicals, such as persistent organic pollutants.

Origin of potential errors in the quantitative determination of terahertz optical properties in time-domain terahertz spectroscopy

We demonstrate theoretically and experimentally how changes of a terahertz （THz） beam induced by the sample affect the accuracy of the determination of THz dielectric properties in THz time-domain transmission spectros- copy （TDTS）. We apply a Gaussian beam and the ABCD matrix formalism to describe the propagation of the THz beam in a focused beam setup. The insertion of the sample induces a focus displacement which is absent in the reference t without a sample. We show how the focus displacement can be corrected. The THz optical properties after focus displacement correction reported in this Letter are in quantitative agreement with those obtained using collimated beam THz-TDTSinpreviouswork.

Material microstructures analyzed by using gray level Co-occurrence matrices

The mechanical properties of materials greatly depend on the microstructure morphology. The quantitative characterization of material microstructures is essential for the performance prediction and hence the material design. At present,the quantitative characterization methods mainly rely on the microstructure characterization of shape, size, distribution,and volume fraction, which related to the mechanical properties. These traditional methods have been applied for several decades and the subjectivity of human factors induces unavoidable errors. In this paper, we try to bypass the traditional operations and identify the relationship between the microstructures and the material properties by the texture of image itself directly. The statistical approach is based on gray level Co-occurrence matrix（GLCM）, allowing an objective and repeatable study on material microstructures. We first present how to identify GLCM with the optimal parameters, and then apply the method on three systems with different microstructures. The results show that GLCM can reveal the interface information and microstructures complexity with less human impact. Naturally, there is a good correlation between GLCM and the mechanical properties.

Prediction of Molar Absorptivities of Color Reagents and Their Color Reactions with Yttrium by Artificial Neural Networks

The new topological indices A x1 A x3 suggested in our laboratories were applied to the study of structure property relationships between color reagents and their color reactions with yttrium. The topological indices of twenty asymmetrical phosphone bisazo derivatives of chromotropic acid were calculated. The work shows that QSPR can be used as a novel aid to predict the molar absorptivities of color reactions and in the long term to be helpful tool in color reagent design. Multiple regression analysis and neural network were employed simultaneously in this study. The results demonstrated the feasibility and the effectiveness of the method.

QSPR Studies on the Octanol/water Partition Coefficient (lgK_(ow)) of Substituted Anilines with 2D and 3D Methods

Octanol/water partition coefficient （Kow） is a crucial property for evaluating the environmental behavior and fate of organic compound. Herein, some quantitative structure-property relationship （QSPR） studies were performed to estimate and predict the lgK ow of substituted anilines. 2D method （multiple linear regression, MLR） and 3D method （comparative molecular field analysis, CoMFA） were applied in this study. Successful 2D and 3D models yielded the correlation coefficient （R2） values of 0.981 and 0.966 and the Leave-One-Out （LOO） cross-validated correlation coefficient （q2） values of 0.933 and 0.820, respectively. The developed models have a highly predictive ability in both internal and external validation. In addition, the results were interpreted in terms of physical and chemical meanings of descriptors and field contribution maps. It showed that the steric and electrostatic properties are the primary factors that govern the lgK ow of substituted anilines. The information obtained from the QSPR models would be helpful to the interpretation of structural features pertinent to the lgK ow of substituted anilines, which may be helpful in estimating the organic compounds＇ potential harm to the environment.

Heterogeneous Fenton degradation of azodyes catalyzed by modified polyacrylonitrile fiber Fe complexes: QSPR (quantitative structure peorperty relationship) study

The amidoximated polyacrylonitrile （PAN） fiber Fe complexeswere prepared and used as the heterogeneous Fenton catalysts for thedegradation of28 anionicwater soluble azodyes inwater under visible irradiation. The multiple linear regression （MLR） methodwas employed todevelop the quantitative structure property relationship （QSPR） model equations for thedecoloration and mineralization of azodyes. Moreover, the predictive ability of the QSPR model equationswas assessed using Leave-one-out （LOO） and cross-validation （CV） methods. Additionally, the effect of Fe content of catalyst and the sodium chloride inwater on QSPR model equationswere also investigated. The results indicated that the heterogeneous photo-Fentondegradation of the azodyeswithdifferent structureswas conducted in the presence of the amidoximated PAN fiber Fe complex. The QSPR model equations for thedyedecoloration and mineralizationwere successfullydeveloped using MLR technique. MW/S （molecularweightdivided by the number of sulphonate groups） and N N=N （the number of azo linkage） are considered as the most importantdetermining factor for thedyedegradation and mineralization, and there is a significant negative correlation between MW/S or N N=N anddegradation percentage or total organic carbon （TOC） removal. Moreover, LOO and CV analysis suggested that the obtained QSPR model equations have the better prediction ability. The variation in Fe content of catalyst and the addition of sodium chloridedid not alter the nature of the QSPR model equations.

Filter paper-templated preparation of ZnO thin films and examination of their gas-sensing properties

ZnO thin films prepared by using quantitative filter paper as a template and Zn（CH3CO2）2.2H2O ethanol precursor solution were characterized by scanning electron microscopy （SEM） and X-ray diffraction （XRD）. The effects of sample calcination temperature, precursor concentration and filter paper types were studied, and the growth process was investigated by infra-red （IR） spectroscopy and thermogravimettic analysis/differential thermal analysis （TGA/DTA）. The results show that samples soaked in a 1.5 mol/L Zn（CH3 CO2）2.2H2O ethanol solution and calcined at 600 ℃ yield ZnO films of uniform particle size, approximately 30, 40 and 50 nm. for fast-, medium- and slow-speed filter papers, respectively. The formaldehyde gas sensing properties of the ZnO nanoparticles were tested, showing that the material prepared from fast-speed filter paper has a higher response to 120-205 ppm formaldehyde at 400 ℃ than that prepared from medium- or slow-sneed paper, which depends on the narticle size.

Mathematical modeling of malaria transmission dynamics in humans with mobility and control states

Malaria importation is one of the hypothetical drivers of malaria transmission dynamics across the globe.Several studies on malaria importation focused on the effect of the use of conventional malaria control strategies as approved by the World Health Organization(WHO)on malaria transmission dynamics but did not capture the effect of the use of traditional malaria control strategies by vigilant humans.In order to handle the aforementioned situation,a novel system of Ordinary Differential Equations(ODEs)was developed comprising the human and the malaria vector compartments.Analysis of the system was carried out to assess its quantitative properties.The novel computational algorithm used to solve the developed system of ODEs was implemented and benchmarked with the existing Runge-Kutta numerical solution method.Furthermore,simulations of different vigilant conditions useful to control malaria were carried out.The novel system of malaria models was well-posed and epidemiologically meaningful based on its quantitative properties.The novel algorithm performed relatively better in terms of model simulation accuracy than Runge-Kutta.At the best model-fit condition of 98%vigilance to the use of conventional and traditional malaria control strategies,this study revealed that malaria importation has a persistent impact on malaria transmission dynamics.In lieu of this,this study opined that total vigilance to the use of the WHO-approved and traditional malaria management tools would be the most effective control strategy against malaria importation.

Calculation of Hildebrand Solubility Parameters of Some Polymers Using QSPR Methods Based on LS-SVM Technique and Theoretical Molecular Descriptors

In this work, some chemometrics methods are applied for the modeling and prediction of the Hildebrand solubility parameter of some polymers. A genetic algorithm （GA） method is designed for the selection of variables to construct two models using the multiple linear regression （MLR） and least square-support vector machine （LS-SVM） methods in order to predict the Hildebrand solubility parameter. The MLR method is used to build a linear relationship between the molecular descriptors and the Hildebrand solubility parameter for these compounds. Then the LS-SVM method is utilized to construct the non-linear quantitative structure-activity relationship （QSAR） models. The results obtained using the LS-SVM method are then compared with those obtained for the MLR method; it was revealed that the LS-SVM model was much better than the MLR one. The root-mean-square errors of the training set and the test set for the LS-SVM model were 0.2912 and 0.2427, and the correlation coefficients were 0.9662 and 0.9518, respectively. This paper provides a new and effective method for predicting the Hildebrand solubility parameter for some polymers, and also reveals that the LS-SVM method can be used as a powerful chemometrics tool for the quantitative structure-property relationship （QSPR） studies.