Showing papers by "Xihua Du published in 2015"
TL;DR: In this paper, the authors adopt the method of controlling discharging temperature to reduce the charging polarization during the first cycle of Li 2 O 2 based on the fact that control of temperature can reduce Li 2 o 2 bulk charge transport path.
4 citations
TL;DR: Based on the location of bromine substituents and conjugation matrix, a new substituent position index 0X not only was defined, but also molecular shape indexes Km and electronegativity distance vectors Mm of diphenylamine and 209 kinds of polybrominated diphenyamine (PBDPA) molecules were calculated as mentioned in this paper.
Abstract: Based on the location of bromine substituents and conjugation matrix, a new substituent position index 0X not only was defined, but also molecular shape indexes Km and electronegativity distance vectors Mm of diphenylamine and 209 kinds of polybrominated diphenylamine (PBDPA) molecules were calculated. Then the quantitative structure-property relationships (QSPR) among the thermodynamic properties of 210 organic pollutants and 0X, K3, M29, M36 were founded by Leaps-and-Bounds regression. Using the four structural parameters as input neurons of the artificial neural network, three satisfactory QSPR models with network structures of 4:21:1, 4:24:1, and 4:24:1 respectively, were achieved by the back-propagation algorithm. The total correlation coefficients R were 0.9999, 0.9997, and 0.9995 respectively and the standard errors S were 1.036, 1.469, and 1.510 respectively. The relative mean deviation between the predicted value and the experimental value of S⊖, ΔfH⊖ and ΔfG⊖ were 0.11%, 0.34% and 0.24% respecti...
TL;DR: In this article, a BP neural network method was used to calculate and analyze the molecular structure of aromatic hydrocarbons, and then, the electrotopological state indices and the molecular electronegativity distance vectors were calculated based on the calculated molecular structure characteristics and adjacency matrix.
Abstract: Using BP neural network method, we calculate and analyze the molecular structure of aromatic hydrocarbons. Then, we get the electrotopological state indices and the molecular electronegativity distance vectors of 25 aromatic hydrocarbons based on the calculation of molecular structure characteristics and adjacency matrix. By regression, we get and optimize the structural parameters E9, E13, E17 and M15. The four structural parameters are used as the input variables and a 4-2-1 network structure is employed to construct a BP artificial neural network model for predicting acute toxicity pEC50. The total correlation coefficient R is 0.994 and the average error between the predicted value and experimental value of pEC50 is 0.079, which indicate that the ANN model has good stability and superior predictive ability. The results show that there is a good nonlinear correlation between acute toxicity pEC50 and the four structural parameters. The results of our research reveal that the toxicity of aromatic hydrocarbons is closely affected by electrotopological state indices and the molecular electronegativity distance vectors. Therefore, it will be helpful in assessing the hazard of aromatic hydrocarbons to environment.