Study of electrical conduction in polypyrrole by varying the doping level

Electrical conductivity and thermopower are studied in the conducting polymer polypyrrole doped with varying levels of the dopant hexafluoro phosphate (PF6). A single sample is prepared by galvanostatic electrochemical polymerization at -40 degreesC. From this sample, six samples having different dopant levels and correspondingly different conductivity are prepared by dedoping. Low temperature d.c. electrical conductivity measurement shows the metal-insulator transition from fully doped sample to dedoped samples. On the metallic side the data are fitted to the localization-interaction model. In critical regime, it follows the power law. On the insulating side, it is variable range hopping. Thermopower measurements are done in the temperature range 300 K to 20 K. Thermopower is linear for samples on the metallic side and becomes more and more non-linear on the insulating side. It is described using a combination of the linear metallic term and the non-linear hopping term. (C) 2002 Elsevier Science B.V. All rights reserved.

Effect of catchment characteristics on the relationship between past discharge and the power law recession coefficient

This study concerns the relationship between the power law recession coefficient k (in - dQ/dt = kQ(alpha), Q being discharge at the basin outlet) and past average discharge Q(N) (where N is the temporal distance from the center of the selected time span in the past to the recession peak), which serves as a proxy for past storage state of the basin. The strength of the k-Q(N) relationship is characterized by the coefficient of determination R-N(2), which is expected to indicate the basin's ability to hold water for N days. The main objective of this study is to examine how R-N(2) value of a basin is related with its physical characteristics. For this purpose, we use streamflow data from 358 basins in the United States and selected 18 physical parameters for each basin. First, we transform the physical parameters into mutually independent principal components. Then we employ multiple linear regression method to construct a model of R-N(2) in terms of the principal components. Furthermore, we employ step-wise multiple linear regression method to identify the dominant catchment characteristics that influence R-N(2) and their directions of influence. Our results indicate that R-N(2) is appreciably related to catchment characteristics. Particularly, it is noteworthy that the coefficient of determination of the relationship between R-N(2) and the catchment characteristics is 0.643 for N = 45. We found that topographical characteristics of a basin are the most dominant factors in controlling the value of R-N(2). Our results may be suggesting that it is possible to tell about the water holding capacity of a basin by just knowing about a few of its physical characteristics. (C) 2015 Elsevier B.V. All rights reserved.