Copyright (C) 2010 S. Karger AG, Basel”
“The concept of energy filtering of the carriers to control the thermoelectric properties of PbTe is experimentally applied in this present work.
The energy barriers SB525334 price at the grain interfaces of the nanocomposites and the embedded Ag-rich nanodots within the grains are supposed to control the energy dependency of carrier scattering: that is what we mean by energy filtering of carriers. As a case study, vertical Bridgman grown bulk PbTe:undoped, PbTe:Ag crystals and nanocomposites of PbTe: Ag are used as samples. Thermoelectric properties of all the samples have been evaluated through temperature dependent electrical conductivity, Seebeck coefficient and room temperature Hall and thermal conductivity measurements. It is found that the PbTe: Ag nanocomposites has the highest power factor of 18.78 x 10(-4) W this website m(-1) K(-2) with a room temperature thermal conductivity of 1.69 W m(-1) K(-1). The crystal structures of these samples show the effective potential barrier at the grain boundaries and embedded nanodots within the grains to facilitate the energy filtering of the carriers. (C) 2010 American Institute of Physics. [doi:10.1063/1.3488621]“
“Background: The 2000 Centers for Disease Control and Prevention (CDC) growth charts included lambda-mu-sigma (LMS) parameters intended to calculate smoothed percentiles
from only the 3rd to the 97th percentile.
Objective: The objective was to evaluate different approaches to describing more extreme values of body mass index (BMI)-for-age by using simple functions of the CDC growth charts.
Design: Empirical data for the 99th and the 1st percentiles of BMI-for-age were calculated from the data set used to construct the growth charts and were compared with estimates extrapolated from the CDC-supplied LMS parameters
and to various functions of other smoothed percentiles. A set of reestimated LMS parameters that incorporated a smoothed 99th percentile were also evaluated.
Results: Extreme percentiles extrapolated from the CDC-supplied LMS parameters did not match well to the empirical data for the 99th percentile. A better fit to the empirical Selleckchem BIIB057 data was obtained by using 120% of the smoothed 95th percentile. The empirical first percentile was reasonably well approximated by extrapolations from the LMS values. The reestimated LMS parameters had several drawbacks and no clear advantages.
Conclusions: Several approximations can be used to describe extreme high values of BMI-for-age with the use of the CDC growth charts. Extrapolation from the CDC-supplied LMS parameters does not provide a good fit to the empirical 99th percentile values. Simple approximations to high values as percentages of the existing smoothed percentiles have some practical advantages over imputation of very high percentiles. The expression of high BMI values as a percentage of the 95th percentile can provide a flexible approach to describing and tracking heavier children.