Determination of a refractive index and an extinction coefficient of standard production of CVD-graphene

The refractive index and extinction coefficient of chemical vapour deposition grown graphene are determined by ellipsometry analysis. Graphene films were grown on copper substrates and transferred as both monolayers and bilayers onto SiO2/Si substrates by using standard manufacturing procedures. The chemical nature and thickness of residual debris formed after the transfer process were elucidated using photoelectron spectroscopy. The real layered structure so deduced has been used instead of the nominal one as the input in the ellipsometry analysis of monolayer and bilayer graphene, transferred onto both native and thermal silicon oxide. The effect of these contamination layers on the optical properties of the stacked structure is noticeable both in the visible and the ultraviolet spectral regions, thus masking the graphene optical response. Finally, the use of heat treatment under a nitrogen atmosphere of the graphene-based stacked structures, as a method to reduce the water content of the sample, and its effect on the optical response of both graphene and the residual debris layer are presented. The Lorentz-Drude model proposed for the optical response of graphene fits fairly well the experimental ellipsometric data for all the analysed graphene-based stacked structures.

Patterns of variance in stage-structured populations: Evolutionary predictions and ecological implications

Variability in population growth rate is thought to have negative consequences for organism fitness. Theory for matrix population models predicts that variance in population growth rate should be the sum of the variance in each matrix entry times the squared sensitivity term for that matrix entry. I analyzed the stage-specific demography of 30 field populations from 17 published studies for pattern between the variance of a demographic term and its contribution to population growth. There were no instances in which a matrix entry both was highly variable and had a large effect on population growth rate; instead, correlations between estimates of temporal variance in a term and contribution to population growth (sensitivity or elasticity) were overwhelmingly negative. In addition, survivorship or growth sensitivities or elasticities always exceeded those of fecundity, implying that the former two terms always contributed more to population growth rate. These results suggest that variable life history stages tend to contribute relatively little to population growth rates because natural selection may alter life histories to minimize stages with both high sensitivity and high variation.

Effect of rate of change of slip on the peak braking coefficient of passenger car tires. Technical report.

Mode of access: Internet.

The coefficient of thermal expansion of alpha uranium as fabricated by various methods /

"U.S. Atomic Energy Commission Contract AT(29-1)-1106."

Comparisons among treatment means in an analysis of variance /

Issued Oct. 1977.

Lectures on the analysis of variance /

Cover title.

Method of determining the coefficient of storage from straight-line plots without extrapolation /

Mode of access: Internet.

Components of variance method and partitioning method of genetic analysis applied to weight per fruit of tomato hybrid and parental populations /

Includes bibliographical references (p. 56-57).

Multivariate analyses of variance and covariance for simulation studies involving normal time series

Photocopy.

An introduction to analysis of variance (ANOVA) with special reference to data from clinical experiments in optometry

This article is aimed primarily at eye care practitioners who are undertaking advanced clinical research, and who wish to apply analysis of variance (ANOVA) to their data. ANOVA is a data analysis method of great utility and flexibility. This article describes why and how ANOVA was developed, the basic logic which underlies the method and the assumptions that the method makes for it to be validly applied to data from clinical experiments in optometry. The application of the method to the analysis of a simple data set is then described. In addition, the methods available for making planned comparisons between treatment means and for making post hoc tests are evaluated. The problem of determining the number of replicates or patients required in a given experimental situation is also discussed. Copyright (C) 2000 The College of Optometrists.

The application of analysis of variance (ANOVA) to different experimental designs in optometry

Analysis of variance (ANOVA) is the most efficient method available for the analysis of experimental data. Analysis of variance is a method of considerable complexity and subtlety, with many different variations, each of which applies in a particular experimental context. Hence, it is possible to apply the wrong type of ANOVA to data and, therefore, to draw an erroneous conclusion from an experiment. This article reviews the types of ANOVA most likely to arise in clinical experiments in optometry including the one-way ANOVA ('fixed' and 'random effect' models), two-way ANOVA in randomised blocks, three-way ANOVA, and factorial experimental designs (including the varieties known as 'split-plot' and 'repeated measures'). For each ANOVA, the appropriate experimental design is described, a statistical model is formulated, and the advantages and limitations of each type of design discussed. In addition, the problems of non-conformity to the statistical model and determination of the number of replications are considered. © 2002 The College of Optometrists.

Statnote 23: Non-parametric analysis of variance (ANOVA)

To carry out an analysis of variance, several assumptions are made about the nature of the experimental data which have to be at least approximately true for the tests to be valid. One of the most important of these assumptions is that a measured quantity must be a parametric variable, i.e., a member of a normally distributed population. If the data are not normally distributed, then one method of approach is to transform the data to a different scale so that the new variable is more likely to be normally distributed. An alternative method, however, is to use a non-parametric analysis of variance. There are a limited number of such tests available but two useful tests are described in this Statnote, viz., the Kruskal-Wallis test and Friedmann’s analysis of variance.