The time has come for an assessment of the most important techniques for the fabrication of advanced catalysts. Catalyst production alone is more than a billion dollar business each year, and the product value of chemical processes using advanced catalysts is a few trillion dollars annually. This book seeks to provide a modern, materials science account of the best and most current techniques for the synthesis of advanced catalytic materials.
Until now, there has been no single book which contains a definitive and comprehensive description of the important technologies for catalyst synthesis within the context of modern materials science. Academic researchers both in the catalytic sciences and materials sciences must have the best synthesis technologies available to accomplish the preparation of solid-state materials of specific structure and morphology. Althugh the emphasis is on new synthetic techniques for catalytic applications, the bookpresents all of the important technologies for the fabrication of electronic and structural ceramics, and superconductors.
Key Features
* Novel Techniques for Advanced Materials
* Nanostructured Materials Synthesis
* Mesoporous Molecular Sieves
* Pillared Clays
* Heteropoly Acids
* Nanostructured Supported Metal Catalysts
* Nanostructured Metal Oxide Catalysts and Materials
* Nanostructured Zeolite Materials
* Vapor Phase Materials Synthesis
* Sonochemical Materials Synthesis
* Aerosol Methods of Catalyst Synthesis
* Hydrodynamic Cavitational Techniques for Catalyst and Materials Synthesis
* Novel Sol-Gel Methods for Catalyst Synthesis
* Supercritical Methods for Materials Synthesis
* Liquid Crystal Techniques for Mesoporous Materials
* Micelle Techniques for Nanostructured Catalyst Preparation
* Fluidized Bed Techniques in Chemical Vapor Deposition
* Flame Methods of Advanced Catalyst Synthesis

Linear models, normally presented in a highly theoretical and mathematical style, are brought down to earth in this comprehensive textbook. Linear Models examines the subject from a mean model perspective, defining simple and easy-to-learn rules for building mean models, regression models, mean vectors, covariance matrices and sums of squares matrices for balanced and unbalanced data sets. The author includes both applied and theoretical discussions of the multivariate normal distribution, quadratic forms, maximum likelihood estimation, less than full rank models, and general mixed models. The mean model is used to bring all of these topics together in a coherent presentation of linear model theory.
Key Features
* Provides a versatile format for investigating linear model theory, using the mean model
* Uses examples that are familiar to the student:
* design of experiments, analysis of variance, regression, and normal distribution theory
* Includes a review of relevant linear algebra concepts
* Contains fully worked examples which follow the theorem/proof presentation