Molecular Modeling of the Sensitivities of Energetic Materials, Volume 22 introduces experimental aspects, explores the relationships between sensitivity, molecular structure and crystal structure, discusses insights from numerical simulations, and highlights applications of these approaches to the design of new materials. Providing practical guidelines for implementing predictive models and their application to the search for new compounds, this book is an authoritative guide to an exciting field of research that warrants a computer-aided approach for the investigation and design of safe and powerful explosives or propellants. Much recent effort has been put into modeling sensitivities, with most work focusing on impact sensitivity and leading to a lot of experimental data in this area. Models must therefore be developed to allow evaluation of significant properties from the structure of constitutive molecules.

This useful reference describes the statistical planning and design of pharmaceutical experiments, covering all stages in the development process-including preformulation, formulation, process study and optimization, scale-up, and robust process and formulation development.Shows how to overcome pharmaceutical, technological, and economic constraints on experiment design!Directly comparing the advantages and disadvantages of specific techniques, Pharmaceutical Experimental DesignA* offers broad, detailed, up-to-date descriptions of designs and methods not easily accessible in other booksA* reviews screening designs for qualitative factors at different levelsA* presents designs for predictive models and their use in optimizationA* highlights optimization methods, such as steepest ascent, optimum path, canonical analysis, graphical analysis, and desirabilityA* discusses the Taguchi method for quality assurance and approaches for robust scaling up and process transferA* details nonstandard designs and mixturesA* analyzes factorial, D-optimal design, and offline quality assurance techniquesA* reveals how one experimental design evolves from anotherA* and more!Featuring over 700 references, tables, equations, and drawings, Pharmaceutical Experimental Design is suitable for industrial, research, and clinical pharmaceutical scientists, pharmacists, and pharmacologists; statisticians and biostatisticians; drug regulatory affairs personnel; biotechnologists; formulation, analytical, and synthetic chemists and engineers, quality assurance personnel; all users of statistical experimental design in research and development; and postgraduate and postdoctoral research workers in these disciplines.

This useful reference describes the statistical planning and design of pharmaceutical experiments, covering all stages in the development process-including preformulation, formulation, process study and optimization, scale-up, and robust process and formulation development.Shows how to overcome pharmaceutical, technological, and economic constraints on experiment design!Directly comparing the advantages and disadvantages of specific techniques, Pharmaceutical Experimental DesignA* offers broad, detailed, up-to-date descriptions of designs and methods not easily accessible in other booksA* reviews screening designs for qualitative factors at different levelsA* presents designs for predictive models and their use in optimizationA* highlights optimization methods, such as steepest ascent, optimum path, canonical analysis, graphical analysis, and desirabilityA* discusses the Taguchi method for quality assurance and approaches for robust scaling up and process transferA* details nonstandard designs and mixturesA* analyzes factorial, D-optimal design, and offline quality assurance techniquesA* reveals how one experimental design evolves from anotherA* and more!Featuring over 700 references, tables, equations, and drawings, Pharmaceutical Experimental Design is suitable for industrial, research, and clinical pharmaceutical scientists, pharmacists, and pharmacologists; statisticians and biostatisticians; drug regulatory affairs personnel; biotechnologists; formulation, analytical, and synthetic chemists and engineers, quality assurance personnel; all users of statistical experimental design in research and development; and postgraduate and postdoctoral research workers in these disciplines.