Experimental Design for Combinatorial and High Throughput Materials DevelopmentJames N. Cawse An invaluable reference to increasingly popular experimental methods In the past decade, combinatorial and high throughput experimental methods have revolutionized the pharmaceutical industry, allowing researchers to conduct more experiments in a week than was previously possible in a year. Now high throughput experimentation is rapidly spreading from its origins in the pharmaceutical world to larger industrial research establishments such as GE and DuPont, and even to smaller companies and universities. Consequently, researchers need to know the kinds of problems, desired outcomes, and appropriate patterns for these new strategies. Editor James Cawse's far-reaching study identifies and applies, with specific examples, these important new principles and techniques. Experimental Design for Combinatorial and High Throughput Materials Development progresses from methods that are now standard, such as gradient arrays, to mathematical developments that are breaking new ground. The former will be particularly useful to researchers entering the field, while the latter should inspire and challenge advanced practitioners. The book's contents are contributed by leading researchers in their respective fields. Chapters include: * High Throughput Synthetic Approaches for the Investigation of Inorganic Phase Space * Combinatorial Mapping of Polymer Blends Phase Behavior * Split-Plot Designs * Artificial Neural Networks in Catalyst Development * The Monte Carlo Approach to Library Design and Redesign The text also contains over 200 useful charts and drawings. Industrial chemists, chemical engineers, materials scientists, and physicists working in combinatorial and high throughput chemistry will find James Cawse's study to be an invaluable resource. |
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application approach approximation architecture array artificial neural networks biased binary catalyst Cawse Chem chemical chemical space chemistry CHTE combinations Combinatorial and High combinatorial chemistry complex components compounds computed covariance function deposition descriptors effects elements example experiment experimental design experimental region experimental space feature selection figure of merit formulation factors fractions genetic algorithms gradient hidden neurons input interactions large number lattice library design ligand linear mapping masking material libraries materials discovery measured molecular molecules Monte Carlo methods Monte Carlo protocols MSE values neural networks neurons noncomposition variables nonlinear number of samples optimal overtraining parallel tempering parameters perceptrons performance phase polymer predictive propene properties Proportion in Catalyst QSPR quaternary random regression response surface screening shown in Figure split-plot statistical stochastic process strategy structure subset substrate synthesis Table techniques Technology temperature template ternary test catalysts thin films tion training data vector Voronoi cell Xiang zeolite