Multivariate Optimization: A Powerful Tool for the Systematic Control of Quantum Dots Properties

In this chapter, we present some chemometric tools used in the area of experimental design and multivariate optimization. To make the subject more understandable, a didactic example employing colloidal aqueous synthesis of quantum dots is employed. We start with the factorial design that is very useful in screening which factors are important to the response of interest. All statistical calculations and interpretations of individual and interaction effects are detailed. Then, we describe how to build and evaluate empirical models by analysis of variance (ANOVA) to explain the behavior of the data set. Finally, the response surface methodology (RSM) is described. We expect this chapter to be useful as a guide for those who seek to solve synthetic problems in a quicker and more objective way, providing particularly a wider perception of the experimental factors that dominate the responses of interest of a system under study.