A D-Optimal Design for Estimation of Parameters of an Exponential-Linear Growth Curve of Nanostructures

We consider the problem of determining an optimal experimental design for estimation of parameters of a class of complex curves characterizing nanowire growth that is partially exponential and partially linear. Locally D-optimal designs for some of the models belonging to this class are obtained by using a geometric approach. Further, a Bayesian sequential algorithm is proposed for obtaining D-optimal designs for models with a closed-form solution, and for obtaining efficient designs in situations where theoretical results cannot be obtained. The advantages of the proposed algorithm over traditional approaches adopted in recently reported nano-experiments are demonstrated using Monte-Carlo simulations. The computer code implementing the sequential algorithm is available as supplementary material.