Discovering paths to optimized nanoparticle characteristics

Abstract

In contrast to materials properties, particle characteristics such as size, specific surface area, degree of agglomeration or crystallinity can be directly controlled by the synthesis process and also be determined experimentally without or with minimal further processing. Although particle characteristics only indirectly influence the (extrinsic) materials properties such as processability or charge carrier mobility and lifetime, they are key parameters to improve materials or device performance in applications. Process parameters – especially the time-temperature-profile in gas phase synthesis – control these nanoparticle characteristics. In this contribution a novel method is presented to discover paths to optimized nanoparticle characteristics in chemical vapor synthesis. As examples, physically realistic time-temperature profiles are predicted for a minimized degree of agglomeration at a desired primary particle size and a maximized degree of crystallinity at a desired specific surface area for the case of titania. The method relies on the integration of a simple model describing particle formation and growth into a Monte Carlo optimization algorithm. The results are surprising but can be rationalized using our understanding of particle formation and growth.