The self- and directed- assembly of colloidal (nano- to micro- scale) particles into structures within materials and devices is an emerging paradigm with wide-ranging technological impact. However, the ability to create a target structure with an acceptably small level of defects is still lacking; systems too easily become dynamically arrested in undesired disordered, or defect-rich, states. Our research will synergistically combine recent advances in digital microscopic imaging techniques and free energy calculation methods to address this problem. Emerging microscopic imaging tools, including techniques such as confocal and total internal reflectance, provide unprecedented high-resolution, real-time, three-dimensional visualizations of colloidal assembly processes. The key is to mine the tremendous amount of digital data produced in these experiments to identify successful pathways to particle assembly. Theoreticians have traditionally approached such problems using the energy landscape (EL) paradigm, wherein one maps data from the high-dimensional configuration space (of order N, the number of particles in the system) to an EL in a low-dimensional set of key descriptors. This EL is the quantity of direct relevance to engineering of the process of interest; it contains the information (peaks, valleys, saddles) to quantify the equilibrium states and transition rates between them. In this research we will (1) develop a close coupling methodology between digital optical microscopy experiments and particle-based simulation to compare measured and predicted ELs, and (2) use the resulting ELs to engineer (design, control, optimize) two applications: the self-assembly of photonic crystals and operation of electronic nanowire devices.
Particles with sizes on the order of nanometers to micrometers immersed in fluid, commonly called colloids, can serve as the building blocks of interesting new products. Examples include photonic band gap materials (e.g. for computers that operate with light instead of electrons) and dynamically reconfigurable nanowires (e.g. for tunable RF devices). Under certain conditions the colloidal particles will spontaneously assemble into such useful materials, or they can be induced to do so through the application of external stimuli such as electric fields or temperature gradients. However, the pathways to creating desired structures with acceptably low levels of defects are not well understood. This research employs a combination of modern digital imaging techniques and theoretical tools from the field of statistical mechanics to measure, quantify, and control the assembly process. We will develop knowledge of engineered pathways to low-defect structured particulate materials that can be systematically implemented in the manufacturing processes of the future. Furthermore, we will use the rich visual data generated in experiments (e.g. images, videos), simulations (e.g. renderings, animations), and analyses (e.g. dynamic, multi-dimensional plots) to provide intuitive educational experiences for students at all levels (K-postgraduate) and in outreach programs to the general public.
