Crystallizations are resource-intensive purification techniques in the manufacture of materials and chemicals that society uses every day. Nucleation is the birth of a crystal from a solution, in which molecules come together to form the tiny nucleus that eventually grows into a larger crystal. This nucleation process is critical to produce valuable products, such as dyes and pharmaceuticals. However, it requires significant amounts of chemical solvents and energy. This project seeks to improve the sustainability of crystallizations by pinpointing mechanisms by which light can induce nucleation. Using an external trigger, such as light, to induce and control nucleation has the potential to reduce the environmental impacts of crystallizations in industrial processes. The project will also develop basic design rules that may ultimately provide better control over crystal shape and the arrangement of molecules to properties that can be optimized for the particular application of the materials. The investigators will also create educational activities that train undergraduate and graduate students from diverse backgrounds to design “greener” crystallizations, making it an inherent part of basic chemical engineering education. By collaborating with the Applied Research Innovations in Science and Engineering (ARISE) program, the investigators will mentor underrepresented high-school students who will complete summer research experiences. <br/><br/>The overall mission of this research program will be to design computer-aided, high-throughput crystallization experiments to quantify the mechanisms that govern non-photochemical laser induced nucleation (NPLIN). Of specific interest will be the quantification of the conditions in which the dielectric polarization (DP) and colloidal impurity (CI) mechanisms govern light-induced nucleation. The novel computer-aided experimental methodology will examine three elementary crystallizations: i) urea as a model single-step nucleation, ii) glycine as a two-step nucleation, and iii) amino acid oligomers, such as glycylglycine and triglycine, that are building blocks for proteins. Continuous-flow, high-pressure microfluidic devices coupled to a laser will be designed and implemented to switch off the CI mechanism by suppressing the formation of nanobubbles. Supervised machine learning methods will be trained with data collected at ambient and elevated pressures to build design rules for the DP and CI mechanisms. Computer-aided experimental methods for the study of crystal nucleation mechanisms, a field that remains vastly an art and based on outdated batch techniques, is an emerging area of science that has the potential to create new unit operations in industrial processes.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.