Self-driving laboratory for emulsion polymerization
Peter M. Pittaway, Stephen T. Knox, Olivier J. Cayre, Nikil Kapur, L.S. Golden, Sophie Drillières, Nicholas J. Warren
Abstract
Modern approaches to chemical product discovery are exploiting the benefits of flow-chemistry, online characterization, and smart automation to rapidly screen and optimize chemical transformations. The present work describes the development and application of an automated continuous-flow reactor platform for the rapid prototyping of latexes prepared via seeded free-radical emulsion polymerization . Using a multi-reactor system comprising a cascade of miniature continuous stirred-tank reactors (CSTRs) followed by a sonicated tubular reactor (STR) with five pumps for reagent delivery, the capability to explore a four-dimensional parameter space of surfactant concentration, seed fraction, monomer ratio, and feed-rate is demonstrated. With user-defined boundary conditions, a one-factor-at-a-time (OFAAT) approach first illustrates the capability to prepare products with unique and tuneable properties. Subsequently, an experimental design is constructed to explore a three-dimensional parameter space, with 16 reactions completed in under three days of platform time. This rapid generation of product prototypes allowed features of the polymer system to be evaluated on a timescale much shorter than traditional methods with a significant reduction in manual effort and human-chemical interaction. The resulting response surface model was applied for in silico optimization using the Thompson-sampling efficient multi-objective (TSEMO) optimization algorithm. Finally, online dynamic light scattering (DLS) was applied with the physical platform which enabled self-optimization of the polymerization, identifying the attainable particle sizes whilst minimizing the amounts of seed and surfactant used. Closing the loop resulted in a fully operational self-driving laboratory.