A self-driving laboratory optimizes a scalable process for making functional coatings
Connor C. Rupnow, Benjamin P. MacLeod, Mehrdad Mokhtari, Karry Ocean, Kevan E. Dettelbach, Daniel W. Lin, Fraser G. L. Parlane, Hsi Nien Chiu, Michael B. Rooney, Chris Waizenegger, Elija I. de Hoog, Abhishek Soni, Curtis P. Berlinguette
Abstract
Functional coatings are used in a wide range of high surface-area technologies, such as low-E windows and photovoltaics. Solution-based coatings are typically less expensive to produce than vacuum-based coatings; however, it is generally more difficult to produce high-quality coatings using solution-based methods due to lower control over the physical and chemical processes involved. Here, we show how a self-driving laboratory can be used to optimize spray coating parameters. For this demonstration, we optimized the combustion synthesis of spray-cast conductive palladium (Pd) films. The closed-loop optimization yielded films with conductivities of >4 MS/m, which compares favorably with the conductivities of 2–6 MS/m reported for thin Pd films obtained by vacuum-based sputtering processes. The champion coating conditions were scaled up to an 8× larger area using the same spray-coating apparatus while preserving coating quality and conductivity. This work shows how self-driving laboratories can optimize solution-based coatings at scale.