A self-driving laboratory optimizes a scalable process for making functional coatings

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 83 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.