A Self-driving Laboratory Optimizes A Scalable Process For Making Functional Coatings

Connor Rupnow, Benjamin Patrick MacLeod, Mehrdad Mokhtari, Karry Ocean, Kevan E. Dettelbach, Daniel Lin, Fraser Glynn Lintel Parlane, Hsi N. Chiu, Michael B. Rooney, Christopher Waizenegger, Elija de Hoog, Curtis P. Berlinguette

Keywords: Self-driving laboratory, bayesian optimization, autonomous experiments, spray-coating, thin films and coatings

TL;DR: We double the state-of-the-art conductivity for spray-combustion-synthesized Pd films using a self-driving laboratory designed to optimize scalable coating processes.

Abstract: Solution-based coating methods offer low-cost routes to deposit coatings at scale. It is difficult, however, to obtain high quality coatings using these methods due to the complex and dynamic physical and chemical processes involved. Here, we show how a self-driving laboratory can optimize spray-coating, which is relevant to manufacturing a range of clean energy technologies. For this demonstration, we optimized the combustion synthesis of spray-cast conductive palladium films. The closed-loop optimization of this synthesis 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 with no further optimization and no reduction in coating quality or conductivity. This work shows how self-driving laboratories can optimize a scalable process for making functional coatings.

Paper Track: Papers

Submission Category: AI-Guided Design, Automated Chemical Synthesis, Automated Material Characterization