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Rupnow, Connor

University of British Columbia

The commercialization of materials can take decades, in part due to the challenge of scaling up laboratory synthesis techniques for manufacturing. For example, perovskite solar cells were discovered in 2009, but are not yet commercially available. Scaling up the solution-based fabrication process is widely cited as the major reason for this. To address this challenge, we have designed and built a self-driving laboratory, “Ada”, for autonomously optimizing ultrasonic spray coating, a scalable coating process. In this thesis, I present two studies that demonstrate the capabilities of our self-driving laboratory. In the first study, I showcase the difficulty of scaling up solution based coatings. Drop cast palladium films were optimized for multiple objectives using Ada, maximizing the conductivity and minimizing the processing temperature. I then scale up several optimized Pd precursor inks by spray coating, yielding films with conductivities of 0.1 – 2.0 MS/m and nearly comparable to those of sputtered films (2 – 6 MS/m). This scale-up process, however, revealed two unanticipated complications: spray process variables needed to be defined without prior knowledge, and non-uniform substrate conditions resulted in non-uniform conductivity across the film. This work demonstrates how a self-driving laboratory can optimize metal coatings in advance of scale up while highlighting the challenges of optimizing materials in non-relevant manufacturing conditions. In the second study, I upgrade Ada with a robotic spray coater and showcase how materials can be more readily scaled up if they are optimized using scalable methods. In this study, I show how our self-driving laboratory can optimize spray coating and precursor inks simultaneously by maximizing the conductivity of spray coated Pd films. The optimization yielded films with conductivities of >4 MS/m, doubling the best conductivity from the previous study and achieving conductivities competitive of those made by sputtering (2 – 6 MS/m). The champion coating conditions were scaled up to an 8× larger area using the same spray coating apparatus with no reduction in coating quality or conductivity. This work shows how self-driving laboratories can optimize a deposition process for making functional coatings at scale.

Text

2024-09-30

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/85381

Chemical and Biological Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/