Accelerated Photodegradation Study of Multi-Component Organic Photovoltaic Materials with Robotic Micro-Experimentation

With the advent of non-fullerene acceptors (NFA), the efficiency of organic photovoltaics (OPVs) has broken previous records and currently stands at a certified power conversion efficiency (PCE) of 19% [1]. Multi-component OPV blends have been increasingly used to improve the performance and stability of devices [2]. However, there is a lack of understanding of the fundamental mechanisms behind improvements in performance and even less so how these blends stabilize devices. Investigating photodegradation of a vast library of materials and their multi-component combinations is a demanding problem that currently requires considerable effort in terms of time and resources. We take the view that workflow automation combined with micro-experimentation and data-driven and machine learning-guided sample selection allowing for (semi-)autonomous workflows can significantly accelerate photodegradation investigations as well as multiparameter optimization. In this work, we describe a robotic platform, the RoboMapper, which formulates and prints miniature OPV active layer areas on transparent substrates and enables rapid evaluation of photodegradation in inert atmosphere under 4 to 40 suns. Printing on a scale 1/10^th to 1/20^th of the size of traditional samples consumes a fraction of materials, whereas robotic micro-UV-Vis characterization enables high throughput evaluation of photobleaching of all materials printed on chip. Using the RoboMapper, we conducted photodegradation campaigns on neat donor and acceptor materials[AA1] , PM6, PTQ10, Y6, IT-4F, and PC₇₁BM, their binary, and multi-component blends. The performed photodegradation analysis of the vast library of materials showed that all acceptors and donors have different degradation behavior in neat films and blends as compared with different donor-acceptor combinations. Additionally, the data allows us to select the right donor-acceptor combinations for optimal photostability in binary and ternary systems. Our study shows that approaches like the RoboMapper can be powerful for collection of big data equivalent to years of work in just a few weeks or months. The ability to prepare multiple samples simultaneously from a small amount of stock materials also considerably reduces the waste generated for the same quantity and quality of data.<br/><br/>1. Zhu, L., Zhang, M., Xu, J. et al. Nat. Mater. 21, 656–663 (2022).<br/><br/>2. Lingling, Z., Li, S. et al. Energy Environ. Sci. 13, 635-645 (2020).