Proceedings of nanoGe September Meeting 2017 (NFM17)
Publication date: 20th June 2016
Organic semiconductors (OSCs) have found numerous applications in thin film electronic devices, including displays, sensors, lighting, and solar cells. OSCs offer the advantage that they can be modified by synthetic chemists to fulfill specific needs, and that they are not limited to being made from single elements or compositional alloys. For example, organic photovoltaics (OPVs) have reached nearly 13% power conversion efficiency (PCE) in small area devices using traditional polymer-fullerene blends, yet non-polymer and non-fullerene composites are now also showing PCEs above 10%. The flexibility offered by synthetic manipulation also presents a challenge: progress towards the discovery of next-generation, high-performance materials can be stifled by the bottleneck of device optimization through process engineering. High-throughput screening techniques that provide high fidelity performance metrics can circumvent this problem and will become important tools to accelerate material development. Here, we introduce such a tool, based on unique microwave conductivity capabilities, and illustrate a cost- and time-effective approach to evaluate the potential of promising new materials. We demonstrate the power of this approach, by correlating figures of merit from our screening tool to the OPV device performance for a library of current state-of-the-art OSCs, based on both polymer and small molecule chemical structure motifs. In the context of polymeric materials, we show that our screening process is independent of the processing conditions used to form thin films (something that cannot be said of device-based screening approaches) and we highlight the sensitivity of the screening process to physicochemical properties (e.g., molecular weight), suggesting that our tool can even be employed for batch-to-batch quality control. Finally, we will show that, in addition to the microwave conductivity figures of merit, other rapidly-accessed material properties must also be considered prior to devoting time and labor to device processing and architecture optimization. We assert that our approach has the potential to save significant effort by focusing attention on optimizing the performance of the most promising candidate materials.
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