Enhancing photovoltaic performance by tuning the domain sizes of a small-molecule acceptor by side-chain-engineered polymer donors†
Abstract
This paper reports two new fluorine-substituted polymer donors (BO2FC8, BO2FEH), with different side-chain architectures, and a new chlorine-substituted small-molecule acceptor (m-ITIC-OR-4Cl) that are capable of simultaneous charge and energy transfer as the binary blend active layer for organic photovoltaics. We first resolved the single-crystal structure of m-ITIC-OR-4Cl and then used simultaneous grazing-incidence wide- and small-angle X-ray scattering to decipher the multi-length-scale structures—such as the shape and size of aggregated domains and molecular orientation—of the blends of BO2FEH and BO2FC8 with m-ITIC-OR-4Cl. The linear side chains of BO2FC8 facilitated its packing and, thus, induced m-ITIC-OR-4Cl to form smaller disc-shaped aggregated domains (thickness: 2.9 nm) than its aggregate domain (thickness: 5.4 nm) in the blend of the branched BO2FEH. That is, the binary blend system of linear-side-chain BO2FC8 with m-ITIC-OR-4Cl featured larger interfacial areas and more pathways for charge transfer and transport, as evidenced by their carrier mobilities. The highest power conversion efficiency (PCE) of 11.0% was that for the BO2FC8:m-ITIC-OR-4Cl device, being consistent with the predicted PCE of 11.2% using machine learning based on random forest algorism; in comparison, the PCE of the BO2FEH:m-ITIC-OR-4Cl device was 6.4%. This study has not only provided insight into the photovoltaic performances of new polymer donor/small-molecule acceptor blends but has also, for the first time, deciphered the hierarchical morphologies—from molecule orientation to nano-domain shape and size—of such blend systems, linking the morphologies to the photovoltaic performances. The use of side-chain architectures suggests an approach for tuning the morphology of the polymer/small-molecule binary blend active layer for use in organic photovoltaics.
- This article is part of the themed collections: Editor’s Choice: Machine Learning for Materials Innovation and 2019 Journal of Materials Chemistry A HOT Papers