Photodegradation of Organic Solar Cells under Visible Light and the Crucial Influence of Its Spectral Composition.

While wavelength-dependent photodegradation of organic solar cells (OSCs) under visible light is typically discussed in terms of UV/blue light-activated phenomena, we recently demonstrated wavelength-dependent degradation rates up to 660 nm for PM6:Y6. In this study, we systematically investigated this phenomenon for a broad variety of devices based on different donor:acceptor combinations. We found that the spectral composition of the light used for degradation, tuned in a spectral range from 457 to 740 nm and under high irradiances of up to 30 suns, has a crucial influence on the device stability of almost all tested semiconductors. The relevance of this phenomenon was investigated in the context of simulated AM1.5 illumination with metal halide lamps and white LEDs. It is concluded that the current stability testing protocols in OSC research have to be adjusted to account for this effect to reveal the underlying physics of this still poorly understood mechanism.

Understanding Causalities in Organic Photovoltaics Device Degradation in a Machine-Learning-Driven High-Throughput Platform.

Organic solar cells (OSCs) now approach power conversion efficiencies of 20%. However, in order to enter mass markets, problems in upscaling and operational lifetime have to be solved, both concerning the connection between processing conditions and active layer morphology. Morphological studies supporting the development of structure-process-property relations are time-consuming, complex, and expensive to undergo and for which statistics, needed to assess significance, are difficult to be collected. This work demonstrates that causal relationships between processing conditions, morphology, and stability can be obtained in a high-throughput method by combining low-cost automated experiments with data-driven analysis methods. An automatic spectral modeling feeds parametrized absorption data into a feature selection technique that is combined with Gaussian process regression to quantify deterministic relationships linking morphological features and processing conditions with device functionality. The effect of the active layer thickness and the morphological order is further modeled by drift-diffusion simulations and returns valuable insight into the underlying mechanisms for improving device stability by tuning the microstructure morphology with versatile approaches. Predicting microstructural features as a function of processing parameters is decisive know-how for the large-scale production of OSCs.

Molecular Oligothiophene-Fullerene Dyad Reaching Over 5% Efficiency in Single-Material Organic Solar Cells.

A novel donor-acceptor dyad, 4, in which the conjugated oligothiophene donor is covalently connected to fullerene PC71 BM by a flexible alkyl ester linker, is synthesized and applied as photoactive layer in solution-processed single-material organic solar cells (SMOSCs). Excellent photovoltaic performance, including a high short-circuit current density (JSC ) of 13.56 mA cm-2 , is achieved, leading to a power conversion efficiency of 5.34% in an inverted cell architecture, which is substantially increased compared to other molecular single materials. Furthermore, dyad 4-based SMOSCs display excellent stability maintaining 96% of the initial performance after 750 h (one month) of continuous illumination and operation under simulated AM 1.5G irradiation. These results will strengthen the rational molecular design to further develop SMOSCs for potential industrial application.