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.

Magnetic Refrigeration with Recycled Permanent Magnets and Free Rare-Earth Magnetocaloric La-Fe-Si.

Magnetic refrigeration is an upcoming technology that could be an alternative to the more than 100-year-old conventional gas-vapor compression cooling. Magnetic refrigeration might answer some of the global challenges linked with the increasing demands for readily available cooling in almost every region of the world and the global-warming potential of conventional refrigerants. Important issues to be solved are, for example, the required mass and the ecological footprint of the rare-earth permanent magnets and the magnetocaloric material, which are key parts of the magnetic cooling device. The majority of existing demonstrators use Nd-Fe-B permanent magnets, which account for more than 50% of the ecological footprint, and Gd, which is a critical raw material. This work shows a solution to these problems by demonstrating the world's first magnetocaloric demonstrator that uses recycled Nd-Fe-B magnets as the magnetic field source, and, as a Gd replacement material, La-Fe-Mn-Si for the magnetocaloric heat exchanger. These solutions show that it is possible to reduce the ecological footprint of magnetic cooling devices and provides magnetic cooling as a green solid-state technology that has the potential to satisfy the rapidly growing global demands.