A modification of the Shockley-Queisser theory for organic heterojunctions is presented with a special focus on constellations, where a linear extrapolation of the temperature dependence of the open circuit voltage results in the optical gap of the absorber rather than in the intermolecular charge transfer (CT) gap. We demonstrate that, depending on the electronic coupling strength between donor and acceptor molecules, either singlet or CT recombination is dominant in different temperature regimes. The different regimes are separated by a transition temperature that is usually well above room temperature. However, in the case of small energy level offset and weak electronic coupling, it can be around 300 K or even below. We point out that a linear extrapolation of the open circuit voltage V OC towards 0 K for measured temperatures larger than the transition temperature results in a photovoltaic gap that is close to the optical gap, whereas for values below the transition temperature the CT gap will be extracted. We show that for α-sexithiophene (6T)/diindenoperylene (DIP) solar cells heating the substrate during 6T deposition leads to a molecular configuration at the interface where the coupling between donor and acceptor molecules is strongly reduced. This leads to a transition temperature well below room temperature which is confirmed by temperature dependent electroluminescence measurements. By comparing the temperature dependent spectra of high temperature and room temperature grown 6T/DIP solar cells to the spectra of the individual materials, the different contributions from the charge transfer gap and the optical gap are separated.

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Carbon fiber reinforced polymers (CFRP) are lightweight but strong composite materials designed to reduce the weight of aerospace or automotive components -contributing to reduced greenhouse gas emissions. A common manufacturing process for carbon fiber tapes consists of aligning tows (bundles of carbon fiber filaments) side by side to form tapes via a spreading machine. Tows are pulled across metallic spreading bars that are conventionally kept in a fixed position. That can lead to high variations in quality metrics such as tape width or height. Alternatively, one could try to control the spreading bars based on the incoming tows' profiles. We investigate whether a machine learning approach, consisting of a supervised process model trained on real data and a process control model to choose adequate spreading bar positions, is able to improve the tape quality variations. Our results indicate promising tendencies for adaptive tow spreading.