Solution-processed organic photovoltaic cells (OPVs) hold great promise to enable roll-to-roll printing of environmentally friendly, mechanically flexible and cost-effective photovoltaic devices. Nevertheless, many high-performing systems show best power conversion efficiencies (PCEs) with a thin active layer (thickness is ~100 nm) that is difficult to translate to roll-to-roll processing with high reproducibility. Here we report a new molecular donor, benzodithiophene terthiophene rhodanine (BTR), which exhibits good processability, nematic liquid crystalline behaviour and excellent optoelectronic properties. A maximum PCE of 9.3% is achieved under AM 1.5G solar irradiation, with fill factor reaching 77%, rarely achieved in solution-processed OPVs. Particularly promising is the fact that BTR-based devices with active layer thicknesses up to 400 nm can still afford high fill factor of ~70% and high PCE of ~8%. Together, the results suggest, with better device architectures for longer device lifetime, BTR is an ideal candidate for mass production of OPVs.

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In the process of finding high-performance materials for organic photovoltaics (OPVs), it is meaningful if one can establish the relationship between chemical structures and photovoltaic properties even before synthesizing them. Here, we first establish a database containing over 1700 donor materials reported in the literature. Through supervised learning, our machine learning (ML) models can build up the structure-property relationship and, thus, implement fast screening of OPV materials. We explore several expressions for molecule structures, i.e., images, ASCII strings, descriptors, and fingerprints, as inputs for various ML algorithms. It is found that fingerprints with length over 1000 bits can obtain high prediction accuracy. The reliability of our approach is further verified by screening 10 newly designed donor materials. Good consistency between model predictions and experimental outcomes is obtained. The result indicates that ML is a powerful tool to prescreen new OPV materials, thus accelerating the development of the OPV field.

A fullerene additive adjusts the miscibility between donor and acceptor for morphology optimization and reduces bimolecular recombination, assisting significant improvement of fill factor and efficiency.

Bulk and interfacial nonradiative recombination hinders the further enhancement of power conversion efficiency (PCE) and stability of SnO2-based planar perovskite solar cells (PSCs). To date, it is still a huge...

A possible high efficiency working model of a monolayer PEDOT:PSS film and a brief fabrication process.

Bimolecular recombination in bulk heterojunction organic solar cells is the process by which non-geminate photogenerated free carriers encounter each other, combine to form a charge transfer (CT) state which subsequently relaxes to the ground state. It is governed by the diffusion of the slower and faster carriers towards the electron donor: acceptor interface. In an increasing number of systems, the recombination rate constant is measured to be lower than that predicted by Langevin's model for relative Brownian motion and the capture of opposite charges. Herein, we investigate the dynamics of charge generation, transport and recombination in a nematic liquid crystalline donor: fullerene acceptor system that gives solar cells with initial power conversion efficiencies of >9.5%. Unusually, and advantageously from a manufacturing perspective, these efficiencies are maintained in junctions thicker than 300 nm. Despite finding imbalanced and moderate carrier mobilities in this blend, we observe strongly suppressed bimolecular recombination, which is ~150 times less than predicted by Langevin theory, or indeed, more recent and advanced models that take into account the Received: ((will be filled in by the editorial staff))Revised: ((will be filled in by the editorial staff))

2D & 3D-printing of self-healing gels enable selective deposition of protective coatings and advanced manufacturing of self-rolling, dynamic objects.