The performance of a photovoltaic device is strongly dependent on the light harvesting properties of the absorber layer as well as the charge separation at the donor/acceptor interfaces. Atomically thin two-dimensional transition metal dichalcogenides (2-D TMDCs) exhibit strong light-matter interaction, large optical conductivity, and high electron mobility; thus they can be highly promising materials for next-generation ultrathin solar cells and optoelectronics. However, the short optical absorption path inherent in such atomically thin layers limits practical applications. A heterostructure geometry comprising 2-D TMDCs (e.g., MoS2) and a strongly absorbing material with long electron-hole diffusion lengths such as methylammonium lead halide perovskites (CH3NH3PbI3) may overcome this constraint to some extent, provided the charge transfer at the heterostructure interface is not hampered by their band offsets. Herein, we demonstrate that the intrinsic band offset at the CH3NH3PbI3/MoS2 interface can be overcome by creating sulfur vacancies in MoS2 using a mild plasma treatment; ultrafast hole transfer from CH3NH3PbI3 to MoS2 occurs within 320 fs with 83% efficiency following photoexcitation. Importantly, our work highlights the feasibility of applying defect-engineered 2-D TMDCs as charge-extraction layers in perovskite-based optoelectronic devices.
Although synaptic behaviours of memristors have been widely demonstrated, implementation of an even simple artificial neural network is still a great challenge. In this work, we demonstrate the associative memory on the basis of a memristive Hopfield network. Different patterns can be stored into the memristive Hopfield network by tuning the resistance of the memristors, and the pre-stored patterns can be successfully retrieved directly or through some associative intermediate states, being analogous to the associative memory behaviour. Both single-associative memory and multi-associative memories can be realized with the memristive Hopfield network.
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