Nanocarbon materials, such as graphene and carbon nanotubes (CNTs), have attracted considerable attention as the main or supplementary components in various optoelectronics boosting the device performance and improving the process conditions. Specifically, their application to perovskite solar cells, which are among the most promising photovoltaic devices acknowledged for eco‐friendly energy generation, has significantly impacted the current standing of metal halide perovskite‐based devices. The uniqueness of the nanocarbon applications can be attributed to their outstanding optical, electrical, chemical and mechanical properties, which conventional materials do not possess. This review overviews past and present reports on graphene‐ and CNT‐incorporated perovskite solar cells. Versatile roles and various synthetic methodologies of the applied nanocarbons in perovskite solar cells, including the material growth methods and sources, and functions as transparent electrodes, charge‐transporting layers, interfacial layers, additives and encapsulants, are categorized and graphically illustrated. The discussion expands from single‐junction to tandem applications with silicon solar cells, where the nanocarbon materials also play an equally important yet divergent function. Applications of each graphene and CNTs to the silicon‐perovskite tandem solar cells are interpreted in terms of what roles they play and how they solve the conventional problems. This review serves as the guideline for the photovoltaics researchers in advancing devices using nanocarbons.
Most chemical vapor deposition (CVD) systems used for graphene growth mainly employ convection and radiation heat transfer between the heating source and the metal catalyst in order to reach the activation temperature of the reaction, which in general leads to a long synthesis time and poor energy efficiency. Here, we report a highly time- and energy-efficient CVD setup, in which the metal catalyst (Cu) is designed to be physically contacted with a heating source to give quick heat transfer by conduction. The induced conduction heating enabled the usual effects of the pretreatment and annealing of Cu (i.e., annihilation of surface defects, impurities and contaminants) to be achieved in a significantly shorter time compared to conventional CVD. Notably, the rapid heating was observed to lead to larger grains of Cu with high uniformity as compared to the Cu annealed by conventional CVD, which are believed to be beneficial for the growth of high quality graphene. Through this CVD setup, bundles of high quality (∼252 Ω per square) and large area (over 16 inch) graphenes were able to be readily synthesized in 40 min in a significantly efficient way. When considering ease of scalability, high energy effectiveness and considerable productivity, our method is expected to be welcomed by industrialists.
Synaptic devices, which are considered as one of the most important components of neuromorphic system, require a memory effect to store weight values, a high integrity for compact system, and a wide window to guarantee an accurate programming between each weight level. In this regard,
memristive devices such as resistive random access memory (RRAM) and phase change memory (PCM) have been intensely studied; however, these devices have quite high current-level despite their state, which would be an issue if a deep and massive neural network is implemented with these devices
since a large amount of current-sum needs to flow through a single electrode line. Organic transistor is one of the potential candidates as synaptic device owing to flexibility and a low current drivability for low power consumption during inference. In this paper, we investigate the performance
and power consumption of neuromorphic system composed of organic synaptic transistors conducting a pattern recognition simulation with MNIST handwritten digit data set. It is analyzed according to threshold voltage (VT) window, device variation, and the number of available
states. The classification accuracy is not affected by VT window if the device variation is not considered, but the current sum ratio between answer node and the rest 9 nodes varies. In contrast, the accuracy is significantly degraded as increasing the device variation; however,
the classification rate is less affected when the number of device states is fewer.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.