Indoor organic photovoltaic (OPV) cells present a promising technology to fuel smart applications due to their thin, lightweight, and flexible nature, rendering them ideal for wearable and implantable devices. Moreover, they can be manufactured through cost-effective and accessible methods, making them an appealing choice for large-scale production. OPV have the potential to revolutionize the realm of medical applications by providing a dependable and sustainable power source for wearable devices. Using OPVs in indoor settings offers the potential to capture clean energy from easily accessible for indoor light sources, to power self-sufficient devices, improve the visual appeal of designs, support sustainability initiatives, and lower the energy expenses. These indoor organic cells are capable of achieving remarkably high power conversion efficiencies (PCEs), which are fairly comparable with the typical efficiencies of commercial single junction indoor silicon photovoltaic cells, which usually hover around 26%. Notably, indoor OPV cells are seamlessly integrated into wearable devices, to ensure patient comfort and safety. The use of organic materials in the active layer have made the realization of highly efficient, flexible, lightweight, biocompatible, and durable devices possible. In this work, we have used PBDB-TCl:AITC:BTP-eC9 as an active layer to produce a high efficiency for indoor OPV as well as to control and optimize the operating voltage of 1865 series sensors in order to power the smart IoT health monitoring sensors. The OPV cells embedded with smart sensor devices help in monitoring the human (patient) vitals such as blood-pressure, body temperature and other parameters. It is found that the layer of polymers PBDB-TCl : AITC : BTP-eC9 in the device provides an open-circuit voltage of 0.87V, fill factor of 79.4% and a power conversion efficiency of 19.1% under low light intensity. Inspiringly, an output efficiency of more than 26% under 1000lx has been realized.
KEYWORDS: Perovskite, Solar cells, Machine learning, Solar energy, Random forests, Artificial intelligence, Electron transport, Education and training, Data modeling, Copper
Perovskite solar cells (PSCs) are renowned for their efficiency, affordability, and mass manufacturing. However, the performance unpredictability, material sensitivity and stability issues, and optimization limit their practicality. This study includes the challenges related to PSCs and the role of Artificial Intelligence (AI) in their advancement. AI has shown that it can accelerate the PSC's designs by finding creative solutions. The design assistance provided through AI-based methods reduces the experimentation time and need for resources, enabling real-time production monitoring and control. These methods identify performance bottlenecks and forecast the device efficiency in various settings. In this paper, we have simulated three perovskite solar cell devices (MASnI3, FASnI3, and MAGeI3) using SCAPS-1D with ETL as ZnO and HTL as Cu2O. Random Forest technique has been used for optimization and prediction of the best PSCs efficiency where the conduction band density of state, thickness of the absorber layer, hole mobility, valence band density of state, and electron mobility have served as design variables. The MSE and R2 scores for performance prediction are 1.37× 10-3 and 0.992 for MASnI3, 4.21 × 10-3 and 0.997 for FASnI3, and 0.79 × 10-3 and 0.993 for MAGeI3 respectively.
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