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Tandem solar cells (TSCs) perform a better adaptation of the incident photons in different-energy-level bandgap materials, and overcome the Shockley–Queisser limit, but they require advanced control over the management of light for optimum performance. Nanomaterials and nanostructures offer a vastly improved control over the management of light. Through different optimization techniques, researchers can gain valuable insights regarding the optimization of various parameters of nano-optical designs. Over the past years, the number of studies on this topic has been continuously increasing. The present study reviews various current state-of-the-art optical designs, and provides an overview of the optimization techniques and numerical modeling of TSCs. This paper collected and analyzed different studies published within the years 2015–2022, using systematic literature review techniques, such as specific protocol screening and a search strategy. Seven different optical designs were extracted, along with their advanced local and global optimization methods, which offer a solution to the optical limitations of TSCs. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a complete ecosystem encompassing various communication technologies, sensors, hardware, and software. IoT cutting-edge technologies and Artificial Intelligence (AI) have enhanced the traditional healthcare system considerably. The conventional healthcare system faces many challenges, including avoidable long wait times, high costs, a conventional method of payment, unnecessary long travel to medical centers, and mandatory periodic doctor visits. A Smart healthcare system, Internet of Things (IoT), and AI are arguably the best-suited tailor-made solutions for all the flaws related to traditional healthcare systems. The primary goal of this study is to determine the impact of IoT, AI, various communication technologies, sensor networks, and disease detection/diagnosis in Cardiac healthcare through a systematic analysis of scholarly articles. Hence, a total of 104 fundamental studies are analyzed for the research questions purposefully defined for this systematic study. The review results show that deep learning emerges as a promising technology along with the combination of IoT in the domain of E-Cardiac care with enhanced accuracy and real-time clinical monitoring. This study also pins down the key benefits and significant challenges for E-Cardiology in the domains of IoT and AI. It further identifies the gaps and future research directions related to E-Cardiology, monitoring various Cardiac parameters, and diagnosis patterns. [ABSTRACT FROM AUTHOR]

Tandem Solar cells are composed of multiple layers with varying bandgap materials to facilitate the absorption of a wider range of solar energy. However, their performance is hampered by interface losses leading to current mismatching. To overcome these limitations, tandem solar cells often require the use of mirrors and lenses to concentrate and trap light to achieve optimal efficiency. Recently, the use of nanoscale 2D meta-materials has emerged as a promising alternative to traditional lenses. However, the optical optimization of these meta-materials can be both time-consuming, resources, and space-intensive, posing a challenge to their implementation. This study explores the use of deep learning to predict the optimal optical design for the top cell in tandem solar cells to maximize power conversion efficiency. Computational techniques are used to analyze the optical responses of metasurfaces. 2D-Convolutional Neural Networks (CNN) are used to train a dataset of 10,578 T i O 2 / C H 3 N H 3 P B r 3 / Z n O metasurfaces. Nine different CNN models were used with different architectures to identify the best hyperparameters that give the low mean square error. CNN displayed fast high prediction accuracy taking an average of 0.3 ± 0.05 seconds per prediction. The Deep SHapley Additive Explanations (SHAP) algorithm was used to gain insights into CNN's predictions and understand the behavior of complex metasurfaces integrated into a typical reference tandem solar cell architecture. The proposed metasurfaces can significantly enhance the efficiency of tandem solar cells. The active layer comprises of near 90% absorption of the solar spectrum. The average absorption of the top cell increased in the UV-vis region (650-800nm) up to 93.4%. • We trained 2D-CNN to predict the optimal metasurface for tandem solar cells. • CNN predictions were generated much faster, taking an average of 0.3 ± 0.05 seconds per prediction. • The average absorption of the top cell increased in the UV-vis region (650-800 nm) up to 93.4%. • The J sc in the top cell increased by 2 mA / c m 2 by integrating the optimized metasurface. [ABSTRACT FROM AUTHOR]