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Synthesis and characterization of Eu3+-doped Er2SiO5 phosphor are reported. The samples are prepared using the modified conventional solid-state synthesis technique with variable concentrations of doping ions and their structural studies are carried out by X–ray diffraction (XRD). The information on the morphology of the samples prepared with optimized doping ion concentrations is obtained by scanning electron microscopy (SEM). A photoluminescence (PL) analysis of the phosphor samples is also done for the fixed doping ion concentrations. The identified PL emission bands are attributed to the intra 4f transitions of a Eu3+ ion, such as 5D0→7F1 at 590 nm, 5D0→7F0 at 602 and 5D0→7F2 at 613 nm. The corresponding transitions of the doping ions and the concentration quenching effect are studied in detail. The 1931 Commission Internationale de l'éclairage (CIE) (x, y) chromaticity coordinates show the spectral distribution calculated from the PL emission spectrum. A thermoluminescence (TL) glow curve analysis is performed for variable doping ion concentrations, and the corresponding trap parameters are calculated using the Computerized Glow Curve Deconvolution (CGCD) technique. Our study shows that as-prepared phosphor may be useful for display devices. [ABSTRACT FROM AUTHOR]

As the junction temperature of a light-emitting diode (LED) is inversely proportional to its lifespan and efficiency. This study investigates the thermal performance of heat sink-based model with a drop-shaped pin fin for LED cooling under natural convection, experimentally, and numerically. The study used three distinct drop form pin fin arrays with variable vertical fin spacing (Sv = 25, 50, and 75 mm), with the best array being used in subsequent studies with variations in drop-shaped pin fin size (diameter DD and apex length LD). The heat transfer coefficient and Nusselt number were obtained in the quantitative analysis of thermal performance. A numerical model with Boussinesq approximation for natural convection is used for this study and well-validated with experimental results. A qualitative investigation was also carried out utilizing numerical research to investigate velocity streamlines and their impacts on heat transfer enhancement in various configurations. An experimental test with conventional plate fin, circular pin fins were done for the verification of the test setup results and their results were compared with results calculated from the standard correlations presented in previous well-known literature. The maximum heat transfer coefficient was found to be 12.80 W/m2K for drop form pin fins with vertical fin spacing (Sv) equal to 75 mm, a diameter of 6 mm, and a length of 7.2 mm, and the corresponding Nusselt number was 94.85. Also, the maximum enhancement in Nusselt number was found to be (Nu/Nuplate fin) 3.11 corresponding to the conventional plate-fin heat sink. This comparison will help the industrialists determine innovative passive cooling technology for electronic devices such as LED street lights. [ABSTRACT FROM AUTHOR]

This paper introduces the Hybrid Quantum Deep Neural Network (HQDNN), a pioneering model that amalgamates classical Convolutional Neural Network (CNN) architecture with Parameterized Quantum Circuits (PmQC) for image classification tasks. The background elucidates the intricacies of CNN and PmQC operations, emphasizing their respective components and functions. HQDNN comprises quantum convolutional and pooling layers alongside a classical fully connected layer, demonstrating its effectiveness through simulations on the MNIST dataset. Results reveal HQDNN's superior learning ability and classification performance compared to traditional CNN, particularly with the inclusion of a pooling layer. This work contributes to the evolution of quantum machine learning, opening avenues for the practical integration of quantum computing in image classification workflows. Future research may explore scalability and diverse applications of HQDNN, marking a promising convergence of quantum computing and deep learning methodologies. [ABSTRACT FROM AUTHOR]