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Core-shell refractory plasmonic nanoparticles are used as excellent nanoantennas to improve the efficiency of lead-free perovskite solar cells (PSCs). SiO 2 is used as the shell coating due to its high refractive index and low extinction coefficient, enabling the control over the sunlight directivity. An optoelectronic model is developed using 3D finite element method (FEM) as implemented in COMSOL Multiphysics to calculate the optical and electrical parameters of plain and ZrN/SiO 2 -modified PSCs. For a fair comparison, ZrN-decorated PSCs are also simulated. While the decoration with ZrN nanoparticles boosts the power conversion efficiency (PCE) of the PSC from 12.9% to 17%, the use of ZrN/SiO 2 core/ shell nanoparticles shows an unprecedented enhancement in the PCE to reach 20%. The enhancement in the PCE is discussed in details.www.nature.com/scientificreports www.nature.com/scientificreports/ model. First, the largest value allowed for mesh size must not exceed λ/10 of the smallest simulated wavelength. Therefore, a tetrahedron shape was selected for meshing the solar cell active layers, where the smaller the mesh size, the more accurate the computational data compared to the practical counterpart. However, this comes on the expense of the computational time. Second, in order to decrease the computational time, a mapped meshing with normal or coarse meshing was applied to the PMLs surrounding the solar cell in order to allow the use of smaller mesh size for the solar cell and the active material. Third, the G opt was integrated to the full simulated bandwidth and used as an input data profile to construct the electrical model. In the electrical model, any enhancements in light absorption was assumed to be directly proportional to the enhancements in the photocurrent as shown by Eq. 9. The electrical parameters of TiO 2 , MASnI 3 , and Spiro-OMeTAD were extracted from literature 49,50 . Scientific RepoRtS |(2020) 10:6732 | https://doi.

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Fuzzy logic, unlike the crispy logic in Boolean theory, deals with uncertain or imprecise situations. Afuzzy control algorithm for a process control system embeds the intuition and experience of an operator, designer and researcher. The control does not need accurate mathematical model of a plant, and therefore, it suits well to a process where the model is unknown or illdefined. The fuzzy control also works well for complex nonlinear multidimensional system, system with parameter variation problem, or where the sensor signals are not precise /1/(1995), /2/(1994). Fuzzy logic is recently finding wide popularity in various applications that include management, economics and process control. In this paper, the basic configuration of the fuzzy logic controller (FLC) is introduced and the component functions are illustrated. The proposed block diagram of the PVBD hybrid system control is configured. The system controller manipulated variable as well as the actuators were clearly defined where all the system disturbances are included in the proposed block diagram. The system operation algorithm as well as the FLC algorithm is introduced. The FLCinputvariables membership fimctions as well as the output consequences are illustrated with their universe of discourses. The FLC is tested and simulatedbyfuzzy logic toolbox under MatLab using first order with delay time process model under variables input signals. The FLC is implemented in the 805 1 series microcontroller based on the low level language (assembly) usage. The normalization of the input sensed signals, output commands as well as the signal conditioning circuits are developed and implemented.

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