Mn₂O₃ nanomaterials have been recently composing a variety of electrochemical systems like fuel cells, supercapacitors, etc., due to their high specific capacitance, low cost, abundance and environmentally benign nature. In this work, mesoporous Mn₂O₃ nanoparticles (NPs) were synthesized by manganese acetate, citric acid and sodium hydroxide through a hydrothermal process at 150 °C for 3 h. The synthesized mesoporous Mn₂O₃ NPs were thoroughly characterized in terms of their morphology, surfaces, as well as their crystalline, electrochemical and electrochemical properties. For supercapacitor applications, the synthesized mesoporous Mn₂O₃ NP-based electrode accomplished an excellent specific capacitance (C_sp) of 460 F·g⁻¹ at 10 mV·s⁻¹ with a good electrocatalytic activity by observing good electrochemical properties in a 6 M KOH electrolyte. The excellent C_sp might be explained by the improvement of the surface area, porous surface and uniformity, which might favor the generation of large active sites and a fast ionic transport over the good electrocatalytic surface of the Mn₂O₃ electrode. The fabricated supercapacitors exhibited a good cycling stability after 5000 cycles by maintaining ~83% of C_sp.

Novel and highly effective composite electrolytes were prepared by combining the two dimensional graphene (Gra) and polyethylene oxide (PEO) for the solid electrolyte of dye sensitized solar cells (DSSCs). Gra sheets were uniformly coated by the polymer layer through the ester carboxylate bonding between oxygenated species on Gra sheets and PEO. The Gra–PEO composite electrolyte showed the large scale generation of iodide ions in a redox couple. From rheological analysis, the decrease in viscosity after the addition of LiI and I₂ in the Gra–PEO electrolyte might be explained by the dipolar interactions being severely disrupted by the ionic interactions of Li⁺, I⁻, and I₃ ⁻ ions. A composite electrolyte with 0.5 wt% Gra presented a higher ionic conductivity (3.32 mS cm⁻¹) than those of PEO and other composite electrolytes at room temperature. A high overall conversion efficiency (∼5.23%) with a very high short circuit current (J_SC) of 18.32 mA cm⁻², open circuit voltage (V_OC) of 0.592 V and fill factor (FF) of 0.48 was achieved in DSSCs fabricated with the 0.5 wt% Gra–PEO composite electrolyte. This enhanced photovoltaic performance might be attributed to the large scale formation of iodide ions in the redox electrolyte and the relatively high ionic conductivity.

Highly crystalline Pr6O11 nanorods were prepared by a simple precipitation method of triethylamine complex at 500°C. Synthesized Pr6O11 nanorods were uniformly grown with the diameter of 12–15 nm and the length of 100–150 nm without any impurities of unstable PrO2 phase. The Pr6O11 nanorod electrodes attained a high electrical conductivity of 0.954 Scm−1 with low activation energy of 0.594 eV at 850°C. The electrochemical impedance study showed that the resistance of electrode was significantly decreased at high temperature, which resulted from its high conductivity and low activation energy. The reduced impedance and high electrical conductivity of Pr6O11 nanorod electrodes are attributed to the reduction of grain boundaries and high space charge width.

This paper describes the unique antireflection (AR) layer of vertically arranged ZnO nanorods (NRs) on crystalline silicon (c-Si) solar cells and studies the charge transport and photovoltaic properties by simulation. The vertically arranged ZnO NRs were deposited on ZnO-seeded c-Si wafers by a simple low-temperature solution process. The lengths of the ZnO NRs were optimized by changing the reaction times. Highly dense and vertically arranged ZnO NRs were obtained over the c-Si wafer when the reaction time was 5 h. The deposited ZnO NRs on the c-Si wafers exhibited the lowest reflectance of ~7.5% at 838 nm, having a reasonable average reflectance of ~9.5% in the whole wavelength range (400–1000 nm). Using PC1D software, the charge transport and photovoltaic properties of c-Si solar cells were explored by considering the lengths of the ZnO NRs and the reflectance values. The 1.1 μm length of the ZnO NRs and a minimum average reflectance of 9.5% appeared to be the optimum values for achieving the highest power conversion efficiency of 14.88%. The simulation study for the vertically arranged ZnO NRs AR layers clearly reflects that the low-temperature deposited ZnO NRs on c-Si solar cells could pose a greater prospect in the manufacturing of low-cost c-Si solar cells.

This paper numerically explores the possibility of ultrathin layering and high efficiency of graphene as a back surface field (BSF) based on a CdTe solar cell by Personal computer one-dimensional (PC1D) simulation. CdTe solar cells have been characterized and studied by varying the carrier lifetime, doping concentration, thickness, and bandgap of the graphene layer. With simulation results, the highest short-circuit current (I_sc = 2.09 A), power conversion efficiency (η = 15%), and quantum efficiency (QE~85%) were achieved at a carrier lifetime of 1 × 10³ μs and a doping concentration of 1 × 10¹⁷ cm⁻³ of graphene as a BSF layer-based CdTe solar cell. The thickness of the graphene BSF layer (1 μm) was proven the ultrathin, optimal, and obtainable for the fabrication of high-performance CdTe solar cells, confirming the suitability of graphene material as a BSF. This simulation confirmed that a CdTe solar cell with the proposed graphene as the BSF layer might be highly efficient with optimized parameters for fabrication.

Solar power generation is usually affected by different meteorological factors, such as solar radiation, cloud cover, rainfall, and temperature. This variability has shown a negative impact on the large-scale integration of solar energy into energy supply systems. For successful integration of solar energy into the electrical grid, it is necessary to predict the accurate power generation by solar panels. In this work, solar power generation forecasting for two types of solar system (non-transparent and transparent panels) was configured by the smart artificial intelligence (AI) modelling. For deep learning models, the dataset obtained from the target value of electricity generation in kWh and other features, such as weather conditions, solar radiance, and insolation. In PV power generation values from non-transparent and transparent solar panels were collected from 1 January to 31 December 2021 with an hourly interval. To prove the efficiency of the proposed model, several deep learning approaches RNN models, such as LSTM, GRU, and transformers models, were implemented. Transformers model for forecasting power generation expressed the best model for non-transparent and transparent solar panels with lower error rates for MAE 0.05 and 0.04, and RMSE 0.24 and 0.21, respectively. The proposed model showed efficient performance and proved effective in forecasting time-series data.