O. Bong Yang

The flammability and volatility of conventional lithium hexafluorophosphate (LiPF₆)-based electrolytes with organic carbonate solvents remain critical issues to the safety and thermal stability of lithium-ion batteries (LIBs). This study investigates the incorporation of phosphate-based additives including ammonium dihydrogen phosphate (ADP), trimethyl phosphate (TMP), and trimethyl phosphite (TMPi) into LiPF₆ electrolytes for improving the ionic conductivity, safety, and electrochemical performance of LIBs. Self-extinguishing time (SET) measurements demonstrated that the ADP-based LiPF₆ electrolyte significantly reduced flammability, achieving a shorter SET of 04 min 53 s, compared to 12 min for the pristine LiPF₆ electrolyte. The ADP-based LiPF₆ electrolyte possessed the highest ionic conductivity (14.08 mS·cm⁻¹) with an excellent lithium-ion transference number of 0.0076. Electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (C-V) analyses demonstrated that ADP lowered interfacial resistance and stabilized long-term cycling behavior. In particular, the 1% ADP-based LiPF₆ electrolyte maintained improved charge-discharge profiles and Coulombic efficiency over 200 cycles. These results highlight ADP’s dual functionality in suppressing gas-phase flammability and enhancing condensed-phase electrochemical stability, making it a promising candidate for next-generation, high-safety, high-performance LIB electrolytes.

Significant focus has been directed towards inorganic perovskite solar cells because of their notable capabilities in converting sunlight to electricity effectively, their efficient light absorption, and their suitability for conventional semiconductor manufacturing methods. The identification of the composition of perovskite materials is an ongoing challenge to achieve high performing solar cells. Conventional methods of trial and error frequently prove insufficient, especially when confronted with a multitude of potential candidates. In response to this challenge, the suggestion is to employ a machine-learning strategy for more precise and efficient prediction of the characteristics of new inorganic perovskite materials. This work utilized a dataset sourced from the Materials Project database, consisting of 1528 ABX₃ materials with varying halide elements (X = F, Cl, Br, Se) and information regarding their bandgap characteristics, including whether they are direct or indirect. By leveraging data augmentation and machine learning (ML) techniques along with a collection of established bandgap values and structural attributes, our proposed model can accurately and rapidly predict the bandgap of novel materials, while also identifying the key elements that contribute to this property. This information can be used to guide the discovery of new organic perovskite materials with desirable properties. Six different machine learning algorithms, including Logistic Regression (LR), Multi-layer Perceptron (MLP), Decision Tree (DT), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), were used to predict the direct bandgap of potential perovskite materials for this study. RF yielded the best experimental outcomes according to the following metrics: F1-score, Recall, and Precision, attaining scores of 86%, 85%, and 86%, respectively. This result demonstrates that ML has great potential in accelerating organic perovskites material discovery.

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.

Anti-reflective coating (ARC) layers on silicon (Si) solar cells usually play a vital role in the amount of light absorbed into the cell and protect the device from environmental degradation. This paper reports on the thickness optimization of hafnium oxide (HfO₂) as an ARC layer for high-performance Si solar cells with PC1D simulation analysis. The deposition of the HfO₂ ARC layer on Si cells was carried out with a low-cost sol-gel process followed by spin coating. The thickness of the ARC layer was controlled by varying the spinning speed. The HfO₂ ARC with a thickness of 70 nm possessed the lowest average reflectance of 6.33% by covering wavelengths ranging from 400–1000 nm. The different thicknesses of HfO₂ ARC layers were used as input parameters in a simulation study to explore the photovoltaic characteristics of Si solar cells. The simulation findings showed that, at 70 nm thickness, Si solar cells had an exceptional external quantum efficiency (EQE) of 98% and a maximum power conversion efficiency (PCE) of 21.15%. The thicknesses of HfO₂ ARC considerably impacted the photovoltaic (PV) characteristics of Si solar cells, leading to achieving high-performance 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.

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.

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.