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In this paper, we report on nanodisc-shaped MgCo2O4 wrapped with ZnS, achieved using the sol–gel-assisted hydrothermal method. This enhances the electrochemical performance, with the electrode delivering superior supercapacitive performance compared to MgCo2O4. Moreover, the nanodisc provides more active sites and allows smooth charge transfer during faradaic reactions. The nanodisc-shaped MgCo2O4 with ZnS delivers a capacitance of approximately 910 F/g at 1 A/g. The fabricated asymmetric capacitor is composed of MgCo2O4@ZnS and activated carbon (AC). The nanodisc-shaped MgCo2O4@ZnS provides more active sites and allows the smooth transport of electrons during long-term cycling. In addition, the electrode side reactions and electrolyte decomposition are significantly reduced due to the ZnS coating on the surface of the MgCo2O4, allowing this asymmetric capacitor to deliver an energy density of 43 Wh·kg−1 at 1454 W·kg−1. The performance of the asymmetric capacitor exhibits enhanced supercapacitive performance and opens a new way to investigate asymmetric supercapacitor devices.

A novel copper-zinc-manganese trimetal oxide nanocomposite was synthesized by the simple co-precipitation method for sensing glucose and methylene blue degradation. The absorption maximum was found by ultraviolet–visible spectroscopy (UV-Vis) analysis, and the bandgap was 4.32 eV. The formation of a bond between metal and oxygen was confirmed by Fourier Transform Infrared Spectroscopy (FT-IR) analysis. The average crystallite size was calculated as 17.31 nm by X-ray powder diffraction (XRD) analysis. The morphology was observed as spherical by scanning electron microscope (SEM) and high-resolution transmission electron microscopy (HR-TEM) analysis. The elemental composition was determined by Energy Dispersive X-ray Analysis (EDAX) analysis. The oxidation state of the metals present in the nanocomposites was confirmed by the X-ray photoelectron spectroscopy (XPS) analysis. The hydrodynamic diameter and zeta potential of the nanocomposite were 218 nm and −46.8 eV, respectively. The thermal stability of the nanocomposite was analyzed by thermogravimetry-differential scanning calorimetry (TG-DSC) analysis. The synthesized nanocomposite was evaluated for the electrochemical glucose sensor. The nanocomposite shows 87.47% of degradation ability against methylene blue dye at a 50 µM concentration. The trimetal oxide nanocomposite shows potent activity against Escherichia coli. In addition to that, the prepared nanocomposite shows strong antioxidant application where scavenging activity was observed to be 76.58 ± 0.30, 76.89 ± 0.44, 81.41 ± 30, 82.58 ± 0.32, and 84.36 ± 0.09 % at 31, 62, 125, 250, and 500 µg/mL, respectively. The results confirm the antioxidant potency of nanoparticles (NPs) was concentration dependent.

Double perovskites are widely recognized in the scientific community for their non-toxic, low-cost, efficient, and stable energy-harvesting properties. This study uses density functional theory and Boltzmann transport theory to explore the physical properties of Cs 2 AuXZ 6 (X = Al/Ga, Z = Cl/Br). The structural stability of the material is confirmed by calculating Goldschmidt's tolerance factor. Formation energy values are predicted to assess the synthesizability and thermal stability of Cs 2 AuXZ 6 . Mechanical properties are analyzed in detail to examine elasticity, ductility, strength, and anisotropy. The obtained elastic constants are further used to calculate thermodynamic parameters for predicting thermal behavior. The electronic properties are evaluated, revealing that Cs 2 AuAlCl 6 , Cs 2 AuAlBr 6 , and Cs 2 AuGaCl 6 exhibit semiconductor characteristics with band gaps of 1.86 eV, 0.89 eV, and 0.84 eV, respectively, while Cs 2 AuGaBr 6 is found to exhibit metallic behavior. These compounds are promising candidates for optoelectronic applications due to their high absorption coefficients (10 4  cm⁻ 1 ), low reflection (below 0.20), and substantial optical conductivity within the visible spectrum. These materials also show considerable potential for heat energy conversion, exhibiting high power factors and figures of merit (0.72, 0.69, and 0.68) for these double perovskite halides. These findings suggest that these materials are promising for optoelectronic and thermoelectric applications.

Prediction of new materials is crucial for the advancement of technology. Here, in this research work, the first-principle computation has been conducted utilizing the WIEN2K package to probe the structural, electronic, mechanical, and optical properties of barium-based chloroperovskites BaMCl3 (M = Ag, Cu) compounds. The optimized lattice constants are calculated for both compounds which are 9.90 Bohr for BaAgCl3 and 9.38 Bohr for BaCuCl3. To obtain better and more precise results for the electronic band’s structure, TDOS and PDOS (total and partial density of states), and the TB-mBJ potential approximation are employed. The indirect band gap (R–Γ) is found for both compounds having values of 1.173 eV and 2.30 eV for BaCuCl3 and BaAgCl3, respectively, which depicts its semiconducting nature. The calculation of elastic properties is conducted with IRelast code. The Cauchy pressure, Bulk modulus, Young’s modulus, Shear modulus, anisotropic ratio, Kleinman parameters, and Poisson’s ratio are calculated from the obtained elastic constants. The computation of elastic parameters indicates that the interested chloroperovskites are anisotropic, mechanically stable, hard to scratch, and ductile. From 0 eV to 40 eV incident photon energy ranges, the various optical parameter such as refractive index, absorption coefficient, dielectric function, reflectivity, extinction coefficient, and optical conductivity are analyzed. These compounds absorb maximum light within 5 to 25 eV incident photon energy. Hence, these materials are good light absorbers, therefore, they can be used in optoelectronic devices for high-frequency applications.

Spatial–temporal forecastability challenges solar radiation forecasting, making it difficult to predict the energy generation from solar photovoltaic-based smart buildings. Accurate forecasting is essential to align the energy generation, storage, and consumption in terms of synchrony and helps achieve efficiency, reliability, and robustness in the grid. The novelty of the research is the formation of a hybrid forecasting model that combines signal processing, neural forecasting, and knowledge-induced clustering techniques to resolve such forecasting complexities under varied atmospheric conditions. The model combines Discrete Orthogonal S-Transform (DOST) for signal decomposition, Nonlinear Auto-Regressive Exogenous Neural Network (NARXNN) for forecasting the decomposed signals, and Knowledge-induced Multiple Kernel Fuzzy Clustering combined with the Green Chameleon Algorithm (KMKFC-GCA) for clustering atmospheric conditions. MATLAB-based simulation and comparison reveal that the hybrid model offers improved performance with a root mean square error of 0.15, a mean absolute error of 0.12, and an R 2 value of 0.99 compared to standard models like the conventional model. The model provides prediction errors within ± 5%, offering a stable and precise solution for optimizing energy-efficient building systems and supporting sustainable energy management.

Solar radiation is the main source of energy on Earth and plays a major role in the hydrological cycles, surface radiation balance, weather and climate changes, and vegetation photosynthesis. Accurate solar radiation prediction is of paramount importance for both climate research and the solar industry. This prediction includes forecasting techniques and advanced modeling to evaluate the amount of solar energy available at a specific location during a given period. Solar energy is the cheapest form of clean energy, and due to the intermittent nature of the energy, accurate forecasting across multiple timeframes is necessary for efficient generation and demand management. Solar radiation prediction using deep learning (DL) includes the applications of neural network methods, namely Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM) models, to forecast and model solar irradiance patterns. By leveraging meteorological variables and historical solar radiation data, DL algorithms can capture complex spatial and temporal dependencies, resulting in accurate predictions. This article presents a novel Solar Radiation Prediction model utilizing a Boosted Coyote Optimization Algorithm with Deep Learning (SRP-BCOADL). The SRP-BCOADL model initially normalizes the input data using a min-max normalization approach to improve the robust nature under different scales. Besides, the SRP-BCOADL technique uses a Deep Long Short-Term Memory Autoencoder (DLSTM-AE) system for precisely forecasting solar radiation levels. The model’s accuracy is further improved through hyperparameter optimization using the BCOA. The performance analysis of the SRP-BCOADL technique is tested using solar radiation data. Extensive experimental outcomes prove that the SRP-BCOADL method obtains better results over other techniques. The Mean Squared Error (MSE) is just 0.13 kWh/m2, is much lower when compared to other models. The Root Mean Squared Error (RMSE) is also reduced to 0.36 kWh/m2, and the Mean Absolute Error (MAE) reaches a minimal level of 0.276 kWh/m2.

Cooling system design for thermal management of electronic equipment, batteries and photovoltaic (PV) modules is important for increasing the efficiency, safety operation, and long life span the products. In the present study, two different cooling systems are proposed with nano-enhanced multiple impinging jets for a conductive panel. The present cooling systems can be used in electronic cooling and PV modules. Perforated porous object (PPO) and sinusoidal porous object (SPO) are used in the jet cooling system. 2D numerical analysis using finite volume method is conducted considering different values of permeability of the objects (Darcy number (Da) between 10−6 and 10−1). When PPO is used in the cooling system, number of cylinders (between 1 and 6), and size of the cylinders (between 0.015 and 0.075) are considered. In the case of using SPO, amplitude (between 0.1 and 2) and wave number (between 1 and 12) are varied. Alumina-water nanofluid with cylindrical shaped nanoparticles is used as the heat transfer fluid. When permeability is changed for PPO, the average temperature increases by roughly 3.89 °C for a single cylinder and drops by roughly 0.57 °C for a six-cylinder cases. Increasing the size of the cylinder in the PPO case at highest permeability results in temperature drop of 5.3 °C. When changing the number of cylinders, cooling rate varies by about 3.6%. Wave number of SPO is more influential on the cooling performance enhancement as compared to amplitude and permeability of the SPO. The average surface temperature drops by 12.4 °C when the wave number is increased to 12. As compared to reference case of jet impingement cooling without porous object, using PPO and SPO along with the nanofluid result in temperature drop of 12.3 °C and 14.4 °C.

Electric vehicles (EVs) have become popular in reducing the negative impact of ICE automobiles on the environment. EVs have been predicted to be an important mode of mass transit around the globe in recent years. Several charging stations in island and remote areas are dependent on off-grid power sources and renewable energy. Solar energy is used in the daytime as it is based on several environmental components. The creation of efficient power trackers is necessary for solar arrays to produce power at their peak efficiency. To deliver energy during emergencies and store it in case there is an excess, energy storage systems are required. It has long been known that reliable battery management technology is essential for maintaining precise battery charge levels and avoiding overcharging. This study suggests an ideal deep-learning-assisted solar-operated off-board smart charging station (ODL-SOOSCS) design method as a result. The development of on-board smart charging for mass transit EVs is the main goal of the ODL-SOOSCS technique that is being described. In the ODL-SOOSCS approach described here, a perovskite solar film serves as the generating module, and the energy it generates is stored in a module with a hybrid ultracapacitor and a lithium-ion battery. Broad bridge converters and solar panels are incorporated into the deep belief network (DBN) controller, which doubles as an EV charging station. An oppositional bird swarm optimization (OBSO) algorithm is used as a hyperparameter optimizer to improve the performance of the DBN model. Moreover, an MPPT device is exploited for monitoring and providing maximal output of the solar panel if the power sources are PV arrays. The proposed system combines the power of metaheuristic optimization algorithms with deep learning techniques to create an efficient and smart charging station for mass transport passenger vehicles. This integration of two powerful technologies is a novel approach toward solving the complex problem of charging electric vehicles in mass transportation systems. The experimental validation of the ODL-SOOSCS technique is tested on distinct converter topologies. A widespread experimental analysis assures the promising performance of the ODL-SOOSCS method over other current methodologies.

In this study, confined slot nano-jet impingement cooling of a hot moving surface is investigated under the combined utilization multiple rotating cylinders and magnetic field. Both convective heat transfer and entropy generation analysis are conducted using a finite element method. Parametric variation of the rotational Reynolds number (Rew between −500 and 500), velocity ratio (VR between 0 and 0.25), Hartmann number (Ha between 0 and 20) and the horizontal location of cylinders (Mx between −8 and 8) are considered. Rotation of the cylinders generally resulted in the degradation of cooling performance while increasing the wall velocity, and the horizontal location of the cylinder was found to positively contribute to this. Heat transfer rate reductions of 20% and 12.5% are obtained using rotations at the highest Rew for the case of stationary (VR = 0) and moving wall (VR = 0.25). When magnetic field at the highest strength is imposed in the rotating cylinder case, the cooling performance is increased by about 18.6%, while it is reduced by about 28% for the non-rotating cylinder case. The hot wall movement contributes, by about 14%, to the overall cooling performance enhancement. Away from the inlet location of the rotating cylinders, thermal performance improvement of 12% is obtained. The entropy generation rises with higher hot wall velocity and higher horizontal distances of the rotating cylinders, while it is reduced with a higher magnetic field for non-rotating cylinders. The best configurations in terms of cooling performance provide 8.7% and 34.2% enhancements for non-rotating and rotating cylinders compared with the reference case of (Rew, VR, Ha, Mx) = (0, 0, 0, 0), while entropy generation becomes 1% and 15% higher.

The Middle East respiratory syndrome coronavirus (MERS-CoV) is a zoonotic pathogen that can transmit from dromedary camels to humans, causing severe pneumonia, with a 35% mortality rate. Vaccine candidates have been developed and tested in mice, camels, and humans. Previously, we developed a vaccine based on the modified vaccinia virus Ankara (MVA) viral vector, encoding a full-length spike protein of MERS-CoV, MVA-MERS. Here, we report the immunogenicity of high-dose MVA-MERS in prime–boost vaccinations in mice and camels. Methods: Three groups of mice were immunised with MVA wild-type (MVA-wt) and MVA-MERS (MVA-wt/MVA-MERS), MVA-MERS/MVA-wt, or MVA-MERS/MVA-MERS. Camels were immunised with two doses of PBS, MVA-wt, or MVA-MERS. Antibody (Ab) responses were evaluated using ELISA and MERS pseudovirus neutralisation assays. Results: Two high doses of MVA-MERS induced strong Ab responses in both mice and camels, including neutralising antibodies. Anti-MVA Ab responses did not affect the immune responses to the vaccine antigen (MERS-CoV spike). Conclusions: MVA-MERS vaccine, administered in a homologous prime–boost regimen, induced high levels of neutralising anti-MERS-CoV antibodies in mice and camels. This could be considered for further development and evaluation as a dromedary vaccine to reduce MERS-CoV transmission to humans.