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This study aims to offer a hybrid stand-alone system for electric vehicle (EV) charging stations (CS), an emerging power scheme due to the availability of renewable and environment-friendly energy sources. This paper presents the analysis of a photovoltaic (PV) with an adaptive neuro-fuzzy inference system (ANFIS) algorithm, solid oxide fuel cell (SOFC) and a battery storage scheme incorporated for EV CS in a stand-alone mode. In previous studies, either the hydrogen fuel of SOFC or the irradiance is controlled using artificial neural network. These parameters are not controlled simultaneously using an ANFIS-based approach. The ANFIS-based stand-alone hybrid system controlling both the fuel flow of SOFC and the irradiance of PV is discussed in this paper.

The ANFIS algorithm provides an efficient estimation of maximum power (MP) to the nonlinear voltage–current characteristics of a PV, integrated with a direct current–direct current (DC–DC) converter to boost output voltage up to 400 V. The issue of fuel starvation in SOFC due to load transients is also mitigated using an ANFIS-based fuel flow regulator, which robustly provides fuel, i.e. hydrogen per necessity. Furthermore, to ensure uninterrupted power to the CS, PV is integrated with a SOFC array, and a battery storage bank is used as a backup in the current scenario. A power management system efficiently shares power among the aforesaid sources.

A comprehensive simulation test bed for a stand-alone power system (PV cells and SOFC) is developed in MATLAB/Simulink. The adaptability and robustness of the proposed control paradigm are investigated through simulation results in a stand-alone hybrid power system test bed.

The simulation results confirm the effectiveness of the ANFIS algorithm in a stand-alone hybrid power system scheme.

Diesel has traditionally been considered the best-suited and most widely used fuel in various sectors, including manufacturing industries, power production, automobiles and transportation. However, with the ongoing crisis of fossil fuel inadequacy, the search for alternative fuels and their application in these sectors has become increasingly important. One particularly interesting and beneficial alternative fuel is biodiesel derived from bio sources.

In this research, an attempt was made to use biodiesel in an unconventional micro gas turbine engine. It will remove the concentric use of diesel engines for power production by improving fuel efficiency as well as increasing the power production rate. Before the fuel is used enormously, it has to be checked in many ways such as performance, emission and combustion analysis experimentally.

In this paper, a detailed experimental study was made for the use of Spirulina microalgae biodiesel in a micro gas turbine. A small-scale setup with the primary micro gas turbine and secondary instruments such as a data acquisition system and AVL gas analyser. The reason for selecting the third-generation microalgae is due to its high lipid and biodiesel production rate. For the conduction of experimental tests, certain conditions were followed in addition that the engine rotating rpm was varied from 4,000, 5,000 and 6,000 rpm. The favourable and predicted results were obtained with the use of microalgae biodiesel.

The performance and combustion results were not exactly equal or greater for biodiesel blends but close to the values of pure diesel; however, the reduction in the emission of CO was at the appreciable level for the used spirulina microalgae biodiesel. The emission of nitrogen oxides and carbon dioxide was a little higher than the use of pure diesel. This experimental analysis results proved that the use of spirulina microalgae biodiesel is both economical and effective replacement for fossil fuel.

In recent years, the convolutional neural network (CNN) based deep learning approach has succeeded in data-mining the relationship between microstructures and macroscopic properties of materials. However, such CNN models usually rely heavily on a large set of labeled images to ensure the accuracy and generalization ability of the predictive models. Unfortunately, in many fields, acquiring image data is expensive and inconvenient. This study aims to propose a data augmentation technique to enhance the performance of the CNN models for linking microstructural images to the macroscopic properties of composites.

Microstructures of composites are synthesized using discrete element simulations and Potts kinetic Monte Carlo simulations. Macroscopic properties such as the elastic modulus, Poisson's ratio, shear modulus, coefficient of thermal expansion, and triple-phase boundary length density are extracted on representative volume elements. The CNN model is trained using the 3D microstructural images as inputs and corresponding macroscopic properties as the labels. The comparison of the predictive performance of the CNN models with and without data augmentation treatment are compared.

The comparison between the prediction performance of CNN models with and without data augmentation showed that the former reduced the weighted mean absolute percentage error (WMAPE) for the prediction from 5.1627% to 1.7014%. This significant reduction signifies that the proposed data augmentation method can effectively enhance the generalization ability and robustness of CNN models.

This study demonstrates that data augmentation is beneficial for solving the problems of model overfitting, data scarcity, and sample imbalance for CNN-based deep learning tasks at a low cost. By developing more and advanced data augmentation techniques, deep learning accelerated homogenization will boost the multi-scale computational mechanics and materials.

The purpose of this research is to assess the removal of oil spills from the seawater surface as well as the antibacterial activity of ZnFe₂O₄-cetyltrimethylammonium bromide (CTAB, cationic surfactant) magnetic nanoparticles (ZFO-CTAB MNPs).

A CTAB-assisted sol–gel method was used to synthesize ZFO-CTAB MNPs. X-ray powder diffraction and Fourier transform infrared spectroscopy were used for ZFO-CTAB MNPs characterization. Also, the magnetic force and apparent density of ZFO-CTAB MNPs were determined. The oil spill cleanup was investigated by using the gravimetric oil removal (GOR) technique, which used ZFO-CTAB MNPs as oil absorbent material and four oil samples (crude, diesel, gasoline and used oil) as oil spill models. The antibacterial activity of ZFO-CTAB MNPs against Gram-negative bacteria (Pseudomonas aeruginosa, Escherichia coli and Salmonella typhi) was investigated by using the optical density method.

The results revealed that, when the amount of ZFO-CTAB was 0.01 g, gasoline oil had the highest GOR (51.80 ± 0.88 g/g) and crude oil had the lowest (11.29 ± 0.82 g/g). Furthermore, for Escherichia coli, Salmonella typhi and Pseudomonas aeruginosa, ZFO-CTAB MNPs inhibited bacterial growth with a higher percentage (94.24%–95.63%).

The applications of ZFO-CTAB MNPs in the cleanup of oil spills from aqueous solutions, as well as their antibacterial activity. The results showed that ZFO-CTAB MNPs are a promising material for removing oil spills from bodies of water as well as an antibacterial agent against Gram-negative bacterial strains.

Effective performance of a direct ethanol fuel cell (FC) stack depends on the satisfactory operation of its individual cells where it is always challenging to manage the temperature gradient, water flow and distribution of reactants. In that, the design of the bipolar fuel flow path plate plays a vital role in achieving the aforementioned parameters. Further, the bipolar plates contribute 80% of the weight and 30%–40% of its total cost. Aim of this study is to enhance the efficiency of fuel to energy conversion and to minimize the overall cost of production.

The authors have specifically designed, simulated and fabricated a standard 2.5 × 2.5 cm² active area proton exchange membrane (PEM) FC flow path plate to study the performance by varying the flow fields in a single ladder, double ladder and interdigitated and varying channel geometries, namely, half curve, triangle and rectangle.

Using the 3D PEMFC model and visualizing the physical and electrochemical processes occurring during the operation of the FCs resulted in a better-performing flow path plate design. It is fabricated by using additive manufacturing technology. In addition, the assembly of the full cell with the designed flow path plate shows about an 11.44% reduction in total weight, which has a significant bearing on its total cost as well as specific energy density in the stack cell.

Simultaneous optimization of multiple flow path parameters being carried out for better performance is the hallmark of this study which resulted in enhanced energy density and reduced cost of device production.

Combustion of fuel with oxidizer inside a combustion chamber of an internal combustion engine forms inevitable oxides of nitrogen (NOx) due to high temperature at different locations of the combustion chamber. This study aims to quantify NOx formed inside the combustion chamber using two fuels, a conventional diesel (n-heptane) and a biodiesel (methyl oleate).

This research uses a computational fluid dynamics simulation of chemically reacting fluid flow to quantify and compare oxides of nitrogen (NOx) in a compression ignition (CI) engine. The study expends species transport model of ANSYS FLUENT. The simulation model has provided the temperature profile inside the combustion chamber, which is subsequently used to calculate NOx using the NOx model. The simulation uses a single component hydrocarbon and oxygenated hydrocarbon to represent fuels; for instance, it uses n-heptane (C₇H₁₆) for diesel and methyl-oleate (C₁₉H₃₆O₂) for biodiesel. A stoichiometric air–fuel mixture is used for both fuels. The simulation runs a single cylinder CI engine of 650 cm³ swept volume with inlet and exhaust valves closed.

The pattern for variation of velocity, an important flow parameter, which affects combustion and subsequently oxides of nitrogen (NOx) formation at different piston locations, is similar for the two fuels. The variations of in-cylinder temperature and NOx formation with crank angles have similar patterns for the fuels, diesel and biodiesel. However, the numerical values of in-cylinder temperature and mass fraction of NOx are different. The volume averaged static peak temperatures are 1,013 K in case of diesel and 1,121 K in case of biodiesel, while the mass averaged mass fractions of NOx are 15 ppm for diesel and 141 ppm for biodiesel. The temperature rise after combustion is more in case of biodiesel, which augments the oxides of nitrogen formation. A new parameter, relative mass fraction of NOx, yields 28% lower value for biodiesel than for diesel.

This work uses a new concept of simulating simple chemical reacting system model to quantify oxides of NOx using single component fuels. Simplification has captured required fluid flow data to analyse NOx emission from CI engine while reducing computational time and expensive experimental tests.

The purpose of this paper is to investigate the performance of Passive Direct Methanol Fuel Cell (PDMFC) experimentally using various Membrane Electrode Assembly (MEA) shapes such as square, rectangle, rhombus, and circle with equal areas and equal perimeters. The variation in MEA shape/size is achieved by altering gasket openings in the dynamic regions.

In the equal areas of MEA shapes, gasket opening areas of 1963.5 (+/−0.2) mm2 are used. Whereas in the equal perimeters of shapes, gasket opening perimeters of 157.1 (+/−0.2) mm are used. In this experimentation, Nickel-201 current collectors with 45.3% of circular openings are used on both the anode and cathode sides. The experiment is carried out at a 5 molar methanol concentration to find out the highest power density of the cell.

In the equal areas, among the shapes that are chosen for investigation, the square shape opening consisting of a perimeter of 177.2 mm has developed a maximum power density of 6.344 mWcm⁻² and a maximum current density of 65.2 mAcm⁻². Similarly, in equal perimeters, the rhombus shape opening with an area of 1400 mm2 has developed a maximum power density of 7.714 mWcm⁻² and a maximum current density of 85.3 mAcm⁻².

The novelty of this research work is instead of fabricating various shapes and sizes of highly expensive MEAs, the desired shapes and sizes of the MEA are achieved by altering gasket openings over dynamic regions to find out the highest power density of the cell.

In the developing field of nano-materials synthesis, copper oxide nanoparticles (NPs) are deemed to be one of the most significant transition metal oxides because of their intriguing characteristics. Its synthesis employing green chemistry principles has become a key source for next-generation antibiotics attributed to its features such as environmental friendliness, ease of use and affordability. Because they are more environmentally benign, plants have been employed to create metallic NPs. These plant extracts serve as capping, stabilising or hydrolytic agents and enable a regulated synthesis as well.

Organic chemical solvents are harmful and entail intense conditions during nanoparticle synthesis. The copper oxide NPs (CuO-NPs) synthesised by employing the green chemistry principle showed potential antitumor properties. Green synthesised CuO-NPs are regarded to be a strong contender for applications in the pharmacological, biomedical and environmental fields.

The aim of this study is to evaluate the anticancer potential of CuO-NPs plant extracts to isolate and characterise the active anticancer principles as well as to yield more effective, affordable, and safer cancer therapies.

This review article highlights the copper oxide nanoparticle's biomedical applications such as anticancer, antimicrobial, dental and drug delivery properties, future research perspectives and direction are also discussed.

The purpose of this paper is to introduce flexible dye-sensitized solar cells (FDSSCs).

In the third generation solar cells, glass was used as a substrate, which due to its high weight and fragility, was not possible to produce continuously. However, in flexible solar cells, flexible substrates are used as new technology. The most important thing may choose a suitable substrate to produce a photovoltaic (PV) device with optimal efficiency.

Conductive plastics or metallic foils are the two main candidates for glass replacement, each with its advantages and disadvantages. As some high-temperature methods are used to prepare solar cells, metal substrates can be used to prepare PV devices without any problems. In contrast to the advantage of high thermal resistance in metals, metal substrates are dark and do not transmit enough light. In other words, metal substrates have a high loss of photon energy. Like all technologies, PV devices with polymer substrates have technical disadvantages.

In this study, the development of FDSSCs offers improved photovoltaic properties.

The most important challenge is the poor thermal stability of polymers compared to glass and metal, which requires special methods to prepare polymer solar cells. The second important point is choosing the suitable components and materials for this purpose.

Dependence of efficiency and performance of the device on the angle of sunlight, high-cost preparation devices components, limitations of functional materials such as organic-mineral sensitizers, lack of close connection between practical achievements and theoretical results and complicated fabrication process and high weight.

In recent times, there has been a growing interest in buoyancy-induced heat transfer within confined enclosures due to its frequent occurrence in heat transfer processes across diverse engineering disciplines, including electronic cooling, solar technologies, nuclear reactor systems, heat exchangers and energy storage systems. Moreover, the reduction of entropy generation holds significant importance in engineering applications, as it contributes to enhancing thermal system performance. This study, a numerical investigation, aims to analyze entropy generation and natural convection flow in an inclined square enclosure filled with Ag–MgO/water and Ag–TiO2/water hybrid nanofluids under the influence of a magnetic field. The enclosure features heated slits along its bottom and left walls. Following the Boussinesq approximation, the convective flow arises from a horizontal temperature difference between the partially heated walls and the cold right wall.

The governing equations for laminar unsteady natural convection flow in a Newtonian, incompressible mixture is solved using a Marker-and-Cell-based finite difference method within a customized MATLAB code. The hybrid nanofluid’s effective thermal conductivity and viscosity are determined using spherical nanoparticle correlations.

The numerical investigations cover various parameters, including nanoparticle volume concentration, Hartmann number, Rayleigh number, heat source/sink effects and inclination angle. As the Hartmann and Rayleigh numbers increase, there is a significant enhancement in entropy generation. The average Nusselt number experiences a substantial increase at extremely high values of the Rayleigh number and inclination.

This numerical investigation explores advanced applications involving various combinations of influential parameters, different nanoparticles, enclosure inclinations and improved designs. The goal is to control fluid flow and enhance heat transfer rates to meet the demands of the Fourth Industrial Revolution.

In a 90° tilted enclosure, the addition of 5% hybrid nanoparticles to the base fluid resulted in a 17.139% increase in the heat transfer rate for Ag–MgO nanoparticles and a 16.4185% increase for Ag–TiO2 nanoparticles compared to the base fluid. It is observed that a 5% nanoparticle volume fraction results in an increased heat transfer rate, influenced by variations in both the Darcy and Rayleigh numbers. The study demonstrates that the Ag–MgO hybrid nanofluid exhibits superior heat transfer and fluid transport performance compared to the Ag–TiO2 hybrid nanofluid. The simulations pertain to the use of hybrid magnetic nanofluids in fuel cells, solar cavity receivers and the processing of electromagnetic nanomaterials in enclosed environments.