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Study of chaos in RayleighBard convection of a micropolar fluid
The paper considers the micropolar fluid (MPF) in a RayleighBard situation and investigates regular and periodic convection, and chaos in the fluid for a wide range of values of the scaled Rayleigh number. The fourth-order scaled Lorenz model that governs weakly non-linear convection is an energy-conserving model whose bounded solution remains within the finiteness of a three-ellipsoid. All the characteristics of the classical Lorenz model are seen in the generalized one. The scaling of the equations is done in such a way that the classical Lorenz model can be obtained as a limiting case of the generalized Lorenz model. The scaled versions of the critical Rayleigh number and the HopfRayleigh number are quantified to determine the onset-of-regular-convection and chaos. Chaotic and periodic regimes appear alternately as the scaled Rayleigh number, r, increases. It is well known from existing literature that the effect of micron-sized particles in the Newtonian fluid is to delay the onset-of-regular-convection. In the paper, it has been found that the suspended particles delay the onset of chaos and the appearance of periodic motion when compared to that in the case of a Newtonian fluid. 2025 Elsevier Ltd -
An improved LSTM based thermal prediction and control algorithm for battery management system in hybrid electric vehicles
Effective thermal management of lithium-ion batteries is critical for ensuring safety, longevity, and optimal performance in Hybrid Electric Vehicles (HEV). This research proposes an improved Long Short-Term Memory (LSTM) based thermal prediction and control algorithm for Battery Management Systems (BMS) to enhance temperature regulation accuracy and computational efficiency. The proposed model integrates an optimized LSTM network with attention mechanisms to capture long-term dependencies in thermal dynamics while reducing prediction latency. A multi-physics-based thermal model is employed to generate high-fidelity training data, accounting for electrochemical-thermal coupling effects. The algorithm incorporates adaptive learning rates and dropout regularization to mitigate overfitting and improve generalization under varying load conditions. A model predictive control framework is designed to leverage real-time LSTM predictions for proactive cooling strategy optimization, minimizing energy consumption while maintaining safe operating temperatures. The proposed model reached RMSE of Heat generation rate of 1.08 W/mA3, Entropy coefficient Error of 0.024 mV/K, Thermal conductivity of 0.626 w/mK, Latency of 28 ms, Cooling energy Consumption of 314.61 kWh and Temperature deviation of 3.34 AC. The proposed solution offers a computationally efficient, scalable framework for next-generation BMS, enhancing battery reliability and vehicle efficiency. 2025 Elsevier Ltd -
Hybrid fractional thermoelasticmachine learning framework for heat and mass transfer in skin tissue: Enhanced simulations using AtanganaBaleanu, CattaneoVernotte models, and KNNSVM classifiers
This study presents a hybrid computational framework that couples advanced fractional thermoelastic modeling with machine-learning-based safety classification for heat and mass transfer in skin tissue. The classical CattaneoVernotte (CV) non-Fourier heat conduction law is extended through the AtanganaBaleanu (AB) fractional operator to capture memory-driven thermal responses, finite thermal wave propagation, and nonlocal biological effects more accurately than traditional Fourier-type formulations. Closed-form expressions are derived using Laplace transforms and inverted numerically to obtain transient temperature, displacement, dilation, and stress fields within the tissue. The AB fractional model demonstrates smoother thermal evolution, reduced overshoot, and lower stress concentrations relative to the CV model, reflecting improved biomedical safety margins during rapid thermal exposure. To enable real-time risk assessment, synthetic datasets generated from the thermoelastic simulations are used to train Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. The ML models reliably distinguish safe and risky thermal regimes, with SVM offering superior generalization and KNN capturing localized variations. The novelty of this work lies in directly integrating fractional physics-based modeling with machine-learning classification for thermal safety diagnosticsestablishing a unified paradigm for predictive biomedical heat transfer. The framework advances thermal therapy planning, burn-injury prevention, implant design, and smart clinical monitoring. While the current study is based on idealized geometry and simulated data, future extensions will incorporate in-vivo tissue characteristics, complex skin layers, and deep learning models to further enhance clinical applicability. 2025 Elsevier Ltd -
Machine learning-enhanced heat and mass transfer study of elliptic motion in piezoelectric thermoelastic plates using Green-Naghdi III and three-phase-lag theories
Rayleigh-type surface waves in piezoelectric (PE) solids are pivotal for acoustic sensors, microelectromechanical systems (MEMS), and non-destructive evaluation. However, classical thermoelastic models fail under high heat flux due to the assumption of infinite thermal signal speeds, which limits their accuracy in coupled thermo-mechanical systems. To capture finite-speed and memory-dependent thermal effects, the Rayleigh wave propagation in a transversely isotropic (TI) PE half-space using generalized theories (such as Green-Naghdi type III (GN-III) and three-phase-lag (TPL)) is studied in this paper. The analytical formulation under varied electrical and thermal boundary conditions has been obtained. Secular equations are derived to characterize phase velocity, attenuation, and specific energy loss. A regression-based machine learning (ML) surrogate model is trained by using an analytical dataset to provide rapid predictions of wave parameters. Additionally, a confusion matrix classifier is applied to identify boundary conditions from simulated wave response features. The results demonstrated that the phase velocity increases with inclination angle and stabilizes with wave number, whereas attenuation and specific loss vary strongly by boundary condition (e.g., minimal in shorted-isothermal cases). The ML surrogate successfully reconstructed analytical predictions with minimal residual error, and the confusion matrix demonstrates accurate classification performance and validates the diagnostic potential of the framework. The novelty of this paper lies in integrating dual thermoelastic theories with machine learning, merging mechanics, heat transfer, and intelligent computing. These findings enable enhanced SAW sensor designs for precise gas/chemical detection, low-loss NDE tools for aerospace composite defect identification, and real-time diagnostics in biomedical ultrasonics for clearer imaging and efficient energy harvesting. 2026 Elsevier Ltd -
Hybrid fractional thermoelasticmachine learning (KNN, CNN and SVM classifier) framework for heat and mass transfer: A computational mechanics approach
The main goal of this study is to create a single fractional thermoelasticmachine learning framework that can accurately model how heat and stress move through skin tissue over time and automatically sort thermal regimes into safe and dangerous ones. The proposed method combines the AtanganaBaleanu fractional operator with the CattaneoVernotte heat flux law and data-driven classifiers (KNN, SVM, and CNN), and Laplace Transforms techniques to derive generalized thermoelastic formulations capable of capturing finite-speed thermal propagation, memory effects, and nonlocal stress relaxation. This connects strict analytical modeling with smart thermal safety prediction. Closed-form expressions for temperature, displacement, dilation, and stress fields are obtained in the Laplace domain and numerically inverted to evaluate transient responses under thermal shock. All fractional thermoelastic simulations and Laplace inversions were executed in MATLAB R2023a, whereas the machine-learning models (KNN, SVM, CNN) were implemented in Python 3.10 using scikit-learn and TensorFlow. To extend the predictive capacity of the analytical models, simulation-derived datasets are used to train three machine learning classifiersK-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). Comparative analyses through confusion matrices, dispersion maps, ROC curves, residual maps, and bar charts demonstrate that CNN achieves superior nonlinear feature extraction and generalization, SVM provides stable global decision boundaries, and KNN efficiently identifies localized thermalmechanical anomalies. The AB fractional model is shown to suppress temperature overshoot and reduce stress concentration relative to CV, offering safer predictions for biological tissues. The combined fractionalML framework enables rapid classification of safe and risky heating regimes, with potential applications in hyperthermia therapy, burn injury prevention, dermatological laser treatments, and thermal hotspot detection in engineered composites. This study establishes a unified pathway where fractional thermoelastic modeling, deep learning, and classical machine learning synergistically addresses complex biomedical and material thermal interactions. A synthetic dataset generated from fractional ABCV thermoelastic simulations was used for training the ML classifiers. 2026 Elsevier Ltd -
Non-Fourier thermal transport analysis in the human eye using a dual-phase-lag bioheat framework under environmental exposure
Understanding how heat propagates inside the human eye is important for preventing thermal damage during environmental exposure, laser treatments, and biomedical procedures, particularly in hot climates where ocular tissues are vulnerable to temperature rise. Conventional bioheat models based on Fourier heat conduction assume instantaneous heat transfer and may therefore fail to capture delayed thermal responses occurring in heterogeneous biological tissues. The aim of this study is to develop and analytically investigate a dual-phase-lag bioheat model capable of accurately predicting intraocular temperature evolution under combined environmental and physiological thermal loading. Motivated by the need for a more realistic and computationally efficient framework for ocular thermal safety assessmentaligned with Saudi Arabias Vision 2030 goals in healthcare innovation and preventive medicinethis study develops a dual-phase-lag (DPL) bioheat model to analyze heat transport in a multilayer human eye under combined environmental and physiological loading. Closed-form analytical solutions are obtained using normal-mode analysis for all six ocular layers while accounting for convection, evaporation, blood perfusion, and tissue porosity. Results show that the DPL model predicts lower and smoother temperature distributions compared with Fourier and LordShulman models, indicating more physiologically realistic thermal behavior. Ambient temperature and evaporation primarily control heating in anterior eye regions, whereas perfusion and tissue porosity dominate thermal regulation in deeper layers. Sensitivity analysis and thermal-safety maps identify critical combinations of exposure conditions that may increase thermal risk. A surrogate-based reduced-order model is further developed and validated, enabling rapid prediction of intraocular temperature with high accuracy. The study demonstrates that incorporating non-Fourier thermal effects significantly improves prediction of ocular temperature dynamics and provides a practical framework for thermal safety assessment, ophthalmic treatment planning, and climate-adaptive healthcare technologies. 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Fractional MooreGibsonThomson thermoelastic analysis of nonlocal nanobeams under moving heat source with machine learning-assisted predictive modeling
This study presents a comprehensive investigation of thermoelastic wave propagation in nonlocal nanobeams subjected to a moving heat source within the framework of fractional MooreGibsonThomson (MGT) heat conduction theory. The model incorporates nonlocal elasticity to capture size-dependent mechanical behavior and employs a fractional-order formulation to account for thermal memory and finite-speed heat propagation. The coupled governing equations are derived and solved analytically using Laplace transform techniques to obtain the temperature, displacement, and stress distributions. A detailed parametric analysis is performed to examine the effects of fractional order, nonlocal parameter, thermal relaxation time, and source velocity on the thermoelastic response. The results reveal significant modifications in wave attenuation, temperature evolution, and stress distribution due to the combined influence of nonlocality and fractional thermal effects, particularly under moving thermal loads. To enhance computational efficiency and enable rapid prediction of system responses, a machine learning-based surrogate framework is developed using an artificial neural network (ANN). The network is trained on data generated from the present analytical model and is shown to accurately predict thermoelastic fields across a wide range of governing parameters. The ANN predictions exhibit excellent agreement with analytical results, demonstrating its capability as a reliable reduced-order modeling tool. The proposed hybrid analyticalcomputational approach provides new insights into thermoelastic behavior at the nanoscale and offers an efficient predictive framework for heat transfer applications involving moving thermal loads. This study is motivated by the need to address unresolved challenges in modeling thermoelastic behavior at the nanoscale, particularly the simultaneous incorporation of fractional heat conduction, nonlocal elasticity, and moving thermal loads within a unified framework. 2026 Published by Elsevier Ltd. -
Bougainvillea glabra-mediated synthesis of Zr?O and chitosan-coated zirconium oxide nanoparticles: Multifunctional antibacterial and anticancer agents with enhanced biocompatibility
The effectiveness and safety of nanomaterials (NMs) are essential for their use in healthcare. This study focuses on creating NPs with multifunctional antibacterial and anticancer properties to combat bacterial infections and cancer disease more effectively than traditional antibiotics. This study investigates the synthesis of Zr3O and chitosan (ch) coated zirconium oxide nanoparticles (chZrO NPs) using Bougainvillea glabra (B. glabra) plant extract through a green, one-pot precipitation method. The synthesized NPs were analyzed using various techniques. Their antibacterial properties are attributed to the production of reactive oxygen species (ROS), influenced by their size, large surface area, oxygen vacancies, ion release, and diffusion capabilities. The chZrO NPs showed superior antibacterial activity compared to Zr3O and chitosan alone, with effective inhibition against both Gram-positive bacteria (S. aureus and B. subtilis) and Gram-negative bacteria (E. coli and P. aeruginosa). Additionally, anticancer studies of chZrO NPs demonstrated significant activity against colon cancer HCT116 cells with C50 values of 4.98 ?g/mL compared to chitosan and Zr3O with 9.62, 6.69 ?g/mL, while biocompatibility tests on L929 cells confirmed their safety showing 93 % cell viability compared to ch and Zr3O. These findings suggest that chZrO NPs are promising candidates for future use in clinical and healthcare applications. 2025 Elsevier B.V. -
TiO2-sodium alginate core-shell nanosystem for higher antimicrobial wound healing application
Wounds that are not properly managed can cause complications. Prompt and proper care is essential, to prevent microbial infection. Growing interest in metal oxide nanoparticles (NPs) for innovative wound treatments targeting healing and microbial infections. In this research, sodium alginate-coated titanium dioxide (TiSA) NPs are synthesized through a green co-precipitation method, combining inorganic TiO2 (Titanium dioxide) and SA (sodium alginate). Analysis via XRD and TEM revealed that the resulting TiSA NPs possessed an anatase phase and polygonal structure, respectively. Biomedical investigations demonstrated that TiSA NPs exhibited enhanced antimicrobial activity compared to the positive control, as well as its counterparts, and showed higher wound healing capabilities compared to TiO2 NPs. The antimicrobial effectiveness of TiSA NPs relied on various physicochemical factors, including small particle size, an altered band gap, and the presence of oxygen vacancies, resulting in microbial cell death. Moreover, TiSA NPs treatment demonstrated higher wound healing activity (98 1.09 %) compared to its counterparts after 24 h of incubation. Assessment of cytotoxicity on healthy fibroblast cells (L929) revealed that TiSA NPs exhibited lower toxicity compared to TiO2 NPs. These findings support the potential of TiSA NPs as promising agents for antimicrobial activity and wound healing. 2025 Elsevier B.V. -
Chitosan stabilized platinum nanoparticles: In vitro and in vivo screening for analgesic and anti-inflammatory applications
In this interdisciplinary research work, the chitosan stabilized platinum nanoparticles are synthesized through the wet chemical method, and the structural, surface morphological, and optical characterizations are done using X-ray crystallography, Raman spectroscopy, transmission electron microscopy, etc. The samples were tested in in vitro trials namely egg albumin denaturation assay and DPPH radical scavenging assays and showed significantly lower effective concentrations (EC50) such as 5.44 ?g/ml and 8.068 ?g/ml respectively. The in vitro experiments were followed by in vivo animal model for analgesic and anti-inflammatory behaviour at two doses of 25 mg/kg and 50 mg/kg utilizing the hot plate method and the carrageenan-induced paw edema model respectively. The in vivo hot plate model for analgesic effect demonstrated that the chitosan stabilized platinum nanoparticles perform exceptionally well and show >90 % analgesia (p < 0.01) by extending the reaction time in the hot plate methodindicating better analgesia. Carrageenan-induced paw edema model demonstrated the exceptional anti-inflammatory ability of chitosan-stabilized platinum nanoparticles. Despite being given at a comparatively lower dosage, chitosan stabilized platinum nanoparticles showed a considerable decrease in paw volume (4045 % edema inhibition) by the third hour of the anti-inflammatory experimentation (p < 0.01) outperforming the standard drug aspirin given at 100 mg/kg. 2025 Elsevier B.V. -
Nanoscale synthesis of nickel oxide@carboxy methyl cellulose@nitrogen doped carbon nanotubes supported metal organic frameworks ternary composite for use symmetric supercapacitor
Metal-organic frameworks (MOFs) are a novel class of porous materials that combine organic linkers and inorganic metal ions. Supercapacitors use a large specific surface area, adjustable architecture, and tunable porosity and pore diameters to improve the electrochemical performances with metal sulfides. The main goal of this study was to make a nickel oxide ternary composite using a hydrothermal method with urea as a catalyst for electrochemical uses. We characterized these fabricated composite materials using analytical and morphological characterization for their confirmation. These results show that the composite electrode had a great specific capacitance of 464 F/g at 0.5 A/g in a 1 M KOH electrolyte when set up with three electrodes. The symmetric two-electrode system showed 52.83 F/g at 0.5 A/g with an excellent energy density of 13.14 Whkg?1 and a power density of 616 Wkg?1 via 1 M KOH electrolyte. The fabricated ternary composite electrode demonstrated cyclic stability, with an excellent retention rate of 89 % after 7000 cycles. Therefore, the fabricated ternary composite electrode materials have enormous potential for electrochemical storage properties. 2025 Elsevier B.V. -
Wound healing efficacy of Couroupita Guianensis loaded carboxymethylcellulose film
Couroupita Guianensis extracts loaded carboxymethylcellulose film (CGELCF) was fabricated through a solution casting technique. Phytochemical screening confirmed the presence of alkaloids, proteins, flavonoids, saponins, carbohydrates, cardiac glycosides, and coumarins in CGE. The HPLC revealed that ellagic acid and gallic acid derivatives are abundant in CGE. FTIR spectrum confirmed the different functional group's presence in the film. The XRD, DSC, and TGA revealed the crystallinity, thermal stability, melting point (300 C), and degradation of the film. FESEM and EDX analysis revealed surface morphology and elemental compositions. The atomic wt% of C, O, Na, and P were found as follows 38.39, 49.34, 11.44, and 0.83. The zone of inhibition was found as 19 and 20 mm against Staphylococcus aureus and Escherichia coli respectively. The tensile strength and modulus of elasticity was found 7.35 MPa and 1.38 GPa respectively. In-vitro wound scratch revealed above 90 % of cell migrations for all the concentrations after 18 h. The in-vitro cytotoxicity revealed 90.83 % of cell viability and 9.17 % of toxicity. In-vivo studies revealed that wound closure and wound healing with the CGELCF mice was within 7 days and exhibited the rapid drug. The CGELCF may find usage as a bandage for wounds. 2025 -
Sodium alginate functionalized nickel ferrite nanocomposites: synthesis, physicochemical characterization, and evaluation of antibacterial, anticancer, and biocompatibility properties
The rise of multidrug-resistant bacteria and the need for effective therapies against breast cancer highlight the demand for multifunctional nanomaterials with high biocompatibility. In this study, Nickel ferrite (NiFe?O?) and sodium alginate functionalized NiFe?O? nanocomposites (NiFe?O?-SA) were synthesized via a green co-precipitation method. X-ray diffraction confirmed a cubic spinel structure, and transmission electron microscopy revealed quasi-spherical nanoparticles with sizes of 1525nm and uniform alginate coating. UVVis analysis showed a reduction in band gap from 4.44eV to 3.13eV, while photoluminescence spectra indicated enhanced charge carrier separation. NiFe?O?-SA exhibited strong antibacterial activity against Gram-negative pathogens (Klebsiella pneumoniae, Escherichia coli, Shigella dysenteriae, Pseudomonas aeruginosa, and Proteus vulgaris), with membrane disruption confirmed by microscopy. Cytotoxicity studies on MCF-7 breast cancer cells demonstrated dose-dependent inhibition with an IC?? of 11.9?g/mL, and zebrafish embryo assays confirmed excellent biocompatibility for NiFe?O?-SA. These findings highlight NiFe?O?-SA nanocomposites as promising multifunctional nanomaterials for therapeutic and biomedical applications. 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Antibacterial performance of chitosan-modified magnesium fluoride nanoparticles: Synthesis and characterization
The rise of multidrug-resistant bacterial infections threatens human health by reducing the effectiveness of conventional antibiotics. This growing challenge highlights the urgent need for advanced nano-based antibacterial materials capable of overcoming resistance and providing broad-spectrum protection. In this study, magnesium fluoride (MgF2) NPs and Chitosan modified MgF2 (MgF2-Cs) were synthesized via a facile wet-chemical route and characterized to evaluate their structural, surface, and antibacterial properties. XRD confirmed the formation of tetragonal MgF2 with crystallite sizes of 29nm for MgF2 and 22nm for MgF?Cs, the reduction attributed to Cs-induced surface modification. FTIR, PL, and XPS analyses verified successful Cs incorporation through the presence of OH, NH?, CN, and OC=O functional groups and the preservation of the MgF2 lattice. DLS further supported increased hydrodynamic size upon polymer coating. PL analysis showed enhanced blue-green emission around 497nm in MgF2Cs, suggesting increased defect density and corresponding ROS-generation ability. Antibacterial activity against Gram-positive: S. aureus, S. pneumoniae and Gram-negative: K. pneumoniae, S. dysenteriae bacteria demonstrated significantly improved inhibition for MgF2Cs, with zone diameters of 1521mm, surpassing MgF2 (1216mm) and Cs (1115mm. The MIC and MBC values for MgF?-Cs against K. pneumoniae were determined to be 0.6mgmL?1 and 0.9mgmL?1, respectively. The enhanced antibacterial performance is attributed to synergistic effects of defect-mediated ROS production and Csbacteria electrostatic interactions. 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Government spending and lower secondary education completion in Asia: A cross-national analysis
This paper examines the influence of government expenditure on lower secondary education completion rates across 35 Asian countries, using 2019 data from the UNESCO Institute for Statistics. Despite global commitments to equitable education, regional disparities in funding and outcomes persist. Employing a cross-sectional correlational design, the study identifies a weak, statistically nonsignificant association (r = 0.26, p = 0.122) between government education spending and completion rates. These findings suggest that while funding remains a critical input, its impact may be limited without concurrent investments in education quality, governance, and equity. Key limitations include reliance on single-year data, absence of control variables, and structural inefficiencies across national systems. The study advocates for more nuanced public investment strategies that emphasize targeted interventions, data-driven policymaking, and inclusive financing to align national efforts with Sustainable Development Goals. These insights are relevant for ministries of education, international organizations, and donors seeking to strengthen education systems and promote equitable access across Asia. 2025 Elsevier Ltd -
Beyond averages: Mapping unequal learning and the dynamics of educational access in Jammu and Kashmir using Gini Decomposition Analysis
Educational inequality remains a persistent challenge in India, particularly in the Union Territory of Jammu and Kashmir, where disparities in access and attainment hinder socioeconomic progress. This study examines shifts in educational inequality over time and explores disparities based on geographic and socioeconomic factors. Using data from two rounds of the National Family Health Survey (NFHS-4 and NFHS-5), we apply a Gini decomposition framework to distinguish between-group and within-group inequalities and employ a generalized ordered logistic regression model to assess determinants of educational attainment. Our findings indicate an overall increase in average years of schooling and a reduction in the Gini coefficient, yet significant disparities remain across gender, wealth status, and regional divisions. The results highlight the need for targeted interventions to improve access to education for rural populations, women, the Muslim community, and the Kashmir division. Strengthening school infrastructure, expanding e-learning resources, and promoting gender inclusive policies can help bridge these educational gaps and foster equitable progress. 2025 Elsevier Ltd -
Parametric analysis for thermally magnetized hybrid ternary (TMHT) nanofluid flow on thin film with temperature stratification
The thermophysical examination of flow field claims various applications in both scientific and industrial domains and hence it remains important to inspect especially when both the heat and mass transfer are taken simultaneously. Owning such motivation, the present study offers a response surface optimization for thermal flow field of hybrid ternary water-based aluminium, silicon and Zinc nanofluid over a stretched surface manifested with both temperature stratification and concentration stratification effects. The governing equations are formulated for mathematical model and those PDE's are reduced to ODE's by using appropriate similarity transformations. Those obtained resultant equations are solved numerically by using Runge Kutta Fehlberg fourth fifth-order (RKF 45) technique. The supremacy of essential aspects on the flow field, heat and mass transfer rates were analyzed using graphical representation. Additionally, Response surface Methodology is performed to derived the heat transfer rate as a response function for the input factors for different parameters. From the graph it is noticed that temperature profile drops as the thermal stratification parameter increases. The temperature admits the direct relation with an increase in the solid volume fraction of ternary nanofluids. From RSM it is noticed that adjusted R-squared and R-squared are obtained as 100 % accuracy of the mathematical model. 2025 The Author(s) -
Statistical thermal study of ternary hybrid nanofluid flow in coaxial cylinder: artificial neural network approach
The objective of this study is to examine heat and mass transfer aspects of ternary nanofluid flow in coaxial cylinder under the influence of Arrhenius activation energy, microorganisms concentration and bioconvection Peclet number, which a pivotal rolet in various scientific and engineering applications. The flow of ternary nanofluid is caused due to stretching inner cylinder with stationary outer cylinder. The nonlinear partial equations are derived for the flow model and reduced to non-linear ordinary differential equation by applying suitable similarity transformation. The resultant equations are resolved mathematically using Runge Kutta Fehlberg (RKF45) technique. The obtained numerical results are validated with the published work to check the exactness of the solution methodology and it is noticed that the present outcomes are on par with published work. The physical behaviour of the pertinent parameters is analysed through graphical depiction. The derived quantities like drag force and Sherwood number are studied through tabular column. Additionally, the heat transfer rate is analysed by using backpropagated Levenberg-Marquardt Machine learning algorithm. Further, the correlation between the parameter on the rate of heat transfer is analysed by using Mean square error and regression graphs. The key outcome of this research is that, the temperature upsurges by increasing the solid volume of nanoparticle due to higher thermal conductivity of the nanoparticles. Further, it is perceived from the artificial neural network model that, the correlation between the input parameters and output data are strongly correlated (R = 1). 2025 -
Influence of magnetic field modulation on thermomagnetic convection in a layer of ferrofluid bounded by rigidfree boundaries
With a focus on rigidfree boundaries, the impact of magnetic field modulation on thermomagnetic convection in ferrofluids is studied. The effects of large and small-scale modulations are examined using linear theory. Utilizing the superposition principle and several perturbation modes, small-scale modulation is investigated. The case of large-scale modulation is dealt with the aid of Mathieu equation to shed light on the prevalence of subharmonic motions. Utility of Floquet theory resulted in an infinite linear system from which the critical magnetic Rayleigh number was evaluated in the case of large-scale modulations. Weak non-linear theory is used in arriving at the non-autonomous tri-modal Lorenz system, from which bifurcation and post-convective analyses are done. It was found that magnetic field-modulation has significant, yet varying stabilizing effects on the convection process, and is uniquely determined by the conditions of the medium and magnitude of the modulation. Another important outcome of the study is that magnetic field modulation induces hyperchaos within the tri-modal autonomous system, and occurs for random combinations of modulation parameters. The emergence of chaos for rigidfree boundaries is confirmed to occur between the equivalent rigidrigid and freefree cases. 2025 -
Optimization of rGO-MoO3 nanocomposite electrode to fabricate an aqueous symmetric supercapacitor device with enhanced electrochemical performance
This study presents the first investigation into the effect of reduced graphene oxide (rGO) on the electrochemical properties of rGO-MoO? nanocomposites, synthesized via the hydrothermal method. The nanocomposites were prepared with varying rGO concentrations, and their structural, morphological, elemental, electrical, optical, and surface characteristics were analyzed. Structural analysis confirmed the presence of an orthorhombic MoO? phase, while the morphological analysis revealed MoO? nanobars anchored onto rGO nanosheets. The electrochemical performance of the nanocomposites was evaluated using a three-electrode configuration. The electrode demonstrating superior performance was selected to fabricate a prototype symmetric device. This device exhibited a specific capacitance of 369 F g?1 at a current density of 1 A g?1 and an energy density of 51 W h kg?1. Moreover, the device demonstrated a stability of 91% over 1000 cycles with a coulombic efficiency of 104%. 2025 Hydrogen Energy Publications LLC