Enhanced heat transfer characteristics of water based hybrid nanofluids with graphene nanoplatelets and multi walled carbon nanotubes

Hybrid nanocomposites impact on heat transfer efficiency and pressure drop in turbulent flow systems: application of numerical and machine learning insights

This research explores the feasibility of using a nanocomposite from multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) for thermal engineering applications. The hybrid nanocomposites were suspended in water at various volumetric concentrations. Their heat transfer and pressure drop characteristics were analyzed using computational fluid dynamics and artificial neural network models. The study examined flow regimes with Reynolds numbers between 5000 and 17,000, inlet fluid temperatures ranging from 293.15 to 333.15 K, and concentrations from 0.01 to 0.2% by volume. The numerical results were validated against empirical correlations for heat transfer coefficient and pressure drop, showing an acceptable average error. The findings revealed that the heat transfer coefficient and pressure drop increased significantly with higher inlet temperatures and concentrations, achieving approximately 45.22% and 452.90%, respectively. These results suggested that MWCNTs-GNPs nanocomposites hold promise for enhancing the performance of thermal systems, offering a potential pathway for developing and optimizing advanced thermal engineering solutions.

A comprehensive review on enzyme-based biosensors: Advanced analysis and emerging applications in nanomaterial-enzyme linkage

Biosensors with nanomaterials and enzymes detect and quantify specific targets in samples, converting recognition into measurable signals. The study explores the intrinsic synergy between these elements for detecting and quantifying particular targets in biological and environmental samples, with results demonstrated through bibliometric analysis and a comprehensive review of enzyme-based biosensors. Using WoS, 57,331 articles were analyzed and refined to 880. Key journals, countries, institutions, and relevant authors were identified. The main areas highlighted the multidisciplinary nature of the field, and critical keywords identified five thematic clusters, revealing the primary nanoparticles used (CNTs, graphene, AuNPs), major application fields, basic application themes, and niche topics such as sensitive detection, peroxidase activity, and quantum dot utilization. The biosensor overview covered nanomaterials and their primary applications, addressing recent advances and inherent challenges. Patent analysis emphasized the U.S. leadership in the industrial sector, contrasting with China's academic prominence. Future studies should focus on enhancing biosensor portability and analysis speed, with challenges encompassing efficient integration with recent technologies and improving stability and reproducibility in the nanomaterial-enzyme interaction.

Study on Heat Transfer Characteristics of Graphene Nanofluids in Mini-Channels of Thermal Integrated Building

Two kinds of rectangular mini-channels of different sizes were designed and fabricated for testing the convective heat transfer characteristics of graphene nanofluids. The experimental results show that the average wall temperature decreases with the increases in graphene concentration and Re number at the same heating power. Within the experimental Re number range, the average wall temperature of 0.03% graphene nanofluids in the same rectangular channel decreases by 16% compared with that of water. At the same heating power, the convective heat transfer coefficient increases with the increase in the Re number. The average heat transfer coefficient of water can be increased by 46.7% when the mass concentration of graphene nanofluids is 0.03% and the rib-to-rib ratio is 1:2. In order to better predict the convection heat transfer characteristics of graphene nanofluids in small rectangular channels of different sizes, the convection heat transfer equations applicable to graphene nanofluids of different concentrations in small rectangular channels with different channel rib ratios were fitted, based on factors such as flow Re number, graphene concentration, channel rib ratio, Pr number, and Pe number; the average relative error (MRE) was 8.2%. The mean relative error (MRE) was 8.2%. The equations can thus describe the heat transfer characteristics of graphene nanofluids in rectangular channels with different groove-to-rib ratios.

Analytical Investigation of the Heat Transfer Effects of Non-Newtonian Hybrid Nanofluid in MHD Flow Past an Upright Plate Using the Caputo Fractional Order Derivative

The objective of this paper is to examine the augmentation of the heat transfer rate utilizing graphene (Gr) and multi-walled carbon nanotubes (MWCNTs) as nanoparticles, and water as a host fluid in magnetohydrodynamics (MHD) flow through an upright plate using Caputo fractional derivatives with a Brinkman model on the convective Casson hybrid nanofluid flow. The performance of hybrid nanofluids is examined with various shapes of nanoparticles. The Caputo fractional derivative is utilized to describe the governing fractional partial differential equations with initial and boundary conditions on the flow model. Exact solutions are obtained for flow transport, temperature distribution besides that heat transfer rate and friction drag in terms of Mittag-Leffler function by using Fourier sine and Laplace techniques as hybrid methods. Further, we provided the limiting case solutions for classic partial differential equations on obtained governing fluid flow models. The influence of various physical parameters with different fractional orders are investigated on hybrid nanofluid’s fractional momentum and energy by plotting velocity and energy curves. Few of the findings suggest that fractional parameters have significant effect on flow parameters and that blade-shaped nanoparticles have a high heat transfer rate. The graphical results reveal that the Grashof number shows a symmetry effect in the case of cooling and heating the plate. Furthermore, the performance of hybrid nanofluid is considerably more effective with the Caputo-fractional derivatives rather than in the classic derivative approach.