Synthesis, stability, density, viscosity of ethylene glycol-based ternary hybrid nanofluids: Experimental investigations and model -prediction using modern machine learning techniques

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.

Energy transmission through radiative ternary nanofluid flow with exponential heat source/sink across an inclined permeable cylinder/plate: numerical computing

The steady two-dimension (2D) ternary nanofluid (TNF) flow across an inclined permeable cylinder/plate is analyzed in the present study. The TNF flow has been examined under the consequences of heat source/sink, permeable medium and mixed convection. For the preparation of TNF, the magnesium oxide (MgO), cobalt ferrite (CoFe2O4) and titanium dioxide (TiO2) are dispersed in water. The rising need for highly efficient cooling mechanisms in several sectors and energy-related processes ultimately inspired the current work. The fluid flow and energy propagation is mathematically described in the form of coupled PDEs. The system of PDEs is reduced into non-dimensional forms of ODEs, which are further numerically handled through the Matlab package (bvp4c). It has been observed that the results display that the porosity factor advances the thermal curve, whereas drops the fluid velocity. The effect of heat source/sink raises the energy field. Furthermore, the plate surface illustrates a leading behavior of energy transport over cylinder geometry versus the variation of ternary nanoparticles (NPs). The energy dissemination rate in the cylinder enhances from 4.73 to 11.421%, whereas for the plate, the energy distribution rate boosts from 6.37 to 13.91% as the porosity factor varies from 0.3 to 0.9.

Synthesis, thermophysical characterization and thermal performance analysis of novel Cu-MXene hybrid nanofluids for efficient coolant applications

Hybrid nanofluids are considered as an alternative for conventional heat transfer fluids and mono nanofluids due to its remarkable enhancement in thermo-physical properties. However, there are some limitations in achieving the better thermo-physical properties due to the stability of nanoparticles in different base fluids at higher concentration. This work aims at synthesizing, thermo-physical characterization and thermal performance estimation of stable Cu-MXene based hybrid nanofluids using various base fluids at very low volume concentration of Cu and MXene nanostructures. Two step method is employed to prepare Cu-MXene hybrid nanofluids by dispersing the low volume concentration of as prepared Cu and MXene nanostructures (ranging from 0.01-0.05 vol%) containing SDS surfactant in various base fluids such as water, methanol, castor oil and silicon oil. Synthesized mono and hybrid nanofluids shows excellent stability against aggregation up to 7 days as evidenced from higher zeta potential values. Wettability studies conducted using contact angle measurement suggests that the castor oil, methanol and silicon oil based hybrid nanofluids exhibits hydrophilic behavior (showing contact angle less than 90°). Hybrid nanofluids display excellent enhancement in thermal conductivity at very low concentration of nanostructures (more than 70% for methanol based Cu-MXene hybrid nanofluid). Viscosity of the silicone oil based hybrid nanofluids show a remarkable enhancement followed by water, methanol and castor oil based hybrid nanofluids. Thermal conductivity and viscosity of hybrid nanofluids are effectively validated with existing theoretical models. Moreover, specific heat and pumping power of the hybrid nanofluids with respect to volume concentration of nanostructures are determined using the existing theoretical equations. Thermal performance of hybrid nanofluids was successfully estimated using Figure of Merit (FOM) analysis and suggested the better heat transfer fluid for improving the heat transfer performance under laminar and turbulent flow conditions for efficient cooling applications.

Studies on the Functional Properties of Titanium Dioxide Nanoparticles Distributed in Silyl-Alkyl Bridged Polyaniline-Based Nanofluids

In the present work, a new kind of nanocomposite (NC)-based solid component was prepared for formulating nanofluids (NFs). The NC comprised metal oxide (titanium dioxide, TiO2) dispersed in a conducting polymer with polyaniline (PANI) and chemically linked silyl-alkyl units in it (PSA) that were designated as T-PSA NC. The NFs with ethylene glycol (EG) as a base fluid were prepared with T-PSA NCs with various compositions of TiO2 and PSA as well for various concentrations of T-PSA NCs. The scanning electron microscopic evaluation of the NC revealed that PSA deposition on TiO2 nanoparticles (NPs) decreased particle agglomeration. The PSA coating on the TiO2 NPs did not influence the crystalline structure of the TiO2 NPs, according to the X-ray diffraction patterns. The thermophysical characterization and molecular interaction features of the NFs at 303 K including a novel inorganic-organic T-PSA NC, were detailed. Furthermore, the stability of the T-PSA NC-based NFs was investigated experimentally using the zeta potential, and the particle size distribution change was analyzed using the dynamic light scattering (DLS) method. The T-PSA NCs had particle sizes that were significantly bigger than pristine PSA and pure TiO2. Most of the preparation conditions used to produce the T-PSA NCs resulted in moderately stable suspensions in EG. The results revealed that the ultrasonic velocity increased with the increase in the concentration of T-PSA NC mass % in the NFs, the refractive index and thermal conductivity increased with the increase in the concentration, and the surface tension exhibited a linear change when the ratio of mass % concentration of the T-PSA NCs increased. The combined presence of components that synergistically contribute to the electro, thermal, optical, and rheological properties is expected to attract advanced applications for NFs.

Laboratory investigation of GO-SA-MWCNTs ternary hybrid nanoparticles efficacy on dynamic viscosity and wear properties of oil (5W30) and modeling based on machine learning

In the present study, the properties of ternary hybrid nanofluid (THNF) of oil (5W30) - Graphene Oxide (GO)-Silica Aerogel (SA)-multi-walled carbon nanotubes (MWCNTs) in volume fractions ([Formula: see text] of 0.3%, 0.6%, 0.9%, 1.2%, and 1.5% and at temperatures 5 to 65 °C has been measured. This THNF is made in a two-step method and a viscometer device made in USA is used for viscosity measurements. The wear test was performed via a pin-on-disk tool according to the ASTM G99 standard. The outcomes show that the viscosity increases with the increase in the [Formula: see text], and the reduction in temperature. By enhancing the temperature by 60 °C, at [Formula: see text] = 1.2% and a shear rate (SR) of 50 rpm, a viscosity reduction of approximately 92% has been observed. Also, the results showed that with the rise in SR, the shear stress increased and the viscosity decreased. The estimated values of THNF viscosity at various SRs and temperatures show that its behavior is non-Newtonian. The efficacy of adding nanopowders (NPs) on the stability of the friction and wear behavior of the base oil has been studied. The findings of the test display that the wear rate and friction coefficient increased about 68% and 4.5% for [Formula: see text] = 1.5% compared to [Formula: see text] = 0. Neural network (NN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gaussian process regression (GPR) based on machine learning (ML) have been used to model viscosity. Each model predicted the viscosity of the THNF well, and Rsquare > 0.99.

Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning

This study focuses on predicting the dynamic viscosity of nanofluids, specifically Polyalpha-Olefin-hexagonal boron nitride (PAO-hBN) using machine learning models. The primary goal of this research is to assess and contrast the effectiveness of three distinct machine learning models: Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The main objective is the identification of a model that demonstrates the highest level of accuracy in predicting a nanofluid's viscosity namely, PAO-hBN nanofluids. The models were trained and validated using 540 experimental data points, where the mean square error (MSE) and the coefficient of determination R² were utilized for performance evaluation. The results demonstrated that all three models could predict the viscosity of PAO-hBN nanofluids accurately, but the ANFIS and ANN models outperformed the SVR model. The ANFIS and ANN models had similar performance, but the ANN model was preferred due to its faster training and computation time. The optimized ANN model had an R² of 0.99994, which indicates a high level of accuracy in predicting the viscosity of PAO-hBN nanofluids. The elimination of the shear rate parameter from the input layer improved the accuracy of the ANN model to an absolute relative error of less than 1.89% over the full temperature range (-19.7 °C-70 °C) compared to 11% in the traditional correlation-based model. These results suggest that the use of machine learning models can significantly improve the accuracy of predicting the viscosity of PAO-hBN nanofluids. Overall, this study demonstrated that the use of machine learning models, specifically ANN, can be effective in predicting PAO-hBN nanofluids' dynamic viscosity. The findings provide a new perspective on how to predict the thermodynamic properties of nanofluids with high accuracy, which could have important applications in various industries.

Municipal solid waste treatment for bioenergy and resource production: Potential technologies, techno-economic-environmental aspects and implications of membrane-based recovery

World estimated municipal solid waste generating at an alarming rate and its disposal is a severe concern of today's world. It is equivalent to 0.79 kg/d per person footprint and causing climate change; health hazards and other environmental issues which need attention on an urgent basis. Waste to energy (WTE) considers as an alternative renewable energy potential to recover energy from waste and reduce the global waste problems. WTE reduced the burden on fossil fuels for energy generation, waste volumes, environmental, and greenhouse gases emissions. This critical review aims to evaluate the source of solid waste generation and the possible routes of waste management such as biological landfill and thermal treatment (Incineration, pyrolysis, and gasification). Moreover, a comparative evaluation of different technologies was reviewed in terms of economic and environmental aspects along with their limitations and advantages. Critical literature revealed that gasification seemed to be the efficient route and environmentally sustainable. In addition, a framework for the gasification process, gasifier types, and selection of gasifiers for MSW was presented. The country-wise solutions recommendation was proposed for solid waste management with the least impact on the environment. Furthermore, key issues and potential perspectives that require urgent attention to facilitate global penetration are highlighted. Finally, practical implications of membrane and comparison membrane-based separation technology with other conventional technologies to recover bioenergy and resources were discussed. It is expected that this study will lead towards practical solution for future advancement in terms of economic and environmental concerns, and also provide economic feasibility and practical implications for global penetration.

Biogenic synthesis of reduced graphene oxide decorated with silver nanoparticles (rGO/Ag NPs) using table olive (olea europaea) for efficient and rapid catalytic reduction of organic pollutants

In this work, graphene oxide (GO) sheets were prepared via a facile electrochemical exfoliation of graphite in acidic medium and subsequent oxidation with potassium permanganate. The GO sheets were employed for preparation of reduced GO adorned with nanosized silver (rGO/Ag NPs) using green reduction of GO and Ag(I) via olive fruit extract as a reducing and immobilizing agent. The crystal phase, morphology, and nanostructure of the prepared catalyst were characterized by XRD, SEM, EDX, UV-Vis and Raman spectroscopy techniques. The as-prepared rGO/Ag NPs showed superior catalytic performance towards the complete reduction (up to 99%) of 4-nitrophenol (4-NPH) to 4-aminophenol (4-APH) and rhodamine B (RhB) to Leuco RhB within 180 s using NaBH₄ at ambient condition. The rate constant (k) values were found to be 0.021 and 0.022 s^-1 for 4-NPH and RhB reduction, respectively. In addition, the regenerated catalyst could be reused after seven cycles without losing any apparent catalytic efficiency. Accounting for the excellent catalytic capability, chemical stability and environment-friendly synthesis protocol, the rGO/Ag NPs has great potential working as a heterogeneous catalyst in the transforming harmful organic contaminants into less harmful or harmless compounds.

Examining rheological behavior of CeO2-GO-SA/10W40 ternary hybrid nanofluid based on experiments and COMBI/ANN/RSM modeling

In this study, the rheological behavior and dynamic viscosity of 10W40 engine oil in the presence of ternary-hybrid nanomaterials of cerium oxide (CeO₂), graphene oxide (GO), and silica aerogel (SA) were investigated experimentally. Nanofluid viscosity was measured over a volume fraction range of VF = 0.25-1.5%, a temperature range of T = 5-55 °C, and a shear rate range of SR = 40-1000 rpm. The preparation of ternary-hybrid nanofluids involved a two-step process, and the nanomaterials were dispersed in SAE 10W40 using a magnetic stirrer and ultrasonic device. In addition, CeO₂, GO, and SA nanoadditives underwent X-ray diffraction-based structural analysis. The non-Newtonian (pseudoplastic) behavior of ternary-hybrid nanofluid at all temperatures and volume fractions is revealed by analyzing shear stress, dynamic viscosity, and power-law model coefficients. However, the nanofluids tend to Newtonian behavior at low temperatures. For instance, dynamic viscosity declines with increasing shear rate between 4.51% (at 5 °C) and 41.59% (at 55 °C) for the 1.5 vol% nanofluid. The experimental results demonstrated that the viscosity of ternary-hybrid nanofluid declines with increasing temperature and decreasing volume fraction. For instance, assuming a constant SR of 100 rpm and a temperature increase from 5 to 55 °C, the dynamic viscosity increases by at least 95.05% (base fluid) and no more than 95.82% (1.5 vol% nanofluid). Furthermore, by increasing the volume fraction from 0 to 1.5%, the dynamic viscosity increases by a minimum of 14.74% (at 5 °C) and a maximum of 35.94% (at 55 °C). Moreover, different methods (COMBI algorithm, GMDH-type ANN, and RSM) were used to develop models for the nanofluid's dynamic viscosity, and their accuracy and complexity were compared. The COMBI algorithm with R² = 0.9995 had the highest accuracy among the developed models. Additionally, RSM and COMBI were able to generate predictive models with the least complexity.

Application of sulfur-coated magnetic carbon nanotubes for extraction of some polycyclic aromatic hydrocarbons from water resources

In the present work, novel sulfur-coated magnetic carbon nanotubes (MCNTs-S) material was fabricated by S coating on the MCNTs using a simple heating procedure. TGA, EDX, XRD, TEM, and VSM were employed to characterize the as-prepared composite. Using HPLC-UV system, the produced superparamagnetic sorbent was employed for the extraction and measurement of trace levels of five polycyclic aromatic hydrocarbons (PAHs) in environmental waters. The synergistic effect of the sulfur layer and CNTs substrate is primarily responsible for the remarkable extraction efficiency of the MCNTs-S sorbent towards PAHs. The experimental factors including MCNTs-S dosage, sorption time, elution solvent, ionic strength and solution pH were explored and optimized. Considering that the ionic strength and pH do not have any impact on the PAHs extraction, as a result, there is no need the unnecessary adjustment of the water samples. The linear dynamic ranges and detection limits under optimal conditions were in the range of 0.05-0.11 ng mL^-1 and 0.2-150 ng mL^-1, respectively. The analysis of PAHs in the real samples (sea water and river water) using this approach was successfully assessed with appropriate recovery values (94.6%-99.0%).

Thermal and rheological properties of magnetic nanofluids: Recent advances and future directions

Technological advancement and miniaturization of electronic gadgets fueled intense research on nanofluids as potential candidates for cooling applications as a substitute to conventional heat transfer fluids. Among nanofluids, magnetic nanofluids, traditionally known as ferrofluids have attracted a lot of attention owing to their magnetic field tunable thermal conductivity and rheological properties due to the aggregation of the magnetic nanoparticles into chains or columns in the presence of the magnetic field. The field-induced aggregates act as low resistance pathways thereby improving thermal transport substantially. Recent studies show that ferrofluids with smaller size and narrow size distribution display significant enhancement in thermal conductivity in the presence of a magnetic field with negligible viscosity enhancement, which is ideal for effective thermal management of electronic devices, especially in miniature electronic devices. On the contrary, highly polydisperse ferrofluids containing large aggregates, show modest enhancement in thermal conductivity in the presence of a magnetic field and a huge enhancement in viscosity. The most recent studies show that magnetic field ramp rate has a profound effect on aggregation kinetics and thermal and rheological properties. The viscosity enhancement under an external stimulus impedes their practical use in electronics cooling, which warrants the need to attain a high thermal conductivity to viscosity ratio, under a modest magnetic field. Though there are several reviews on heat transfer in nanofluids and hybrid nanofluids, a comprehensive review on fundamental understanding of field-induced thermal and rheological properties in magnetic fluids is missing in the literature. This review provides a pedagogical description of the fundamental understanding of field-induced thermal and rheological properties in magnetic fluids, with the necessary background, key concepts, definitions, mechanisms, theoretical models, experimental protocols, and design of experiments. Many important case studies are presented along with the experimental design aspects. The review also provides a summary of important experimental studies with key findings, along with the key challenges and future research directions. The review is an ideal material for experimentalists and theoreticians practicing in the field of magnetic fluids, and also serves as an excellent reference for freshers who indent to begin research on this topic.