Search Results (546)

Normalization of entropy generation provides a dimensionless and physically consistent framework for comparing irreversibility mechanisms under different operating conditions, while log-space third-order polynomial regression enables accurate representation of nonlinear coupled transport behavior over wide parameter ranges. Accordingly, this study investigates entropy generation associated with double-diffusive mixed convection in an oblique vented chamber filled with a nano-encapsulated phase change material suspension under local thermal non-equilibrium (LTNE) conditions in a porous medium. The governing equations are formulated by incorporating the effects of inlet and outlet sizes through the Reynolds number to provide a more realistic representation of the physical configuration. The dimensionless governing equations are discretized using the finite volume method based on the control-volume approach. The resulting entropy generation, heat transfer, and mass transfer characteristics, including the average fluid Nusselt and Sherwood numbers, are normalized and correlated using log-space third-order polynomial regression models. An effective RSM approach is employed to perform a sensitivity analysis of the key operating parameters. The results reveal that, for all inlet and outlet configurations, the normalized average fluid Nusselt number increases with increasing nanoparticle volume fraction. Moreover, both the normalized average Sherwood number and normalized fluid entropy generation increase with the Soret coefficient, demonstrating its pronounced influence on double-diffusive transport and irreversibility within the LTNE porous chamber. Full article

The preparation of TiO₂ microspheres by spray drying followed by controlled sintering was investigated. Starting from commercial rutile TiO₂ nanoparticles dispersed in water, spherical microspheres with broad size distributions were produced by adjusting key spray-drying parameters, including solid concentration, gas flow, and suspension composition. The results showed that controlling the spray-drying conditions and the subsequent sintering cycle influenced the resulting microsphere size and morphology, grain evolution, and porosity. A tailored multi-step sintering approach was used to control the microstructural evolution of individual TiO₂ microspheres while limiting inter-microsphere coalescence. SEM and three-dimensional nano-tomography revealed pronounced grain growth and progressive pore elimination during sintering. The grain size increased from approximately 2–5 µm at 1200 °C to 20–40 µm at 1400 °C, while ex situ nano-tomography showed a decrease in pore volume fraction from 15.9% to 1.3% over the same temperature range. In situ nano-holotomography further confirmed the progressive reduction in the connected pore network during heating. Overall, sintering at 1400 °C provided the best compromise between densification, grain growth, microsphere isolation, and preservation of the spherical morphology. Full article

Osteoporosis requires long-term management, yet current pharmacological therapies are limited by safety concerns. Calcium carbonate is a biocompatible and widely used calcium supplement; however, its low absorption efficiency restricts therapeutic effectiveness. Herein, a biomimetic calcium delivery system was constructed by depositing an ultrathin (~10 nm) tannic acid/fish collagen (TA/FC) nanolayer onto CaCO₃ nanoparticles via coordination- and hydrogen bonding-mediated self-assembly. The TA/FC nanocoating modulates cellular internalization by activating multiple endocytic pathways, thereby enhancing nanoparticle uptake and intracellular calcium delivery. As a result, cellular calcium uptake increased by 5.6-fold, and the apparent absorption rate reached 93.2%, 4.9 times higher than that of medical-grade calcium carbonate. In vivo, CaCO₃@TA/FC nanoparticles restored bone metabolic homeostasis, as indicated by increased serum calcium, decreased phosphate and alkaline phosphatase levels, and elevated osteoprotegerin expression. This was accompanied by significant improvements in bone microarchitecture, including increased bone mineral density, bone volume fraction, trabecular number and thickness, as well as recovery of mechanical strength. Overall, this work demonstrates that interfacial engineering enhances calcium bioavailability and improves the therapeutic performance of calcium carbonate. Full article

This study aimed to develop polymer matrix nanocomposites reinforced with Al₂O₃ nanoparticles to enhance the mechanical properties of PMMA. The composite was fabricated via friction stir processing (FSP) to ensure the homogenous dispersion of Al₂O₃ nanoparticles in the polymer. A grid of 5 holes in a 7 × 7 mm² area was made in which the hole diameter was varied from 1.77 mm to 2.28 mm to obtain different volume fractions of reinforcement ranging from 15% to 25%. The holes were made with a depth of 3 mm in a 4 mm-thick PMMA sheets. After packing the Al₂O₃ powder in the holes, a 2 mm-thick PMMA sheet was used as a cover to prevent the sputtering of nanoparticles. A number of FSP parameters were examined. The tool rotation rates ranged from 800 to 1200 rpm, traverse speeds of 25 and 50 mm/min, and tool tilts of 1 and 2° were used. A soft paraffin (Vaseline) layer was used on the top surface to prevent severe shoulder friction with the PMMA plate, which caused severe wear and thinning on the surface. For the developed PMMA/Al₂O₃ nanocomposites, the surface quality, SEM microstructure, impact energy, and transverse hardness were investigated. Good surface quality and dispersion of nanoparticles were attained by employing adequate processing conditions. The experimental results indicated that as the nanoparticle percentage increased, impact energy, hardness, and tensile strength increased, reaching 2 kJ/m², 14.7 HV, and 52.1 MPa at a nanoparticle concentration of 25%. This means that the polymer ceramic composite’s toughness, hardness, and tensile strength are higher than those of unprocessed PMMA by 66%, 33%, and 23%, respectively. Full article

The thermo-hydraulic performance of Ni-Fe₃O₄/water hybrid nanofluid (HNF) in a shell and helical tube heat exchanger (SHTHE) was investigated numerically and experimentally. A Eulerian mixture model was employed to simulate the two-phase flow behavior of the HNF and analyze the impacts of nanoparticle volume fraction and mixing ratio on the heat-transfer and flow properties under turbulent flow. Numerical simulations were performed at nanoparticle volume fractions (φ) of 0.1%, 0.5%, and 1% and Ni:Fe₃O₄ mixing ratios (MRs) of 1:4, 1:1, and 4:1. Experimental measurements at a nanoparticle volume fraction of 0.1% for different MRs were used to validate the numerical predictions. The numerical results showed that the nanoparticle volume fraction and mixing ratio significantly affected the thermal performance, whereas their influence on the hydraulic behavior remained relatively limited. At the highest Dean number, MR (4:1) and φ = 1% yielded the highest thermal performance, reaching a 56% enhancement in the Nusselt number, whereas the friction factor increased by only 6%. In addition, MR (4:1) exhibited the highest average thermal performance index of 1.4. Furthermore, good agreement was achieved between the numerical predictions and experimental measurements, confirming the validity of the proposed numerical model. These findings demonstrate the applicability of the validated Eulerian mixture model for evaluating the thermo-hydraulic performance of Ni-Fe₃O₄/water HNF in SHTHEs, particularly for achieving heat-transfer enhancement with limited hydraulic penalties. Full article

Glass-ceramics are inorganic, non-metallic materials obtained by controlled crystallization of glasses through different processing routes; they contain at least one functional crystalline phase together with a residual glass, and the crystallized fraction may range from trace levels to nearly complete crystallization. Transparent glass-ceramics (TGCs) constitute the optically transparent subset of this class and combine a controlled crystalline microstructure with a residual amorphous matrix. Their transparency distinguishes them from conventional opaque glass-ceramics and is achieved by minimizing light scattering through careful control of crystallite size, volume fraction, spatial distribution, and refractive-index mismatch between the crystalline and glassy phases. Unlike conventional sintered ceramics, TGCs retain many of the processing advantages of glass while incorporating crystalline phases that can enhance mechanical, thermal, optical, or functional properties. Depending on their composition and microstructure, TGCs may exhibit improved hardness, fracture toughness, thermal stability, chemical durability, luminescence, nonlinear optical response, or ion-exchange strengthening capability. These features make TGCs attractive for applications requiring both optical clarity and advanced performance, including protective cover glass, transparent armour, precision optical substrates, laser and photonic components, optical sensors, and multifunctional host materials for rare-earth ions and nanoparticles. Full article

Triply periodic minimal surface (TPMS) heat exchangers offer high surface-area-to-volume ratios and interconnected flow pathways, making them attractive for compact thermal management. However, accurately predicting nanofluid heat transfer over a wide range of nanoparticle concentrations and operating conditions in complex TPMS geometries remains computationally challenging because of the coupled effects of porous architecture, flow dynamics, and concentration-dependent thermophysical properties. In this study, a hybrid physics-informed neural network (PINN) framework was developed to reconstruct concentration-dependent ${Al}_2{O}_3–water$ nanofluid temperature fields in TPMS gyroid heat exchangers. The originality of the proposed approach lies in integrating sparse thermocouple measurements, a steady-state convection–diffusion equation, boundary condition residuals, concentration-dependent nanofluid property models, and a physics-based concentration scaling procedure within a unified framework. The proposed framework was applied to aluminum and silver TPMS heat exchangers over a wide range of nanofluid volume fractions and flow conditions. The trained PINN accurately reconstructed the experimentally measured temperature field, demonstrating excellent agreement with the reference experimental data. Predictions at concentrations beyond the experimentally measured reference condition were obtained using the physics-based concentration scaling model. The effective heat transfer coefficient and Nusselt number were subsequently evaluated from the predicted mean TPMS temperature through an energy balance formulation. Increasing nanoparticle concentration reduced the predicted TPMS temperatures by approximately 17.5–18.5%, while the combined increase in concentration and flow rate produced an overall temperature reduction of about 33.5%. Relative to the selected baseline condition, the combined variation in concentration and flow rate was associated with calculated increases of 62.08% in $h_{eff}$, 58.33% in Nu, and 59.32% in Re. These results demonstrate the potential of the proposed hybrid PINN framework as a computationally efficient surrogate for evaluating nanofluid-enhanced TPMS heat exchangers, while acknowledging that predictions away from the training concentration depend on the validity of the concentration scaling model. Full article

Nanostructured copper oxide (CuO) holds significant promise for optoelectronic, photocatalytic, and biomedical applications, yet its performance is critically dictated by its morphology, crystallite size, and surface chemistry. In this work, pure monoclinic CuO nanoparticles were successfully synthesized via a facile, kinetically controlled aqueous co-precipitation route using three distinct precursor salts: copper acetate (CO-Ac), copper sulfate (CO-S), and copper chloride (CO-Cl). To achieve precise architectural control, the synthesis was conducted near the thermodynamic solubility limit of the precursors combined with an abrupt NaOH injection, effectively decoupling the nucleation stage from crystal growth. Rietveld refinement of X-ray diffraction (XRD) data confirmed the structural integrity of the monoclinic lattice across all samples, χ² = 1.04 − 2.02, crystallinity 53–55%, while demonstrating that the precursor anion strongly governs the volume-averaged crystallite size, which expanded from 16 nm (CO-Ac) to 30 nm (CO-S) and 52 nm (CO-Cl). Morphological analyses revealed that acetate acts as a non-specific capping ligand, promoting isotropic, quasi-spherical nanoparticles that aggregate into high-surface-area (69.04 m²/g) “bead-chain” assemblies. Conversely, sulfate and chloride ions act as shape-directing agents via facet-selective adsorption on nucleation seeds, yielding two-dimensional plates and anisotropic acicular/needle-like architectures, respectively. X-ray photoelectron spectroscopy (XPS) and modified Auger parameter (α ~1851 eV) analyses confirmed the absolute dominance of Cu²⁺ states, with a minor fraction (~2.5 mol %) of lower-coordinated surface edge states. XPS further unveiled that the strongly alkaline environment (pH ~14) drives precursor-dependent surface chemistry: CO-Ac nanoparticles retain a clean, hydroxylated layer with minor acetate residues, whereas CO-S and CO-Cl samples develop a passive copper hydroxycarbonate (Cu₂(OH)₂CO₃) surface barrier that blocks active sites and reduces porosity. Optical properties analyzed via UV-vis diffuse reflectance revealed a pronounced, size-dependent blueshift relative to bulk CuO, with fundamental indirectly allowed bandgaps of 2.6 eV, 2.36 eV and 1.93 eV for CO-Ac, CO-S and CO-Cl samples, while the direct bandgaps shifted from 3.0 eV, 3.2 eV, and 3.57 eV for the mentioned samples. This behavior is attributed to quantum confinement governed by fine individual nanocrystals. These findings establish that precursor engineering offers a robust pathway to tailor the morphological, optical, and interfacial properties of CuO nanostructures for targeted functional devices. Full article

Photoacoustic signal analysis in weakly absorbing media remains challenging because of low signal-to-noise ratios. This work proposes a deep learning framework for classifying particle size and concentration in an indirect absorption configuration. We conducted a comparative study using raw temporal signals, Savitzky–Golay filtering, and time–frequency scalograms via Continuous Wavelet Transform (CWT), and evaluated both 1D and 2D convolutional neural network architectures. Experimental validation was performed using poly(methyl methacrylate) (PMMA) microspheres (6 μm and 15 μm) and hydroxyapatite nanoparticles (<200 nm) at volume fractions as low as $6\times {10}^{-4}\%$. While raw signals led to unstable training (accuracy ≈ 47%), CWT-based representations significantly improved performance, achieving near-perfect size discrimination and over 96% accuracy in discrete volume-fraction classification. Grad-CAM analysis confirmed that the model identifies physically meaningful regions of the acoustic waveform, ensuring interpretability. The proposed framework was validated under controlled experimental conditions using discrete particle types and predefined volume-fraction classes, providing a foundation for future extensions toward continuous particle characterization. Ultimately, these findings demonstrate that combining time–frequency representations with deep learning provides a robust, physically consistent approach for particle characterization in turbid media, with significant potential for biomedical diagnostics and material analysis. Full article

Iron oxide-based nanomaterials have attracted considerable interest owing to their unique magnetic properties and potential biomedical and environmental applications. In this study, starch-coated superparamagnetic Fe₃O₄ nanoparticles (Sta-MNP) were synthesized and comprehensively characterized using electron microscopy (TEM, SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), vibrating sample magnetometry (VSM), attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR), and nanoparticle tracking analysis (NTA). The results confirmed the formation of a magnetite-based iron oxide nanoparticles sample with a median physical diameter of 12.24 nm, superparamagnetic behavior with a saturation magnetization of 59.81 emu/g, and effective starch coating on the nanoparticle surface. The biological effects of Sta-MNP were assessed in Triticum aestivum L. using the mitotic index (MI) and aberration index (AI) as cytogenetic endpoints, respectively. Exposure-induced concentration-dependent increases in both parameters across the tested volume fractions (0–200 µL/L), suggesting a significant interaction between Sta-MNP and dividing cells. Overall, this study provides a comprehensive physicochemical profile of starch-coated magnetite nanoparticles and demonstrates their potential cytogenetic impact in a plant model system, supporting further investigation of their environmental interactions and potential agricultural applications. Full article

Epoxy resin (EP) exhibits pronounced intrinsic brittleness arising from the highly crosslinked network formed after curing, thereby restricting its application in load-bearing structures. Although TiO₂ nanoparticles possess the potential for impact-strength improvement, they are highly prone to aggregation, which compromises stress-transfer efficiency within the composite. To overcome this challenge, a ball-milling strategy is adopted to anchor TiO₂ nanoparticles onto three-dimensional graphene oxide (3DGO), leading to the successful fabrication of a 3DGO@TiO₂ hybrid filler. At an ultralow loading of 0.03 wt%, the 3DGO@TiO₂ epoxy resin composite shows a 221.5% increase in impact strength to 19.55 kJ/m² and 33.53% and 32.34% increases in tensile and flexural strength to 64.32 MPa and 96.17 MPa, respectively, relative to neat EP. Morphological analyses indicate that the 3DGO spatial confinement reduces TiO₂ aggregate characteristic length by 55.1% from 1123 nm to 504 nm. Molecular dynamics simulations show that the hybrid filler decreases fractional free volume to 17.6%, induces denser matrix packing, and increases the calculated physical interfacial energy to 1023 kcal/mol, which is 2.2 times that of the pure TiO₂ epoxy resin system. This work confirms that 3DGO simultaneously optimizes nanofiller dispersion and physical confinement, offering a novel strategy for high-performance epoxy composites at ultralow loadings. Full article

This study presents a numerical investigation of heat transfer enhancement in a corrugated channel equipped with concave-up obstacles and hybrid nanofluids. The novelty of the present work lies in the combined evaluation of a new obstacle configuration with five different nanoparticles and hybrid nanofluids at two volume concentrations using both single-phase and two-phase numerical models. Numerical simulations were carried out using ANSYS Fluent 19.2 with a two-phase mixture model. Five types of nanoparticles (${\mathrm{S}\mathrm{i}\mathrm{O}}_2$, ${\mathrm{T}\mathrm{i}\mathrm{O}}_2$, ${\mathrm{A}\mathrm{l}}_2{\mathrm{O}}_3$, ZnO, and CuO) were tested at volume fractions of 1% and 2%, with obstacles optimized in size and position to enhance fluid mixing, over a Reynolds number range of 10,000–30,000. The combined application of concave-up obstacles and nanofluids increased the heat-transfer performance by approximately 244% in terms of percentage enhancement (PE) relative to the baseline corrugated channel using water without obstacles. Despite a considerable pressure drop (up to 15.5 times the baseline pressure ratio (PR)), the performance evaluation coefficient (PEC) indicates an effective trade-off, with the ${\mathrm{A}\mathrm{l}}_2{\mathrm{O}}_3$–ZnO (50:50) hybrid nanofluid (Case 2) achieving a balanced thermal–hydraulic performance with a PEC of 1.28. These findings demonstrate that the combined application of corrugated channels, obstacles, and hybrid nanofluids is a highly effective strategy for improving heat exchanger efficiency in practical applications. Full article

Hybrid nanofluids combining thermally conductive nanoparticles with latent heat-storing nanocapsules have attracted growing interest for near-ambient liquid-based thermal management, yet direct comparisons between mono and hybrid phase-change-material-containing systems on a common experimental basis remain scarce. In this work, water-based mono Al₂O₃, mono polyurethane-nanoencapsulated n-nonadecane (PU-NEPCM), and Al₂O₃/PU-NEPCM hybrid nanofluids were prepared under identical surfactant, sonication, and dispersion conditions, and their thermal conductivity, dynamic viscosity, and Day-1 colloidal stability were characterized over 298–313 K at total volume fractions of 0.1, 0.3, and 0.5 vol.%, with the hybrids prepared at a 50:50 volumetric ratio. At 0.5 vol.% and 313 K, the hybrid (NFH3) exhibited the highest thermal conductivity enhancement (+8.27%), exceeding the corresponding mono Al₂O₃ and mono PU-NEPCM nanofluids by 4.6 and 5.2 percentage points, respectively, while maintaining a moderate viscosity penalty. The hybrid formulations also achieved |ζ| = 32–37 mV, exceeding the conventional electrostatic-stabilization threshold and outperforming both mono families. A two-factor analysis of variance (ANOVA) identified particle concentration as the dominant factor governing both properties (p < 0.001), with temperature becoming statistically significant only for the hybrid viscosity (p = 0.043). The synergy index varied between 0.85 and 1.43 across the tested conditions—reaching values of 1.20–1.43 for the lowest-loaded hybrid (NFH1)—while the performance index remained close to unity (0.97–1.01). These results identify low-loaded Al₂O₃/PU-NEPCM hybrid nanofluids as a balanced and stable candidate for near-ambient liquid-based thermal management applications. Full article

This study investigates the thermal optimization of unsteady nanofluid natural convection within a quarter-circular domain with an inner wavy boundary under inclined periodic magnetic forcing. A combined finite-element method (FEM) and central composite design-based response surface methodology (RSM) is employed for optimizing both the geometric configuration and the parametric setting. For the geometric optimization, we find that the wavy-wall amplitude is the key parameter to determine the optimal configuration, followed by the inner radius and undulation number. The parametric analysis shows that strong magnetic effects suppress convection, while increasing the Rayleigh number and the nanoparticle volume fraction significantly enhances heat transport. Additionally, rising magnetic field wavelength and/or inclination angle result in a reduction in heat transmission under strong magnetic intensity. A statistical quadratic correlation equation with the help of the RSM method between Rayleigh number, Hartmann number, and nanoparticle volume fraction, and the mean Nusselt number is formulated, which gives a good match with numerical FEM analysis results in which about 99% of the variation in the response variable is predicted (R² = 0.9975). The results obtained in this study offer valuable information along with computational efficiency in predicting the behavior of such advanced thermal systems. Full article

A numerical investigation of forced convection heat transfer in a three-dimensional T-shaped bifurcating channel with an upstream rotating cylinder and a downstream vibrating wavy wall is presented. The working fluid is a ternary hybrid nanofluid (Fe₂O₃, CuO, MoS₂ in water) exhibiting Casson rheology under an inclined magnetic field. The novelty of this work lies in the first integrated configuration combining these simultaneous mechanical, magnetic, and non-Newtonian effects. Using COMSOL Multiphysics, 413 parametric combinations of Reynolds number, Hartmann number, Casson parameter, nanoparticle shape and volume fraction, magnetic field angle, cylinder rotation speed, wall amplitude (Am), and period were solved. Average Nusselt and Bejan numbers quantified heat transfer enhancement and thermodynamic irreversibility. To interpret the high-dimensional parameter space and to circumvent the prohibitive computational cost of additional 3D magnetohydrodynamics simulations, machine learning (XGBoost) models were developed to rank feature importance and provide fast, accurate surrogate predictions (R² > 0.99). Cylinder rotation dominates heat transfer, increasing the Nusselt number by over 980% (feature importance 0.42) with a modest entropy penalty. Nanoparticle volume fraction reduces the Nusselt number via viscous damping. Magnetic field parameters negligibly affect heat transfer but strongly influence entropy generation; a perpendicular field recovers up to 97% thermal efficiency at high Hartmann numbers. Full article