Abstract Thermally stratified, disorder and dissipated nonlinear nanomaterials’ role in the optimization of heat transfer in many practical fields is determined to be very effective. Dynamical configuration of the relevant materials with the addition of particle diffusion and thermophoretic forces introduced highly predictable scenarios during many production processes. This significance is even more meaningful if the role of viscous dissipation, thermal stratification, entropy generation, Newtonian heating, electric and magnetic fluxes are considered for the optimization of heat transfer via time-dependent rotating motion carrying a hybrid type, i.e., Careau micropolar fluid. Moreover, the effective rotating motion of such materials between the two disks leads to a more practical problem with the consideration of the inclusion of generalized nano-type materials that further explore the importance of two types of forces, i.e., Brownian and thermophoretic. The complex dynamical conduct is formulated mathematically by utilizing the conservation laws, including conservation of momentum, volume fraction, thermal balance, and mass. Remarkable findings are portrayed through the pictorial representation of the hybrid materials’ speed, volume fraction, thermal balance, and related surface-oriented physical quantities. The entire analysis is based on the boundary layer bounded rotating flow between two clockwise and anticlockwise rotating and stretching disks. Built-in command in MATLAB is exercised for the approximation of the newly proposed problem with a higher degree of convergence. The higher time factor appears in the flow, causing a reduction near the disk’s surfaces for all three cases (0 < n < 1, n = 1, n > 1) of generalized materials. Brownian motion, Joule heating, and thermophoretic forces enhanced the thermal status of the materials, and the thermal stratification factor caused a reduction in the thermal behavior of the materials. The entropy generation grows for higher diffusion and Brinkman numbers, and an opposite demeanor is noticed for the Bejan number. The stretching factors reduced the tangential skin frictions. The current methodology is validated through a suitable/appropriate comparison with already available works.
Abstract Gastric cancer (GC) is one of the leading causes of death globally, and its early diagnosis is hindered by significant technical limitations. Metal nanoparticles (MNPs), characterized by nanoscale size, excellent targeting capabilities, and multimodal imaging properties, can substantially enhance detection sensitivity and specificity. This paper reviews recent advances in the application of MNPs for early-stage gastric cancer (EGC) diagnosis, highlighting their considerable potential to overcome the limitations of traditional diagnostic methods and revolutionize early detection strategies. We systematically describe how combining MNPs with existing imaging techniques has significantly improved lesion visualization and diagnostic accuracy for GC. Additionally, MNP applications in liquid biopsy, microfluidic chips, biosensors, nanotheranostic integrated platforms, and artificial intelligence (AI)-assisted diagnosis have demonstrated remarkable advancements in detecting GC biomarkers, identifying circulating tumor cells (CTCs), analyzing exosomes, and enhancing multimodal imaging. The innovative use of MNPs in GC early diagnosis presents broad prospects and provides crucial technological support for precision medicine.
Abstract Nanocomposites made from biodegradable polymers reinforced with nanocellulose have become quite attractive for sustainable electromechanical applications. In this work, nanocellulose extracted from roselle (RCNC) has been added to polyvinyl alcohol (PVA) through solution casting to prepare flexible nanocomposite films that offer tuneable structure-property-performance attributes. The effect of RCNC loading (0–7 wt%) on the structure, mechanical, thermal, morphological, and physical properties of PVA/RCNC nanocomposites was explored. XRD and FTIR analysis suggested that RCNC acts as a nucleating and reinforcing agent that promotes structural organization and enhances the intermolecular hydrogen bonding in PVA while maintaining the same chemical structure. FESEM analysis showed uniform distribution and formation of a well-connected fibrillar network at low to moderate levels of RCNC, while aggregation occurs at high levels. Significant improvement in mechanical properties was achieved, resulting in up to a 260 % increase in tensile strength and 360 % increase in modulus when the amount of RCNC was optimized to 5 wt%. This is due to limited mobility of polymer chains caused by aggregation and restricted movement. The thermal stability of nanocomposites improved for low to moderate amounts of RCNC, while water absorption and moisture content increased with RCNC content. These results show that structural organization, interface interaction, and dispersion of nanoparticles play important roles in achieving good performance of nanocomposites. Optimal 5 wt% level of RCNC is obtained for mechanical reinforcement, thermal stability, and controllable hygroscopicity. Therefore, such nanocomposites have promising potential for the preparation of biodegradable piezoelectric sensing and energy harvesting devices.
Cement-based composites incorporating carbon nanomaterials (CNMs) have attracted increasing attention as multifunctional construction materials owing to their potential to integrate mechanical performance, durability performance, electrical performance, and piezoresistive response. This review summarizes recent advances in cement-based composites incorporating graphene-based materials, carbon nanotubes (CNTs), carbon nanofibers (CNFs), and nanocarbon black (NCB). The intrinsic characteristics of different CNMs are compared, followed by a discussion of dispersion approaches, interfacial regulation strategies, and composite fabrication routes. Particular emphasis is placed on conductive network formation, percolation behavior, and conduction mechanisms, which govern electrical performance and piezoresistive response. The effects of CNMs on mechanical performance, durability performance, electrical performance, and piezoresistive response are then reviewed. Multifunctional applications, including structural health monitoring (SHM), electrothermal de-icing and snow melting, and electromagnetic interference (EMI) shielding, are discussed alongside emerging functions involving electrostatic protection, self-cleaning and anti-fouling, electrochemical maintenance, energy storage, and energy harvesting. Finally, challenges related to dispersion scalability, cost, environmental impact, long-term service reliability, and field validation are identified. This review integrates fundamental principles with multifunctional applications and may serve as a useful reference for future research and engineering development of CNM-incorporated cement-based composites.
The sustainable synthesis of metallic nanoparticles (MNPs) using plant extracts represents a paradigm shift in catalysis toward environmentally benign methodologies that align with green development and chemistry principles. Phytochemical-mediated bio-reduction employs diverse secondary metabolites, including polyphenols, flavonoids, and terpenoids, as both reducing and stabilizing agents, eliminating toxic chemicals while enhancing biocompatibility. Quantitative analysis reveals that plant-synthesized nanoparticles achieve turnover frequencies (TOF) ranging from 17,490 to 18,724 h−1 in organic transformations, with yields of 85–91 %, comparable to those of conventional catalysts. Life cycle assessment demonstrates superior environmental performance with reduced E-factors (0.2–0.8 vs. 2.5–15 for chemical synthesis) and enhanced atom economy (78–95 %). However, standardization challenges, batch-to-batch variability (±15–25 %), and scale-up limitations remain significant barriers to commercialization. This review provides critical perspectives on sustainable nanoparticle catalysis, addressing mechanistic understanding, performance benchmarking, and future research directions toward industrial implementation.
The present study explores the MHD flow and heat transmission characteristics in a Casson nanoliquid over a nonlinearly stretched sheet, based on the Neural Network Backpropagation approach. The parameters controlling the mathematical formulation of the problem are Casson liquid parameter, power-law number, random motion and thermophoresis of nanomaterials, magnetic strength and Schmidt number. The boundary value problem is solved by the BVP4c numerical method, which is also used for the generation of the reference dataset for Neural Network Backpropagation. Solution graphs and error analyses for different parameter variations are evaluated in MATLAB based on this dataset. Regression analyses, error histograms, and determination of the mean squared error serve as tools to assess the accuracy of Neural Network Backpropagation. The MHD-Casson nanofluid model is worked on using test, validation, and training approaches. The influences of concentration, temperature distribution, and fluid flow on influential parameters are further analyzed. From the results, it is also discerned that with increasing Casson fluid parameter, both the temperature and nanoparticle volume fraction go higher.
To characterise complex non-Newtonian motion, the Williamson-Casson (WC) nanofluid flow past a curved, elongating sheet is considered, which is important for promoting heat transfer, nanotechnology, and controlling flow. It is used in advanced cooling systems, extrusion of plastics, bio-medical (blood flow simulation), and in the manufacturing industry. The present paper provides a comprehensive numerical exploration of the unsteady MHD flow of a WC nanofluid past a curved stretching sheet, using the Cattaneo-Christov heat flux model. The impact of ferro-hydrodynamics and the use of complex boundary conditions, such as melting, second order slip, and chemical reactions are also examined. The flow equations are reformed into a system of non-dimensional ODEs. The numerical solution is obtained through the parametric continuation method (PCM) to generate the date for artificial neural networking based (ANN) on the Levenberg-Marquardt back-propagation algorithm (LMBPA). To ensure the legitimacy of the numerical outputs, the results are compared with published findings, and reveals the percent error up to 0.00059480 % by taking Rd = 0.3 (thermal radiation parameter). From the graphical results, it can be observed that the skin friction drops up to 9.89189 % and 48.0315 %, whereas the energy transfer rate enhances by up to 21.6389 % and 41.4158 % by varying the 1st order and 2nd order slip parameters from 0.4 to 0.7, respectively. The skin friction increases up to 27.311 %, while the Nusselt number drops by up to 8.2858 % by varying the ferrohydrodynamic interaction factor from 1.5 to 3.5. Furthermore, the ANN-LMBPA show a very steady training procedure as the magnitude of the gradient and adaptive learning rate essentially converged towards near-zero values (e.g., 9.9 × 10−9 and 1 × 10−9) at the end of up to 597 epochs.
The near-infrared (NIR) emission and the excellent excitonic properties of carbon nanotubes (CNTs) are the basis for their potential use as nanoscale light sources in photonics, biomedical applications, and quantum communication. However, these are impeded by exciton quenching, environmental instability, and, in terms of chirality control, but only limited control. In this review, we summarize in depth the recent progress in interfacial engineering strategies to address these challenges, such as chemical defect functionalization, protective encapsulation, nanoscale heterostructure integration with two-dimensional (2D) and photonic materials, and chirality-selective self-assembly. It also reviews the photoluminescence (PL) and electroluminescence (EL) processes of CNT emitters, with a particular focus on the effect of interface interaction on exciton dynamics, emission efficiency, and device performance. Applications in silicon photonics, single photon sources for quantum optics, flexible optoelectronics, and NIR bio-imaging are critically discussed and key challenges, including dark exciton bottlenecks, interfacial degradation, scalable fabrication, and deterministic control of chirality, are discussed. The current review includes an overview of interfacial engineering, optical emission mechanisms, heterostructure design, and new surface engineering optimization strategies based on AI, unlike previous reviews that have concentrated on either CNT emitters or single interface modification strategies. It also pinpoints existing research gaps and outlines future directions for the development of high efficiency, stable and scalable CNT-based photonic and optoelectronic devices.
Modifying the functional qualities of nanomaterials is made possible by defect engineering, a difficult but powerful method. The reason is that the synthesis conditions have a significant impact on the formation of flaws, making it difficult to reproduce them. In deterministic defect-engineered nanomaterial manufacturing settings, by explicitly modelling defect architectures as intermediate representations, we present an AI-guided process optimisation framework that integrates uncertainty-aware Bayesian exploration with dual-stage defect–property learning, going beyond Gaussian process optimisation and reinforcement learning. Using experimental synthesis, improved defect characterisation, and machine-learning-based predictive modelling, this methodology quantifies the effects of synthesis parameters on defect states and functional performance. Rapid convergence on optimal synthesis settings and reduction of redundant trials are both made possible by learning through iteration and optimisation with an awareness of uncertainty. Materials optimised by artificial intelligence exhibit superior performance, reproducibility, and robustness compared to those traditionally synthesised. Optimal defect windows improve the material’s functional properties. A sensitivity study of key synthesis factors causing fault production can provide actionable insights for scalable nanomanufacturing. This study’s results show that AI-driven defect engineering could be used to develop nanomaterials with predictive capabilities, durability, and an emphasis on energy, catalysis, sensing, and medical applications.
This study employs DFT first-principles calculations to investigate the structural, elastic, mechanical, thermal, electronic, optical, and thermoelectric properties of the bromide double perovskites A2AuBiBr6 (A = K, Rb, Cs). The computed cubic (Fm3̄m $Fm\bar{3}m$ ) structural parameters and stability are in good agreement with the previously reported theoretical results, confirming the structural stability of these A2AuBiBr6 compounds. The calculated elastic constants satisfy the Born stability criteria, while the negative formation energies indicate thermodynamic stability. Mechanical analysis reveals ductile behavior that is enhanced by A-site substitution. Electronic-structure calculations performed using both the GGA-PBE and TB-mBJ approaches show that all A2AuBiBr6 compounds are narrow indirect band-gap semiconductors with promising optoelectronic properties. Thermoelastic analysis predicts favorable melting temperatures, Debye temperatures, and average sound velocities, indicating good thermodynamic stability. The TB-mBJ approach further improves the description of the electronic structure and related physical properties. Thermoelectric transport calculations performed using the BoltzTraP package reveal high figure-of-merit values of ZT = 0.911, 0.868, 0.732 for A2AuBiBr6 (A = K, Rb, Cs), respectively, suggesting promising thermoelectric performance. Overall, the investigated A2AuBiBr6 double perovskites exhibit attractive narrow-gap semiconducting, optical, and thermoelectric characteristics, indicating their potential for thermoelectrics energy-harvesting, infrared optoelectronic, and other green-energy applications.
The present study examines the behaviour of micropolar fluid colloidal blends of magnetic nanoparticles (Fe3O4, CoFe2O4, Mn-ZnFe2O4) suspended in water. The analysis focuses on stagnation point flow over a stretching/shrinking sheet in the context of incompressible micropolar fluid. Such fluids are gaining importance in biomedical applications. The main aim of this research is to observe the temperature and concentration behaviour with magnetic field, heat source and chemical reaction with ternary hybrid nanofluids at prescribed surface temperature (PST) and prescribed surface concentration (PSC). We quantify the effects of heat source/sink, velocity slip, Schmidt number and chemical reaction. The mathematical model is constructed by utilising micropolar fluid theory and the boundary layer approximation. To facilitate analysis, nonlinear partial differential equations are transformed into dimensionless nonlinear ordinary differential equations using appropriate similarity transformations. The analytical examination of this system yields a singular solution for the stretching sheet and two-fold solutions for the shrinking sheet. The main findings are as follows. Increasing the value of the heat source/sink contributes to an increase in fluid temperature, and the opposite behaviour is observed while increasing the velocity slip on both the stretching and shrinking sheets. Increasing the chemical reaction parameter results in a decline in fluid concentration on both the stretching and shrinking sheets. Increasing the magnetic field (M) decreases the skin friction coefficient for the stretching surface, and the opposite behaviour is observed in the shrinking sheet. Increasing the heat source results in a decline in Nusselt number; opposite behaviour is observed in Sherwood number for increasing the chemical reaction on both the stretching and shrinking sheets.
The creation of 3D-printed biocompatible composite polymers has become a significant field of research in additive manufacturing during the fourth industrial revolution because of its promise for high-performance and sustainable material development. Polylactic acid (PLA), polycaprolactone (PCL) and polybutylene adipate-co-terephthalate (PBAT) are biocompatible polymers that have specific mechanical, thermal, and degradation properties, which have made them highly applicable in medical, industrial, aerospace, defence, and social applications. Addition of natural fibres and bio-based additives also strengthens their mechanical, tribological and microstructural properties and thus high-performance composite materials are produced. This paper provides an in-depth discussion of the process-structure-property relationships, focusing on how the most relevant 3D printing parameters influence the end performance of the printed parts, as well as the issues of hydrophilicity, processing constraints, and degradation behaviour in terms of advanced polymer-based additive manufacturing methods. In the future, it is anticipated that the use of biodegradable polymer composites in additive manufacturing will increase due to ongoing advancements in material development, process optimization, and intelligent manufacturing.
Geopolymer concrete is a sustainable alternative to ordinary Portland cement concrete. However, challenges remain regarding its mechanical performance, durability, and microstructural stability under various exposure conditions. This review examines the influence of nano fly ash (nFa) and nano slag (nSg) on the mechanical properties, durability, and microstructural characteristics of geopolymer concrete, based on strength tests, ultrasonic pulse velocity, water absorption, permeability, and SEM, FESEM, and XRD analyses. The reviewed findings indicate that nFa can improve compressive, splitting tensile, and flexural strengths by up to 40 %, 56 %, and 25 % respectively, while nSg has been reported to enhance these properties by up to 59 %, 73 %, and 79 %, respectively. Both nanomaterials contribute to matrix densification, pore refinement, and reduced permeability, thereby improving durability performance. Furthermore, the formation of a compact geopolymer gel structure enhances the interfacial transition zones while XRD observations indicate the development of reaction products such as N-A-S-H, C-S-H, and C-A-S-H gels. However, the findings highlight a significant research gap regarding the combined use of nFa and nSg, particularly with respect to the long-term durability of nSg-based and hybrid nano-modified geopolymer systems beyond 90 days. Future studies should focus on optimizing nanoparticle dosages, dispersion methods, and curing regimes, as well as experimentally validation the potential synergistic effects of combining nFa and nSg for sustainable geopolymer concrete applications.
Nanocomposite films containing 5–30 wt% gold nanoparticles were prepared by solution casting. Their optical properties and calculated gamma-ray shielding performance were investigated. XRD indicates that the films retain the semicrystalline structure of PVA, whereas FTIR indicates changes in the PVA bonding environment, consistent with a modified polymer network following Au incorporation. The UV–Vis–NIR spectra show enhanced absorption and a visible Au surface-plasmon-resonance band. Within the Au-containing series, two apparent Tauc transition energies decrease from 2.02 to 1.75 eV and from 3.15 to 2.27 eV as the nominal Au loading increases from 5 to 30 wt%. The refractive index, optical conductivity, and model-estimated linear and third-order optical susceptibilities increase across this series. The Phy-X/PSD calculations, based on nominal compositions and density inputs, predict increasing GMAC and Zeff, and decreasing GHVL and GMFP, with increasing Au loading, with a distinct Au K-edge feature near 80.7 keV (0.0807 MeV). PVA/30Au provides the best calculated attenuation parameters among the investigated nominal compositions. These calculations do not constitute experimental validation of gamma-ray transmission, and direct microscopic characterization is required to establish nanoparticle distribution.
Scientists are interested in nanomaterials because they offer various possible uses in industrial operations and thermal engineering systems. This study presents a novel unified analysis of a Jeffrey tri-hybrid nanofluid incorporating electroosmosis, magnetohydrodynamics, coupled chemical reactions, and entropy generation in an inclined channel, a combination not previously reported in the literature. The proposed model aims to enhance heat and mass transfer for efficient microfluidic, thermal, and biomedical applications. The analysis takes into account the effects of electroosmosis, as well as homogeneous-heterogeneous chemical processes. A study is performed to analyze the effects of the thermophoresis parameter and Brownian motion parameter. The technique of self-similar conversions is used to transform the governing partial differential equations (PDEs) of the fixed frame into ordinary differential equations (ODEs) of the wave frame. The resulting system is solved numerically using the bvp4c solver in MATLAB under long wavelength and low Reynolds number assumptions. Graphical analysis is used to study the fluid’s velocity, temperature, concentration, and electroosmosis characteristics. Contour plots are used to examine the changes in entropy generation, heat transfer rate, and skin friction coefficient concerning physical parameters. The results indicate that temperature increases with higher values of the Brownian motion parameter, thermophoresis parameter, Prandtl number, and homogeneous reaction heat parameter. The velocity profile initially decreases to zero and then increases in the latter region with increasing values of the Grashof number. Additionally, key physical parameters significantly influence entropy generation, heat transfer rate, and skin friction characteristics. Overall, the study demonstrates that transport behavior can be effectively controlled by tuning governing parameters, which is essential for optimizing thermal and biomedical systems.
Cancer remains a leading cause of morbidity and mortality worldwide, accounting for millions of new cases and deaths annually. Despite advances in conventional chemotherapy, treatment outcomes are often limited by poor selectivity, systemic toxicity, drug resistance and suboptimal pharmacokinetics. Nucleic acid-based therapies are promising strategies for targeted cancer treatment; however, their clinical application is hindered by rapid degradation, poor cellular uptake and low transfection efficiency. Nanotechnology-based delivery systems have therefore been explored to overcome these barriers. Among these, nanoplexes are a versatile platform for gene delivery due to their ability to complex, protect and transport nucleic acids into target cells. This review focuses on the engineering of nanoplex platforms for enhanced transfection and gene expression in cancer therapy. We provide a comprehensive analysis of the current progress in nanoplex design and application and discuss advances in nanoplex-mediated gene delivery across cancer types, highlighting their transfection efficiency, gene expression, gene knockdown and safety profiles. Brief comparisons with other non-viral delivery systems are included to contextualize their relative advantages and limitations. Finally, the review outlines key challenges and future opportunities for the clinical translation of nanoplex-based gene therapies.
The incorporation of ceramic particles into magnesium metal matrix composites is garnering significant interest as a novel lightweight material due to its favorable strength-to-weight ratio, vibration-dampening properties, and biocompatibility. These attributes render them ideal for applications where weight is critical, such as components in the aerospace and healthcare sectors. The incorporation of reinforcing particles is a crucial step in altering the aging process of magnesium alloys. After heat treatment, the mechanisms of precipitation alter, the microstructural characteristics improve, and phase transitions occur more rapidly. This work analyzes the impact of various parameters, including reinforcing type, particle size, and volume %, on the aging characteristics of magnesium composites subjected to T4 and T6 treatments. A thorough examination of the essential mechanisms is conducted, including the increased dislocation density due to particle interactions, the enhanced heterogeneous nucleation at the interfaces between particles and the matrix, and the expedited diffusion of solutes. This study scientifically evaluates the modifications to the aging properties of Mg-MMCs designed to improve their mechanical performance, while providing valuable insights for the development of next-generation materials for aerospace, biomedical, and structural applications.
Abstract The global obesity epidemic underscores the urgent need for safer and more sustainable alternatives to conventional lipase inhibitors, which are limited by adverse side effects. In this study, chromium oxide nanoparticles (Cr 2 O 3 NPs) were synthesized via a microwave-assisted green route using durian ( Durio zibethinus or D. zibethinus ) husk extract (DHE), valorising agricultural waste as a natural reducing and stabilising agent. The nanoparticles were comprehensively characterised, confirming a rhombohedral eskolaite crystalline structure, quasi-spherical morphology with an average size of 65.93 ± 13.48 nm, and a bandgap energy of 3.32 eV. Functionally, the Cr 2 O 3 NPs exhibited strong antioxidant activity (91.94 % DPPH, 57.0 % ABTS) and significant pancreatic lipase inhibition (73.82 %). Kinetic analysis revealed an uncompetitive inhibition mechanism, representing the first report of durian husk-derived Cr 2 O 3 NPs with this enzymatic action. These findings highlight the dual antioxidant and enzymatic inhibitory potential of green-synthesised Cr 2 O 3 NPs and their promise as sustainable nanotherapeutics for obesity management within the framework of green nanomedicine and circular bioeconomy.
Abstract Significant research interest in hybrid nanofluids stems from their extensive applications in areas like industrial cooling and biomedical engineering. The present investigation focuses on a hybrid Casson nanofluid, a non-Newtonian fluid with superior thermal conductivity. The study models its flow as it is propelled by a linearly stretching elastic sheet within a Darcy-law porous medium. The study mixed copper (Cu) and aluminum oxide (Al 2 O 3 ) nanoparticles to leverage the high thermal conductivity of metals (copper) and the chemical stability of non-metals (aluminum oxide), improving heat transfer. Further, the impact of chemical interactions within the system and the presence of a magnetic field were also factored into this work. Not only this, but slip velocity phenomenon through the model was also taken into consideration. Dimensionless and similarity variables convert the model from its initial form into ODEs, and the shooting technique integrated with a fourth-order Runge–Kutta numerical scheme provides the computational solution. The findings reveal that stronger magnetic fields and higher porosity parameters significantly boost both heat transfer rate and skin friction, while larger slip parameters and Casson fluid characteristics lead to their reduction. Moreover, intensified chemical reactions decrease the nanoparticle concentration in the flow field. These outcomes, validated through comparison with prior studies, demonstrate a novel contribution by highlighting the dual role of magnetic fields and porosity in simultaneously enhancing heat transport and momentum transfer in hybrid Casson nanofluids.
