Abstract
The objective of the present work is to investigate the size-dependent nonlinear thermo-elastic behaviour of sandwich nano-beams with porous core composed of random functionally graded materials (FGMs). In this, three types of FGM sandwich nano-beams are examined, the first one with metallic face sheets and porous FGM core with maximum ceramic content at the centre, the second one with ceramic face sheets and porous FGM core with maximum metallic content at the center and the third one with metallic bottom face sheet and ceramic top face sheet with porous FGM core. Porosity is modeled by assuming open-cell type, and two cosine-type porosity distributions are taken into consideration. Eringen's non-classical elasticity in association with Reddy's beam theory is employed to take care of size effects, and Von-karman type nonlinearity is used to incorporate the geometric nonlinearity. Obtaining and solving the governing equations are done according to the minimum potential energy principle and the finite element method, respectively. The first-order perturbation theory (FOPT) is used for stochastic analysis. Further, the proposed model is validated by comparing the numerical results with those obtained from literature and Monte Carlo simulation (MCS). In addition, the influence of amplitude ratio, length to thickness ratio, uncertain material properties, size parameter, porosity, thermal environment, and gradation of material on the stochastic behaviour of the nano-sandwich beams are demonstrated. The presented results show the importance of stochastic mechanical behaviour analysis for the reliable design of the components of nano-devices and systems.
Key Words
first order perturbation theory; functionally graded material; nano-beam; nonlocal elasticity; porosity; stochastic thermo-mechanical vibration
Address
Vikram Singh Chandel, Mohammad Talha: School of Engineering, Indian Institute of Technology Mandi, Mandi, Himachal Pradesh-175075, India
Abstract
We developed and studied a reusable antibacterial nanocoating composed of copper ferrite in polyurethane/silica hybrid matrix. The aim was to increase the level of hygiene in high-contact public spaces like sports halls. To achieve this, copper ferrite nanoparticles (CuFe2O4) were used as an antimicrobial agent in a polyurethane (PU) polymer matrix with silica (SiO2) nanoparticles. Silica increased the surface roughness and made the surface more hydrophobic. The formation of the uniform nanocomposites was confirmed by elemental analyses (FTIR, XRD, SEM, TGA, TEM and EDS). The results indicated that the size of particles was in the range of 15-50 nm and were homogeneously dispersed in the matrix. Water contact angle analysis demonstrated the increase in the hydrophobicity to around 140-150 degrees. Biological tests indicated a 90-99% decrease in the growth of E. coli and S. aureus bacteria. The thermal properties also showed that the coating was thermostable. In conclusion, this nanocoating is an efficient, environmentally friendly and reusable solution to lower the microbial load on highly touched surfaces.
Key Words
waste valorization; antimicrobial nanocoating; copper ferrite; hydrophobicity; magnetic nanocatalysis; nanocomposite; polyurethane; silica; sports halls
Address
Chuan Yang: School of Public Utility Management, Chongqing Vocational College of Transportation, Chongqing 402260, China
Chao Xie: School of Economics, Wuhan Donghu University, Wuhan 430212, Hubei, China
Mostafa Habibi: Technical Sciences, Chennai, India/ Department of Mechanical Engineering, Faculty of Engineering, Haliç University, Istanbul, Turkey/ Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, India
Abstract
Accurately predicting the stability and critical buckling loads of advanced steel-concrete structures remains a central challenge in structural engineering, particularly when assessing long-term durability under complex loading regimes. Traditional Structural Finite Element Analysis (SFEA), while robust, can be computationally prohibitive for parametrically exploring the design space of functionally graded composites, where material properties vary spatially to optimize performance. This study presents a novel hybrid framework that enhances conventional SFEA by integrating it with Artificial Neural Networks (ANN). This dataset trains an efficient ANN surrogate model capable of predicting critical loads with high accuracy. The primary application of this framework is the stability and durability assessment of innovative steel-concrete composite structures. By capturing the influence of geometric non-uniformity, material gradation profiles, and porosity, the model provides a powerful tool for engineers. It enables the rapid evaluation of how these factors collectively influence a structure's critical load capacity and, by extension, its long-term durability and failure resilience. This approach moves beyond conventional analysis by offering a method to systematically optimize functionally graded designs for enhanced stability, directly addressing the need for more reliable and durable steel-concrete systems in modern infrastructure. The fusion of numerical mechanics and machine learning outlined here provides a significant step forward in the efficient and accurate design of next-generation, performance-driven structures.
Address
Hongwei Zhong: School of Railway Engineering, Zhengzhou Railway Vocational & Technical College, Zhengzhou, 450052, China
Mostafa Habibi: Department of Mechanical Engineering, Faculty of Engineering, Halic University, Istanbul, Turkey/ Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, India
Tayebeh Mahmoudi: Hoonam Sanat Farnak, Engineering and Technology Knowledge-Based Enterprise
Abstract
Rotating shafts in micro and nano motors are tricky things. Classical beam theories work well at large scales. But they start to fail when the diameter drops below a few microns. The reasons are not mysterious. Nonlocal forces and strain gradients both come into play. They often pull in opposite directions. One softens the response. The other stiffens it. Adaptive manufacturing complicates things further with non‑uniform sections and graded properties. We develop a framework that handles all of this together. The equations come from Hamilton's principle with a higher‑order beam theory. Size effects enter through the nonlocal strain gradient elasticity theory. We solve the system with finite elements. Then we train a neural network on the FEM data to act as a fast surrogate. Our results show the small‑scale parameters shift the critical whirl speeds in a way that depends strongly on the taper ratio. For some combinations, the critical speed drops nearly fifteen percent relative to the uniform case. The ANN gives good accuracy on the validation set.
Abstract
This paper investigates the buckling behavior of carbon nanotube-reinforced composite (CNTRC) sandwich beams resting on Kerr elastic substrates using an accurate quasi-3D higher-order shear deformation theory. The beam consists of a homogeneous porous core bonded to two CNT-reinforced facesheets. Core porosity is modeled through uniform, symmetric, and asymmetric distributions, enabling the assessment of various porous sandwich configurations. CNT reinforcement in the facesheets follows uniform, AV-shaped, and VA-shaped patterns, providing a comprehensive representation of CNTRC sandwich structures. The foundation is described by a three-parameter Kerr elastic model. An integral field displacement formulation is introduced for the first time within a quasi-3D framework to improve the accuracy of buckling analysis for CNTRC sandwich beams on elastic foundations by accounting for transverse normal deformation. Governing equations are derived using Hamilton's principle in conjunction with the quasi-3D HSDT, and analytical solutions based on Navier series are employed to evaluate the critical buckling load. A detailed parametric study examines the effects of porosity distribution, CNT reinforcement pattern, CNT volume fraction in the facesheets relative to the porous core, and Kerr foundation parameters on the buckling response. Results demonstrate that porosity characteristics, CNT distribution, and foundation stiffness play dominant roles in governing the mechanical stability of porous CNTRC sandwich beams, confirming the effectiveness and robustness of the proposed quasi-3D formulation.
Key Words
CNTRC Sandwich beams; higher-order shear deformation theory buckling analysis; Kerr elastic foundation; porous core; porosity distribution
Address
Omar Yagoubi, Hassen Ait Atmane, Riadh Bennai: Laboratory of Structures, Geotechnics and Risks, Department of Civil Engineering, Hassiba Benbouali University of Chlef, Algeria/ Department of Civil Engineering, Faculty of Civil Engineering and Architecture, Hassiba Benbouali University of Chlef, Algeria
Mokhtar Nebab: Laboratory of Structures, Geotechnics and Risks, Department of Civil Engineering, Hassiba Benbouali University of Chlef, Algeria/ Department of Civil Engineering, Faculty of Technology, University of M'Hamed BOUGARA Boumerdes, Algeria
Abstract
In the current study, an analytical treatment of the phase velocity phenomena in volleyballs filled with graphene oxide nanocomposites is presented. The proposed mathematical model of the volleyball is treated as a multilayer nanocomposite spherical shell filled with graphene oxide powder (GOP). The effective properties of this nanomaterial are determined by means of the Halpin-Tsai approach. The higher order shear deformation theory (HSDT) is used to account for the transverse shear deformation effect without imposing shear correction factor assumption, which allows one to analyze high-frequency waves. The fundamental equations of the problem statement are obtained on the basis of the Hamiltonian variational principle. The acquired differential equations are solved by the differential quadrature method (DQM) utilizing Chebyshev-Gauss-Lobatto nodes. According to the proposed formulation, DQM will be utilized in the meridional direction whereas harmonic wave propagation will be considered in the circumferential direction in order to determine the dispersion relations and calculate the phase velocity properties. The theoretical approach introduced will provide an effective evaluation of the effects of graphene oxide reinforcement, geometrical parameters and wave numbers on phase velocity dispersion. Numerical analysis shows that the reinforcement of the structure with nanocomposites greatly affects the wave propagation and improves the dynamic response of spherical shell volleyball systems. The suggested theory will be very helpful for the development of high performance sports balls and other nanocomposite spherical shell structures.
Abstract
This paper introduces a hybrid nano-biosensor and data-driven intelligent system of real-time blood pressure monitoring and customized weight control. The suggested system combines both the cutting-edge nanomaterial-based sensing technology with machine learning algorithms to allow continuous cardiovascular monitoring without invasiveness and accuracy. The nano-biosensor uses high-sensitivity functional nanomaterials in order to record physiological signals related to systolic and diastolic changes in blood pressure, thereby assuring a fast response time, an increase in signal stability as well as precision in making the measurements. The physiological data obtained is sent to an intelligent platform running on a cloud, where a predictive model is implemented on the data provided in real-time by using supervised learning and adaptive algorithms. The model does not only approximate the blood pressure trend but also compares them with the lifestyle parameters, metabolic parameters, and weight changes. Using personalized health information, the system will produce unique prescriptions on weight management, such as exercise rates, nutrition changes, and behavior changes. The proposed hybrid approach allows combining both nanoscale sensing precision and artificial intelligence-based decision support, unlike traditional monitoring systems, which can be used to proactively manage health, as opposed to reactive diagnosis. Real-time performance, strong predictive accuracy, and adaptability control over various users are proved experimentally. Nano-biosensing technology combined with intelligent data analytics provides the scalable and cost-efficient solution of preventive healthcare, remote patient monitoring, and optimization of individual wellness.
Key Words
nano-biosensor; blood pressure monitoring; personalized weight management; data-driven intelligent system
Address
Zhang Hongcai, Nie Qian, Zhao Jue, Xie Wen, Zhang Delai: Department of Cardiology, Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu City, Sichuan Province, China
Yang Bingxia: Aviation Medical Office, Sichuan Airlines, Chengdu City, Sichuan Province, China
Abstract
Nanotechnology has come up as this kind of game changing approach for boosting the performance, sustainability, and even the visual appeal of today's architectural structures. In this work we look at how nanotechnology based art design innovation can help with structural strength, water durability, energy efficiency, and broader environmental sustainability, mainly by weaving in advanced nanomaterials. A finite element method (FEM) style methodology was used to figure out what happens when architectural materials are treated with carbon nanotubes (CNTs), nano-silica, graphene quantum dots (GQDs), nano-clay, titanium dioxide nanoparticles, and smart nanocoatings. From there, comparative checks were carried out, focusing on mechanical performance, durability traits, resistance to water, thermal behavior, and also whether the whole approach makes economic sense. The findings pretty clearly show that adding nanoparticles can markedly raise load transfer efficiency, improve crack resistance, and increase matrix densification, which then gives better structural behavior and a longer service life. On top of that, nanocoated glazing systems seemed to deliver better thermal insulation and less energy passage, so buildings end up needing less heating and cooling energy. For water durability, the tests showed big drops in moisture penetration and permeability. Meanwhile, even though the start-up or first installation costs can be higher, the economic view over time still suggests an attractive long run payoff. Across all the materials we looked at, carbon nanotubes and hybrid nanocomposite systems showed the strongest gains. The most notable results pointed to something like up to 54% improvement in structural strength, around 28% water absorption, about 88% better durability performance, and reduction in energy transmission through nano coated glass systems. So overall, the evidence really highlights how nanotechnology can support sustainable, tough, and energy smarter architectural design, even if the details of implementation may vary a bit in practice.
Key Words
art design; durability in water; nanotechnology; structures
Address
Su Hongen: School of Design, Zhoukou Normal University, Zhoukou City,Henan Province,China,466001/ Faculty of Fine Arts, Srinakharinwirot University, Bangkok, Thailand, 10110
Alireza Zamani Nouri: Department of Civil Engineering, ShQ.C., Islamic Azad University, Shahr-e Qods, Iran
Ahmad Horri: Department of Civil Engineering, University of Zabol, Zabol, Iran
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