The integration of acoustics, nanotechnology, and materials chemistry has led to new ways to produce multifunctional/intelligent systems over the past decade. In this research project, a new type of nanocomposite based on Titanium carbide (Ti3C2Tx) (MXene) and Polydimethylsiloxane (PDMS) was created and analyzed for acoustic purposes and as a smart musical instrument. MXene nanosheets were produced by a mild etch process (MILD) and mixed with a polymeric PDMS matrix via a solution-mix method. The results showed that adding MXene significantly increased the nanocomposite's mechanical, electrical, and acoustic performance. The Young's modulus values for PDMS alone and for PDMS with 5 wt% MXene were 1.2 MPa and 5.2 MPa, respectively. The electrical conductivity increased from approximately 10-12 S/m to 55 S/m. At a frequency of 1000 Hz, the sound absorption coefficient of the nanocomposite increased from 0.18 to 0.72, and the resonance frequency decreased from 820 Hz to 640 Hz, indicating increased internal damping. For a practical application, the nanocomposite membrane sample increased sound intensity levels from 62 dB to 79 dB and significantly broadened the device's frequency response. Clearly, the developed material functions as both a passive mechanical structure and a tunable active acoustic medium. Finally, this study provides a material-based approach for the design of smart musical instruments, in which the nanoscale structure of the material plays a decisive role in controlling and producing sound.
The combination of nano-enabled communication technology with machine-learning-enabled adaptive intelligence is a revolutionary model of intelligent and personalized learning system. The present study suggests a nano-inspired educational communication system that is powered by real time data collection, predictive engagement modeling, and content personalization to maximize the learning process. With simulation of 300 interactions of learners we test important performance measurements such as nano-interface signal quality, communication latency, system response efficiency, engagement prediction, learning personalization, assessment accuracy, and learning gain. The findings reveal that fidelity of signals and lower latency can enhance greatly system responsiveness, whereas machine-learning-based personalization and adaptive feedback can serve as an improvement to engagement and learning. In multidimensional analysis, it can be seen that the three factors of high engagement, high personalization, and high system responses produce maximum benefits on learning. These results confirm the effectiveness of the suggested framework and emphasize its possibilities to make a step forward to intelligent, real-time, and adaptive education platforms.
We will create a smart controller for educational management systems to optimize their design and stability. This will include inspiration from more advanced structures such as piezoelectric sensor-actuator plate systems. In particular, we will utilize a GNP (graphene nanoplatelet) reinforced core system to achieve high performance, resilient behaviors similar to those found in smart composite materials. To develop a mechanical model of the system, we will employ the Halpin-Tsai model to determine effective material properties and higher order shear deformation theory to properly capture transverse shear effects and improve the fidelity of our model. Next, we will derive the governing equations for the system using Hamilton's principle through virtual displacements based on the Green-Gauss theorem to ensure that we have mathematically solid foundations for our models. Additionally, we will implement a state dependent damping control strategy for increased system stability and response time under different operating conditions; allowing for continued real-time adjustments to system parameters similar to what is seen with piezoelectric sensor-actuator systems' dynamic feedback mechanisms. The Laplace transform is used to study the dynamics of a system and its stability with the time-domain solutions calculated numerically via the modified Dubner and Abate algorithm. When applied, our proposed model achieves increased stability margins, improved convergence rates, and better disturbance resistance than traditional control systems. Our results suggest that bio-inspired smart material concepts would dramatically enhance the adaptability and performance of educational management control systems. As such, this project represents an interdisciplinary effort to link the mechanics of nanocomposites with the design of intelligent control systems, providing a new approach to the development of resilient and adaptive frameworks for managing complex, data-rich educational markets.
Percutaneous kyphoplasty (PKP) is a popular procedure in treatment of vertebral compression fractures in the elderly, but existing bone cements are prone to poor mechanical integration, poor postural restoration, and cement leakage or poor load transfer. This paper presents a machine learning-optimized nanocomposite bone cement intended to increase biomechanical performance and achieve better postural reduction outcomes in the elderly PKP surgery. The proposed system should enhance compressive strength, modulation of elasticity, and restoration of vertebral height by incorporating nanoscale reinforcement agents into polymethyl methacrylate (PMMA)-based cement and modulating the composition parameters with the help of data-driven learning algorithms. The model takes advantage of predictive learning to determine the best nanofiller concentration, dispersion properties of the particles, and curing dynamics. The findings demonstrate that the optimized nanocomposite formulation has the potential of significantly improving structural stability and minimizing the progression of kyphotic deformity in the case of conventional cement systems. The study shows how nanomaterials engineering with machine learning can be used to improve the outcomes of minimally invasive spinal surgery.
This research investigates how nanocomposites can help improve the dynamic stability of button designs within the Mazu clothing industry using a functionally graded graphite nanoplatelet-reinforced (FG-GPLR) material. An eccentric annular plate model is used to study the dynamic response of buttons. Buttons are critical components of the overall physical strength and functional integrity of Mazu clothing, and a reinforced approach using graphene nanocomposites will add to the strength of the button via improved mechanical properties (tensile and impact resistance), providing long-lasting functionality under dynamic load conditions. A modified form of the Halpin-Tsai micromechanics model has been employed to predict the behavior of the material, taking into account the non-linear distribution of graphene platelets within the composite matrix, and the refined shear deformation theory (RSDT) has been utilized to analyze the deformation of the plate subject to dynamic loading. Subsequently, Hamilton's principle was used to derive the equations of motion for the governing equations and to create a numerical solution for the design of the buttons; the Gauss-Lobatto-Chebyshev grid generation method has been used to define the numerical region for this study, and the transformed differential quadrature method (TDQM) has been used to yield accurate numerical solutions to aid the design of dynamic stability for the buttons. The findings indicate that there has been a considerable increase in the strength and stability of buttons, thereby supporting the engineering potential for nanocomposite materials to aid the Mazu design of clothing with both functionality and visual aesthetic appeal.
The research evaluates the possibility of using DNA-like helix structures as a basis for developing innovative approaches to elderly health technologies. This work applies a various multiscale modeling strategy that integrates Carrera's unified formulation (CUF)-based finite element method (FEM) and molecular dynamics (MD) simulations and characterizes both continuum and atomic/nano-scale vibrational responses of biologically inspired helices. In order to address issues related to natural frequency and stability of bio-inspired helices under physiologically-like loading conditions, the research focuses on the impact of variation in the helical radius, pitch of turns, as well as, size-dependent elastic properties on structural dynamics and mechanical stability of the helices. The analysis found that variations in nanoscale interactions & nonlocal elastic behaviour had an impact on both the stiffness and dynamic response of the helices; thereby providing insight into the mechanical robustness of these structures and confirming the reliability of the proposed methodology due to the similarity of the CUF-FEM predictions, MD simulations, & experimentally determined results. This work establishes a foundational understanding of how to engineer DNA-like structures with enhanced mechanical properties while providing a means for integrating them into next-generation healthcare monitoring platforms, biomechanical sensors, & nanoscale drug delivery systems designed for use by an aging population. Linking structural mechanics to biomedical applications proves that future innovations can be achieved through the development of new types of functional units, e.g., DNA-based helices or structures, that may address healthcare problems associated with the aging population.
Advancements in nanotechnology have created opportunities to improve both athletic performance and athlete health through the construction of smart materials using enhanced structural design. The present research describes a new method of assisting in the development of athlete health by combining nanocomposite reinforced shoe soles to improve dynamic stability as well as provide transient load resistance to the sole structure. The structure of the shoe sole is modelled as a doubly-curved panel with two radius of curvature parameters, related to a tunnel shape, to replicate the complex geometry of footwear and the interaction with the ground. Graphene oxide powder (GOP) is used as a nanoscaled reinforcement; the homogenized mechanical properties are obtained using the extended Halpin-Tsai method. The equations of motion are derived using first order shear deformation theory (FSDT), while Hamilton's principle produces five coupled partial differential equations representing the vibratory response of the panel. In order to meet simply supported boundary conditions, the displacement fields are expanded using double Fourier trigonometric series according to Navier's solution method. This system provides the dynamic response of the GOP shaped, reinforced soles of shoes when subjected to an active (e.g., impact) force, such as with ground reaction forces during sports activity. The next step in this process will be to apply a Laplace Transform method to solve the equations for the temporal evolution of the materials' displacement and stress. We will use the modified Dubner and Abate Formulation to take the inverse Laplace Transform for the temporal evolution of the materials' displacements and stress. This multi-scale, nano-enabled framework provides a methodology and quantitative measures for optimizing shoe sole design to reduce excessive movement of the foot, decrease the risk of ankle injury, and improve stability in athletes. The data support the potential of nanocomposite designs coupled with advanced continuum mechanics to link advances in nano research to future commercial wearable technology platforms for the improvement of sports health.
The objective of this research is to examine the use of multi-phase nanocomposites as roof covering on a railway station, assessing their performance with respect to energy absorption and dynamic characteristics as roof covering for railway stations. In particular, the focus will be on developing a trilevel configuration of silicon-based solar roof covering materials for railway stations consisting of three layers; a carbon fibre composite, a polymer component and a low carbon (LC) nanotube reinforcement layer. This study will investigate the intended benefits of using advanced materials in developing the roof systems on railway stations in terms of their mechanical and energy harvesting capabilities when subjected to real-life service conditions. The structural response of the railway station roof is predicted from Von K & aacute;rm & aacute;n nonlinear geometric relations for large deformation using dynamic loads typically experienced by railway stations from wind and other environmental effects. In this work, a detailed simulation is created to investigate the performance of the containable nano-composite material layered nano-composite material (CM) in rooftop construction in response to the effects of wind induced vibrations and force impacts. The role of LC in enhancing the rigidity, damping, and energy absorption is especially emphasized. Parametric studies are conducted to determine if adding LC to the layer would produce an increase in structural durability and longevity when used in the construction of roof systems within train stations and would decrease the amount of long-term maintenance required because of the increase in structural integrity. This research concluded that LC-reinforced nano-composite solar-roofs (nanocomposite) provided the potential for environmentally friendly and sustainable solutions for rooftops located within future railway stations. In addition, these systems improved performance, resiliency, and efficiency of railway stations functioning in dynamic environmental conditions.
This research examines the use of novel nanomaterials to improve the strength and quality of geopolymer concrete. Nano silica is the most commonly used nanoparticle and it increases concrete strength. Nano-titanium dioxide (TiO2) improves the mechanical properties and it is used to reduce air pollution and self-cleansing the atmosphere. Nano silica and nano-titanium dioxide are replaced by 1% of the total weight of fly ash and paver blocks are cast for M40 grade concrete with different fly ash replacement and nano silica combinations (0.25% TiO2 + 0.75 % SiO2), (0.50% TiO2 + 0.50% SiO2), (0.75% TiO2 + 0.25% SiO2). These paver blocks increase the compressive strength with the increase in age and reduces the various environmental issues. Microstructural analysis shows the interaction between nanoparticles and geopolymer concrete reveals a higher filling effect. The air quality of the paver block has been monitored using an IoT device designed for this application. It is found that the air pollutants are reduced up to 30 percent at the center of the paver block. The combination of 0.75% nano-silica and 0.25% nanotitanium dioxide was optimal. It can be used to achieve higher compressive strength and it purifies the atmospheric air, reducing harmful air pollutants and enhancing environmental protection.
This work presents a hybrid polyurethane nanocomposite coating reinforced with functionalized nano-SiO2 and graphene oxide for high-performance furniture applications. Surface modification via APTES silane coupling promoted uniform nanoparticle dispersion and robust interfacial bonding within the polymer matrix. Multiscale characterization using SEM, XRD, FTIR, and Raman spectroscopy confirmed successful hybrid nanostructure formation and strong matrix-filler interactions. The nanocomposite exhibited synergistic reinforcement: pencil hardness increased from HB to 3H, flexural modulus improved by 22%, and pull-off adhesion strength rose by 18%. Taber abrasion testing revealed approximately 40% enhanced wear resistance, while thermogravimetric analysis showed elevated thermal stability with the onset of degradation shifting from 284 to 308 degrees C. These improvements stem from nanoscale mechanisms, including efficient stress transfer, crack deflection by GO nanosheets, and barrier effects from the hybrid architecture. Despite a modest 6 to 8% material cost increase, lifecycle assessment supports economic viability through extended service life. This study offers a scalable nanomanufacturing strategy for advanced polymer nanocomposites.
The screening of malignant pulmonary nodules is important in order to enhance the survival rates of the patients having lung cancer. Traditional methods of diagnosis like computed tomography (CT) scans and biopsy procedures are usually challenged in terms of sensitivity, specificity and late diagnosis. This paper suggests an interdisciplinary approach that would involve both nanosensor arrays and deep learning into the prevention and classification of malignant pulmonary nodules at an early stage. The nanosensor arrays will have the ability to detect volatile organic compounds (VOCs) and molecular biomarkers of lung cancer in breath samples. These sensor reactions yield the high-dimensional signal patterns which are analyzed by a deep learning model to identify the salient nodules as benign or malignant. A training and evaluation model based on a convolutional neural network (CNN) was trained and tested on a dataset of sensor responses obtained on clinical breath samples. Preprocessing of data and feature normalization was done in order to improve signal quality and minimize noise. Cross-validation method was used to test the proposed system to guarantee the presence of robustness and reliability. The experimental findings show that the combined nanosensor deep learning system had an overall detection accuracy of 94.3 where the sensitivity was 92.1 and the specificity was 95.6 in classifying between malignant pulmonary nodules and benign conditions. The results demonstrate that the integration of nanosensors arrays and state-of-the-art deep learning algorithms can greatly increase the early detection of lung cancer. A non-invasive, fast, and cost-effective method can potentially assist in clinical decision-making and screening programs, which eventually would allow making administration of patients earlier and achieving better results. More extensive clinical studies are advised to support and streamline the suggested system to be used in medical practice.
The comprehensive stability analysis of projectile discs reinforced with orthopaedic nanomaterials has been performed in this research using advanced multiphysical coupling effects. The structural model was developed as an eccentrically mounted annular plate, with piezo-magnetic patches incorporated into the design, allowing for the analysis of magnetic-electric field coupled interactions. The effective material properties of the orthopaedic nanocomposite were calculated using a modified Halpin-Tsai technique using the actual nanoscale reinforcement and anisotropic properties found in orthopaedic composites. The governing equations were formulated based on the theory of first order shear deformation, which provided an accurate representation of shear deformation in moderately thick discs. Compatibility conditions were imposed between the composite and the piezo-magnetic interfaces for continuity. The formulation contains both the strength components of the magnetic-electric fields and their corresponding potentials, enabling a full magneto-electro-elastic analysis. Hamilton's principle enables the development of a variational approach to modelling the system, producing a set of related differential equations, which govern the dynamic stability characteristics of the system. The differential equations are discretised in an efficient manner and solved accurately using the transformed differential quadrature method (TDQM), yielding very high numerical accuracy at a low overall computational cost. Parametric studies are performed to examine the effect of nanomaterial reinforcement, geometric eccentricity, and external magnetic/electric field parameters on the stability characteristics of the discs of projectiles. The results clearly show that the structural stability and tunability of orthopaedic nanomaterials are greatly enhanced by applying multi-physical loads. The framework proposed opens up new opportunities to develop and optimize advanced smart composite structures in aerospace, biomedical, and defense applications.
The development of mathematical models that describe physical phenomena is an essential aspect of engineering, enabling the prediction of structural behavior through equations derived from physical laws. Among various computational techniques, the finite element method (FEM) remains the most prevalent in analyzing complex structures and components. This article presents a three-dimensional finite element analysis (3D-FEM) with the aim of investigating the fretting fatigue behavior of second-generation titanium alloys (Ti-45Nb). The research focuses on the analysis of contact parameters, the influence of crack size, sliding amplitude, and the occurrence of stick, slip, and stick-slip zones on crack nucleation and propagation under multiaxial stress conditions. The multiaxial stress state at the contact interface plays a dominant role in determining the location and initiation of the crack. For fatigue life estimation and identification of the crack initiation zone, the Crossland, Findley, and Smith-Watson-Topper (SWT) multiaxial fatigue criteria were applied. In addition, advanced fracture mechanics parameters including the J-integral, stress intensity factors (Mode I, II, and III: K-i, K-i, K-i(i)), and T-stress were evaluated using the Extended Finite Element Method (XFEM), providing a detailed characterization of the crack driving forces under complex loading conditions. The results provide quantitative proof of the damage mechanisms and critical parameters influencing the durability of Ti-45Nb alloys under fretting fatigue. The originality of this study lies in combining 3D contact analysis, multiaxial fatigue criteria, and XFEM-based fracture assessment for Ti-45Nb alloy under fretting fatigue within a single framework. Accordingly, the main purpose of this study is to identify the critical crack initiation region and to characterize crack propagation behavior under multiaxial contact loading.
While prior research has explored the dynamic behavior of conical shells under moving loads, the dynamic response of graphene platelet-reinforced metal foam (GPLRMF) conical shells with spinning motion remains uninvestigated. This study establishes a dynamic model for such GPLRMF conical shells under moving loads to analyze their response characteristics. The first-order shear deformation theory (FSDT) is integrated with Hamilton's principle to formulate the governing equations. The motion equations are discretized using the Galerkin method under simply supported boundary conditions, resulting in a system of ordinary differential equations. The mechanical model's validity is confirmed through two comparative examples. Further, convergence analysis is performed for conical shells with varying semi-vertex angles to validate the method's accuracy. Finally, parametric analysis of the dynamic response is conducted using the Runge-Kutta method, with results including the time history of midpoint deflection and the velocity history of maximum midpoint deflection. It can be found that, higher rotational speeds significantly reduce deflection due to centrifugal forces counteracting deformation, the GPL-A/Foam-I shell exhibits minimal central deflection, benefiting from higher GPL concentration and lower porosity, boosting stiffness, and the forced vibration increases deflection with larger semi-vertex angles, as smaller angles (closer to cylindrical geometry) enhance load-bearing capacity.
The research being undertaken focuses on studying the effects of using carbon nanotubes (CNTs) which are nano-enhanced materials, applied to sport stadium roof systems, and how they perform aerodynamically and affect the dynamic stability characteristics of those structures. A structural model was created using the parabolic shear deformation theory (PSDT). A refinement to the transverse shear strain function has been made that accurately represents the effect of shear deformation without using shear correction factors. The coupled governing equations of motion that define fluid-structure interaction (FSI) under high-velocity flow conditions were derived using Hamilton's principle. The Krumhaar modification to the supersonic piston method was used to provide a robust yet analytically traceable means by which to determine unsteady aerodynamic pressure on roof geometry subjected to high wind loads or transient aerodynamic disturbances. The resulting P.D.E.s will be discretized using the differential quadrature method (DQM), which employs Lagrange interpolating polynomials expressed in terms of Chebyshev polynomial root interpolation to enhance numeric stability and convergence of DQM. This discretization method will allow for accurate evaluation of spatial derivatives using a smaller computational grid, allowing for effective parametric studies on complex roof shapes. The effects of the volume fraction of CNT reinforcement, distribution patterns and orientation are explored in order to quantify improvements in terms of stiffness, aeroelastic resistance, and flutter limits. The study shows that CNT reinforced composite roofs have much higher aerodynamic damping values and higher critical dynamic instability limits than traditional laminates. Moreover, PSDT based modeling indicates a significant sensitivity of transverse shear on the flutter onset, particularly for large span curved roofs that are common for modern stadiums. The overall methodology presented in this study integrates state-of-the-art nanoscale material modeling with high-fidelity aeroelastic analysis and provides a comprehensive method for optimising lightweight, durable and aerodynamically stable stadium roof systems for the next generation of sports facilities.
This study presents a comprehensive analytical framework for optimizing nanocomposite-reinforced coal mining components designed for advanced rock mechanics applications. Emphasis is placed on the mechanical performance of structural panels enhanced with graphene oxide powder-based nanocomposite reinforcement (GOPCR). A doubly curved GOPCR panel subjected to distributed airflow pressure representative of harsh underground mine ventilation environments is modeled to capture the coupled effects of curvature, pressure loading, and nanoscale reinforcement on dynamic behavior. Hamilton's principle is employed to derive the governing equations of motion, incorporating an improved shear deformation theory with an appropriate shear-correction factor to accurately represent transverse shear effects associated with moderately thick, nanocomposite-enhanced structures. The resulting partial differential equations are solved analytically using a double trigonometric series expansion consistent with Navier's solution technique, enabling explicit closed-form expressions for modal characteristics. Parametric studies investigate the influence of GOP volume fraction, curvature ratio, and airflow pressure on frequency response. Results indicate that GOP reinforcement significantly enhances stiffness, yielding noticeable increases in natural frequencies compared to conventional polymer-reinforced panels. A focused comparison is conducted between the natural frequencies of the GOPCR doubly curved panel and those of a shallow spherical shell of analogous geometric and material configuration. The findings reveal that nanocomposite modification produces more pronounced frequency elevations in the shallow shell due to its higher inherent geometric rigidity. Overall, this research demonstrates the strong potential of GOPCR materials for improving the durability, stability, and vibration resistance of coal mining structural components, offering valuable insights for the design of next-generation rock mechanics support systems.
Over the past years, nanotechnology has become a promising approach to delivering drugs more effectively, whereas machine learning methods offer great solutions of optimizing the design of the treatment, depending on the unique aspects of a patient. Uterine fibroids belong to the category of the most frequent benign tumor in women of childbearing age and may cause serious clinical issues as pain in the area of the pelvis, irregular bleeding, and infertility. Traditional methods of treatment are not very personalized and can cause either poor therapeutic outcomes or unwanted side effects. This paper has shown a machine-learning-based design and assessment of nanotherapeutics to achieve the customization of the treatment of uterine fibroids. A sample population comprising of 420 cases was created that contained clinical and therapeutic important variables such as the age of patients, body mass index (BMI), fibroid size, severity of symptoms, type of nanoparticles, dose of drug, and ligand of targeting. These were inputs to be used in predicting treatment efficacy which is the expected level of effectiveness of the nanotherapeutic intervention. To assess the associations between patient properties, nanocarrier properties, and the therapeutic outcomes expected, both statistical analysis and graphic visualisation were used to investigate the relationships. The findings show that patient related variables as well as the parameters used in the design of nanotherapeutics play a major role in determining the performance of the treatment. Specifically, predicted treatment efficacy is enhanced by the optimization of the dosage of drugs as well as by the choice of targeting ligands. The results indicate the possibility of combining machine learning and nanomedicine to allow individualized treatment plans to increase precision of treatment and better clinical results in patients with uterine fibroids.
The research examines how extreme weather conditions affect the stability and aerodynamic capabilities of stadium roofs that use nano materials. The modern architectural design for large stadium roofs uses a doubly curved panel system which achieves better structural performance and flexible design options. The roof structure will achieve better mechanical performance through the use of graphene nanoplatelet (GPL) reinforced composites which create panel materials that will provide higher stiffness and lower weight and better environmental performance. The analysis uses rain-induced loading as an extreme weather condition because it considers the extra weight and damping effects and fluid-structure interaction which occurs during precipitation. The governing equations of motion for the doubly curved panels are formulated based on classical shell theory which is modified through the addition of nanoparticle reinforcement effects. An effective medium method enables material property evaluation by demonstrating how graphene nanoplatelets affect material performance. The first-order piston theory provides an effective method for measuring unsteady aerodynamic pressures which affect curved surfaces during high wind conditions. Through the evaluation of structural/aerodynamic systems one obtains important information about system data that indicates natural frequencies and damping ratios, as well as the dynamic performance of a structure. The results of the research indicate that the addition of graphene nanoplatelets results in an increase in the rigidity/stability of the roof system and decreases the amount of rain-induced loading that the roof system will experience due to vibration. Additionally, the aerodynamic performance of the roof system is enhanced/helped by its ability to accommodate this high-velocity airflow. Furthermore, the findings demonstrated that nano-reinforced materials permit architects to develop new roof structures for future use by stadiums.
This study investigates the influence of graphene origami (GOri) reinforcement and multi-field piezoelectric/piezomagnetic loading on the natural frequency characteristics of a sandwich curved beam. The structure consists of a GOri-reinforced composite core integrated with piezoelectric and piezomagnetic layers. A higher-order shear deformable model incorporating thickness stretch effects is developed to establish the kinematic relationships. The constitutive equations are derived by combining: Micromechanical models for the effective material properties of the GOri-reinforced core, Multi-field coupling equations for the piezoelectric/piezomagnetic layers. The governing equations of motion are obtained using Hamilton's principle and solved analytically. The natural frequency responses are systematically analyzed with respect to: GOri foldability parameters and reinforcement content, Multi-field (electro-magneto-elastic) loading conditions, Geometric parameters of the curved beam, Elastic foundation characteristics. The model is validated through comparison with existing literature before presenting new results. This work provides fundamental insights into the dynamic behavior of smart sandwich structures with tunable graphene-based reinforcements.
Lumbar fusion surgery and its public health are intervention with significant variability in the outcomes and overall risks and benefits for the patients, therefore requiring state-of-the-art risk assessment models. In this paper, an Explainable Artificial Intelligence (XAI) approach is developed for the application in spinal fusion surgeries and public health with the Machin learning (ML) methods combined with nanotechnology. Consequently, the proposed framework captures patient's demographic, public health index, clinical, and biomechanical characteristics for prediction of surgical outcomes, pos-surgical complications, and recovery curves. Feature importance analysis and SHapley Additive exPlanations (SHAP) are used for decision justification to let clinicians understand which factors contribute to the high-risk score. The proposed XAI model is tested on clinical datasets from previous years and year ended to be more accurate compared to traditional statistical models yet enough compact for interpretation. The results reveal the application of explainable AI combined with nanotechnology in the enhancement of treatment plans and public health for various patients, the minimization of possible surgery complications, as well as, the introduction of improved chances of success. Results show that older patients with osteoporosis face higher physiological strain because it results in delayed postoperative healing along with greater surgical complications because of their decreased hemoglobin levels and insufficient bone repair. The duration of surgeries increases when patients have low hematocrit levels and lower vitamin D concentrations which calls for unique preoperative optimization methods to enhance surgical results.
