Armchair graphene nanoribbon field-effect transistors are promising candidates for nextgeneration nanoelectronics due to their tunable bandgap and high carrier mobility. In this work, a nearest-neighbor tight-binding Hamiltonian within a self-consistent non-equilibrium Green’s function (NEGF) framework is employed to investigate the impact of elastic phonon dephasing on the switching performance of dual-gate AGNR-FETs. Phonon dephasing is modeled using a scalar coupling parameter and analyzed under two limiting cases: momentum-conserving (MC) and momentum-relaxing (MR) scattering. The effects on key device metrics, including the on/off current ratio and subthreshold swing, are systematically evaluated. The results demonstrate that MC dephasing severely degrades switching behavior by increasing off-state leakage and worsening subthreshold characteristics, whereas MR dephasing preserves transistor-like operation with relatively minor performance degradation. Transmission analysis reveals that MC dephasing smears the transport gap, whereas MR dephasing preserves energy selectivity. These findings highlight the critical role of dephasing mechanisms in AGNR-based devices and emphasize the necessity of incorporating realistic phonon scattering models for reliable nanoscale transistor design.
The present work investigates the propagation of Rayleigh wave in micropolar thermoelastic biological medium subjected to initial stress. Revisiting micropolar and thermoelastic theory, we incorporate thermal nature to the micropolar theory to obtain basic equations for micropolar thermoelastic biological medium. Using the solutions of basic equations and appropriate boundary conditions, the secular equation of Rayleigh wave is derived. On solving the secular equation, we obtain the speeds of Rayleigh wave in micropolar thermoelastic biological medium. Numerically, the speeds of Rayleigh waves are investigated for particular cases of normal and cancerous human skin tissue. The effect of micropolar couple modulus, initial stress and linear thermal expansion coefficient are observed and depicted graphically for detection of skin malignancy.
This paper examines the effective electromechanical behavior of angular piezoelectric fiber-reinforced composites (PFRCs), adopting an analytical and numerical approach based on micromechanics. The goal is to examine the effect of the volume fraction of fibers ([Formula: see text] and the angular orientation of fibers [Formula: see text] on the effective mechanical, dielectric, and piezoelectric properties of such composites. This is accomplished through analytical derivations using the SM method with coordinate transformation considering inclined fibers within an inactive piezoelectric epoxy matrix. Such derivations are then verified through three-dimensional FEA of the RVE conducted through ANSYS 14.0. Results suggest that while both the stiffness [Formula: see text] and permittivity [Formula: see text] vary linearly with [Formula: see text], the piezoelectric charge constant [Formula: see text] has a nonlinear optimization point. In particular, the sensitivity is optimized to be 186[Formula: see text]pC/N for a [Formula: see text] around 0.8, which outperforms that of the bulk PZT-5A ceramics. This study shows that designing the fiber structure can lead to enhanced electromechanical interactions that provide a high degree of control authority for use in smart structures, MEMS, and energy harvesting systems.
This study investigates the magneto-thermo-bioconvective sheared hybrid nanofluid (SWCNTMWCNT/H 2 O) flow within a porous cavity containing oxytactic microorganisms. The enclosure is left-side heated and right-side cooled with adiabatic tops and bottom. In addition, the upper wall moves horizontally (+x-direction), and other walls are stationary. Finite-difference discretization applied to non-dimensional equations, and the resulting linear equations are efficiently solved through iterative techniques. The flow-controlling parameters are varied in the ranges as: Ra b = 0.1-10; Pe = 0.01-0.5; Ra = 10 5 -10 7 ; Re = 25-75; Sc = 0.1-1; Da = 10 -5 -10 -2 ; Ha = 0-20; β 1 = 0.1-1; and Ø hnf = 0-4%. The findings reveal that Nu avg has a rise of 544%, while Sh avg and Nn avg are reduces by 16.49% and 17.62%, respectively, with increased Ra. Moreover, heightening Ra b alters fluid circulation, with minimal impact on isotherms but reduces Nu avg and slightly increases Sh avg and Nn avg Also, as Re upsurges from 25 to 75, Nu avg surges by 73.36%, while Sh avg and Nn avg see modest gains of 1.52% and 1.57%, respectively. As ‘Ø hnf ’ increases, Nu avg exhibits an upward trend, whereas it diminishes with increased values of ‘Pe’ and ‘Sc’. In contrast, Sh avg experiences growth with increasing Ø hnf but declines as ‘Sc’ and ‘Pe’ rises.
The accurate determination of mineral optical properties faces a persistent trade-off between the high cost of crystallographic characterization and the limited accuracy of classical empirical relationships, such as the Gladstone-Dale law. These empirical methods fail to capture non-linear compositional effects and mechanical influences in complex natural systems. This study addresses these limitations by proposing a novel five-layer computational framework that integrates Explainable Artificial Intelligence (XAI) with Symbolic Regression (PySR). Leveraging a curated dataset of 3,112 mineral samples, we demonstrate that gradient-boosted decision trees, specifically LightGBM, achieve superior predictive accuracy (R 2 = 0.814, RMSE = 0.304) using only basic physicochemical attributes, effectively decoupling prediction from the necessity of Crystallographic Information Files (CIF). Critically, a direct numerical benchmark shows this result surpasses the Gladstone-Dale law applied to the same test set (R 2 = 0.743, RMSE = 0.361). Beyond prediction, SHAP analysis identifies Mohs hardness (mean ∣SHAP∣ = 0.192) as a determinant nearly as critical as specific gravity (mean ∣SHAP∣ = 0.207). Symbolic regression subsequently distills the model into interpretable constitutive equations, discovering a non-linear power law n ∝ H 0.263 and a unified interaction term n ∝ (ρ + H) 0.208 , which extends classical formulations to explicitly incorporate mechanical bond strength. These findings bridge the gap between high-accuracy data-driven modeling and fundamental physical understanding, establishing a robust numerical methodology for materials discovery in computational mineralogy.
This research effort presents an extensive investigation of unsteady magnetohydrodynamic (MHD) blood flow in an inclined, stenosed artery embedded with porous medium, non-Newtonian, incompressible, pulsatile and Herschel–Bulkley fluid (HBF) model. Human Blood is simulated as incompressible Herschel–Bulkley (HB) blood flow model to encompass shear thinning (viscosity reduction) or thickening (dilatant behavior) of blood and the effect of yield stress is observed in physiological conditions. The arterial wall is assumed to be the tapered and scheme incorporates Darcy and nonlinear Forchheimer drag forces to simulate realistic flow resistance within biological tissues. The governing equations of the present study consisting of coupled nonlinear partial differential equations (PDEs) are formulated using cylindrical coordinates and solved numerically by finite difference method (FDM). A parametric sensitivity analysis is performed to systematically evaluate the influence of essential dimensionless parameters on hemodynamic and thermal characteristics. The specific effects of the arterial inclination angle, Hartmann number ([Formula: see text]), consistency index ([Formula: see text]), power-law index ([Formula: see text]), yield stress ([Formula: see text]), the influence of the Forchheimer drag coefficient ([Formula: see text]) on the axial velocity distribution and wall shear stress are examined. The effects of the Eckert number ([Formula: see text]) and Prandtl number ([Formula: see text]) in influencing the temperature profile inside the stenosed arterial segment are rigorously examined. These results have considerable significance for improving clinical diagnostic and therapeutic approches for illnesses related to stenosed arteries.
We systematically investigated the electronic, optical, and strain-dependent properties of the WSi 2 N 4 /g-GaN van der Waals heterostructure (vdWH) using first-principles calculations. The heterostructure exhibits an indirect band gap of 1.89 eV with type-I band alignment, indicating its potential for high-performance optoelectronic applications. A net charge transfer of 0.103 |e| from WSi 2 N 4 to g-GaN induces charge redistribution, generating a strong built-in electric field that significantly enhances charge carrier transport. Furthermore, tensile strain can induce a transition from indirect to direct band behavior. Meanwhile, the inherent type-I alignment reversibly transformed to type-II under a small compressive strain of -1% or near-zero tensile strain. Notably, the sharp shift indicates high strain sensitivity, enabling precise control over the transition of band alignment type. The heterostructure demonstrates enhanced absorption in both visible and ultraviolet regions compared to its constituent monolayers. Under tensile strain, the optical absorption coefficient exhibits a systematic enhancement, accompanied by a significant redshift in the absorption peak position and a substantial broadening of the absorption spectrum. Conversely, compressive strain leads to band gap widening and a consequent reduction in optical absorption efficiency. These results collectively highlight the exceptional strain-tunable properties of the WSi 2 N 4 /g-GaN vdWH, making it particularly promising for applications in optoelectronic devices and photocatalytic systems.
This paper presents a numerical and data-driven study of the one-dimensional (1D) time-fractional Gray-Scott reaction-diffusion (TFGS) model, which is widely used to describe pattern formation in chemical and biological systems. A fully discrete scheme is developed by combining the Caputo fractional derivative (CFD) in time with cubic B-spline (CBS) collocation in space. The nonlinear reaction terms are linearized to obtain an efficient algebraic system at each time step, and a discrete stability analysis shows that the proposed method is stable in the associated energy norm. Numerical investigations demonstrate that the approach is accurate and reliable, with errors decreasing as fractional orders under temporal and spatial refinement. A machine learning-assisted surrogate model is presented to predict the reported numerical error behavior resulting from fractional order and discretization parameters, thereby supplementing the deterministic method. Furthermore, CBS feature mapping in machine learning is highlighted as a natural link between the numerical technique and the data-driven surrogate. The findings demonstrate that the presented method offers an effective tool for investigating the numerical performance of the TFGS model.
The importance of thermo-solutal behavior on the flow of Casson fluids is crucial in different biomedical and polymer processing applications, as control over heat transport phenomena is essential. Specifically, Casson fluids are widely used in modelling of blood flow and polymeric fluid behavior for the coating processes. The consideration of Soret combined with Dufour, along with variable magnetization and heat source, influences the flow phenomena. The flow past through a radial expanding surface embedded within a porous matrix enriches the flow properties. The mathematical model presenting the flow behavior with the inclusion of aforementioned properties forms a nonlinear model and then transformed into a dimensionless form with the utility of similarity rules. One of the novel approaches, such as physics-informed neural network (PINN), is employed to analyze the flow profiles. The flow phenomena of the Casson rheological model are reformulated into a residual loss function to be minimized utilizing neural network training. PINN integrates physical laws directly into the loss function without using a mess solution. The observation reveals that with an increasing Casson parameter, the fluid velocity is suppressed, which is crucial in modeling hyperthermia treatment.
This paper proposes a meshfree collocation approach based on the multiquadric radial basis function (MQRBF) for the analysis of flexural responses in skew laminated plates resting on elastic foundations under practical transverse loadings. Skew plates are extensively used in aero-nautical, marine, and civil applications where geometric asymmetry is directly linked with design efficiency. However, the presence of coupling between bending and twisting responses due to geometric asymmetry makes the analysis of these structures challenging. Laminated plates are modeled through an equivalent single-layer higher-order shear deformation theory (HSDT) with five independent displacement variables. The energy principle-based derivation of governing differential equations and their discretization by the stable MQRBF method is presented. A computational code in MATLAB is presented and validated through some comparative studies. Particular emphasis has been placed on the assessment of the influence that varying degrees of skewness introduce on stiffness distribution, load transfer, and deflection features that are considerably modified by the introduction of the skew angle. Extensive parametric analyses have been carried out to study the combined influence of the skew angle, span-to-thickness ratio, two-parameter elastic foundation, aspect ratio, orthotropy ratio, and number of layers on the bending behavior of skew laminated plates.
Enhancement of heat transfer (HT) rate through fins is one of the prevalent options, and the highly adaptable design of fins enables their application in many systems, such as radiators and electronic cooling devices. Pyramidal spine fins (PSF) are designed to enhance HT and provide better performance compared with other fin designs. Due to their effectiveness, these fins are implemented in heat sinks. Motivated by this practical application, the current study examines the thermal behavior of a wetted PSF, taking into account the effects of both convection and radiation mechanisms. The highly nonlinear energy equation of PSF is converted into dimensionless form using appropriate dimensionless terms. To get the solution for the resultant equation, the Schroder polynomial collocation method (SPCM) is applied. The impact of important parameters on the thermal profile, HT rate, and efficiency of PSF is illustrated graphically to address the thermal performance. The study reveals that increasing the value of the radiation-conduction parameter from 0 to 0.2 and the conduction-convection parameter from 0 to 0.5 enhances the HT rate by approximately 1.35% and 28.08%, respectively. Additionally, a larger rate of heat transmission of about 2.23% is seen in the wetted PSF compared with the dry PSF.
This project explores the potential of bio-inspired honeycomb structures for aerospace applications, focusing on the structural advantages of a trabeculae honeycomb design inspired by beetles. Initially, the research involved analyzing conventional honeycomb structures and their applications, followed by a detailed study of various bio-inspired designs. Through an extensive literature review, the trabeculae honeycomb structure was identified as a promising candidate due to its superior mechanical properties. The trabeculae honeycomb structure was modeled using CAD software and 3D-printed using PLA material. The structure is made up of a number of polygonal cells strengthened with trabeculae-like struts positioned at the vertices, similar to the natural architecture of Elytron Beetle Trabeculae. This one-of-a-kind arrangement increases compressive and bending strength, reduces buckling, and provides multi-directional stress distribution while keeping a lightweight profile. Finite Element Analysis and experimental tests, including compression, buckling, and bending, were conducted to evaluate the performance of the trabeculae design. The experimental validation was performed using a digital Universal Testing Machine to ensure precise measurement of mechanical behavior. The results demonstrated that the trabeculae honeycomb structure exhibits enhanced stiffness, reduced deformation, and improved energy absorption compared to conventional designs, making it highly suitable for weight-critical aerospace applications. This study highlights the potential of bio-inspired designs in advancing structural performance and material efficiency in the aerospace sector.
This research uses machine learning (ML) techniques - ANN, MLR, RF, and SVR - to predict GAP's pull-out capacity based on key parameters such as pile diameter, relative density, liquid limit, plastic limit, plasticity index, specific gravity, and L/d ratio. A dataset from previous experimental studies is used to train and test these models, with their performance assessed using statistical metrics like R-2, NSE, PBIAS, MSE, and RMSE. K-fold cross-validation was employed to evaluate the efficacy of the models. Visual comparison tools, such as regression plots, residual distribution, Taylor diagram and violin plots, were adopted for comprehensive model evaluation. Feature importance and the sensitivity of each input parameter on pull-out capacity were identified with RF and ANN, while SHAP plots help interpret the ANN predictions. The workflow uses python environment to implement training and validating the model. Artificial Neural Network (ANN) model with a ReLU activation function was identified as the best-performing method among all tested algorithms. Additionally, ML models are compared with two empirical equations for predicting pull-out capacity. Empirical equations can accurately predict the smaller pull-out value, but for larger values, these equations have not been able to converge. The SVR model performed worse than the empirical equations. However, ML models - except for SVR - exhibited high R2 values, indicating greater accuracy. Feature Importance and SHAP analysis results show pile diameter as the most influential factor, followed by L/d and soil consistency indices. Pile with optimum diameter with L/d ratio=8-10 and high density granular material (RD > 70%) with moderate plastic clay yields the most favorable pull-out resistance, providing a guide for design and optimization of GAP in field conditions.
Gelatin is a versatile natural biopolymer widely applied in food, pharmaceutical, and biomedical fields. Yet, its brittleness and moisture sensitivity restrict broader use in sustainable materials. While glycerol has long been the standard plasticizer, the search for eco-friendly alternatives remains pressing. In this study, gelatin interactions with natural plasticizers were examined using Density Functional Theory (B3LYP/6-31G**) and Molecular Dynamics (GROMACS, OPLS-AA, 100ns trajectories in explicit water). Plasticizers included polyols (sorbitol, mannitol, erythritol), organic acids (citric, succinic), amino acids (glycine, arginine), and amides (urea, thiourea). Hydrogen-bonding patterns, binding free energies, and electronic properties were systematically evaluated. All plasticizers formed stable H-bond networks with gelatin. Sorbitol and arginine generated the highest number of bonds (10-12 per cluster), while citric acid provided strong cross-linking. Normalized interaction energies reached - 350kcal/mol, confirming thermodynamic stabilization. Thiourea showed unique sulfur-mediated coordination, suggesting enhanced flexibility. MD simulations confirmed complex stability (RMSD <0.25nm, stable radii of gyration), with Delta Gbind ranging from - 25.6kcal/mol (urea) to - 67.3kcal/mol (glycerol). Agreement with literature data supports the predictive power of the approach. For the first time, combined DFT and MD modeling were applied to gelatin-plasticizer systems, offering molecular insights to guide the design of biodegradable, tunable gelatin-based films for food, packaging, and biomedical applications.
Natural fillers and fibers exhibit high variability in their mechanical properties, while traditional deterministic FEM is unable to capture this inherent uncertainty in composite materials. The uniqueness of the presented work lies in its integration of a stochastic thermally induced free vibration of an elastically supported multiphase composite plate (ESMPCP) reinforced with date seed powder and short glass fiber on elastic foundations. The stochastic natural frequencies characterized by mean and coefficient of variation (COV) have been computed numerically for the first time using a MATLAB code based on a finite element model, first-order perturbation technique (FOPT), and first-order shear deformation theory (FSDT). The effective elastic properties of ESMPCP were predicted using the Mori-Tanaka and Halpin-Tsai models. Various random parameters are investigated for their impact on the vibrational characteristics. Findings show that increasing the shear parameter (k2 = 0-20) leads to a 67.8% rise in the mean frequency of ESMPCP, reflecting the better resistance against vibration of the shear parameter relative to the Winkler parameter (k1) alone. Also, increasing the date seed filler content (10-40%) raises the mean frequency by about 40% and decreases the COV, making it suitable for vibration-sensitive applications.
The thermal efficiency of tetra-hybrid nanofluids exposed to a horizontal magnetic field over a stretched revolving disk has significant potential for enhanced cooling and energy-related applications. Such fluids, formed by dispersing multiple nanoparticles within a base fluid, offer enhanced heat transfer suitable for turbine blade cooling, compact electromagnetic heat exchangers, and biomedical thermal control systems. In this study, the three-dimensional flow of a tetra-hybrid nanofluid over a rotating stretchable disk is analyzed by accounting for the combined influence of a horizontal magnetic field, porous medium, thermal radiation, and internal heat sink/source under convective boundary conditions. The governing equations are transformed into ordinary differential equations (ODEs) and solved numerically using the Runge-Kutta-Fehlberg-Fourth-Fifth (RKF-45) method. To maximize the heat transfer rate, the Taguchi-based statistical method, combined with signal-to-noise ratio analysis and analysis of variance (ANOVA), is employed. The results reveal that increasing magnetic field strength and porosity significantly suppress both tangential and radial velocities due to enhanced resistive forces. Quantitatively, the optimization predicts a maximum Nusselt number of 10.4844123215026. The ANOVA results show that the rotational parameter dominates heat transfer enhancement with a contribution of 86.86%, while the Biot number has a minimal influence of only 0.30%. These findings provide useful design guidance for controlling thermal performance in magnetically regulated cooling systems and high-efficiency thermal management devices.
We systematically investigated the electronic, optical, and strain-dependent properties of the WSi2N4/g-GaN van der Waals heterostructure (vdWH) using first-principles calculations. The heterostructure exhibits an indirect bandgap of 1.89eV with type-I band alignment, indicating its potential for high-performance optoelectronic applications. A net charge transfer of 0.103 |e| from WSi2N4 to g-GaN induces charge redistribution, generating a strong built-in electric field that significantly enhances charge carrier transport. Furthermore, tensile strain can induce a transition from indirect to direct band behavior. Meanwhile, the inherent type-I alignment reversibly transformed to type-II under a small compressive strain of -1% or near-zero tensile strain. Notably, the sharp shift indicates high strain sensitivity, enabling precise control over the transition of band alignment type. The heterostructure demonstrates enhanced absorption in both visible and ultraviolet regions compared to its constituent monolayers. Under tensile strain, the optical absorption coefficient exhibits a systematic enhancement, accompanied by a significant redshift in the absorption peak position and a substantial broadening of the absorption spectrum. Conversely, compressive strain leads to bandgap widening and a consequent reduction in optical absorption efficiency. These results collectively highlight the exceptional strain-tunable properties of the WSi2N4/g-GaN vdWH, making it particularly promising for applications in optoelectronic devices and photocatalytic systems.
Computational methods to quantify structure-property relationships of materials are lacking in comparison to the widespread availability of nanostructure image-based characterization techniques. There are several existing quantification methods to characterize self-assembled nanomaterials, however many techniques are infeasible for use in processing large-scale spatial variation and/or require significant manual image pre-processing. The use of shapelet functions to computationally quantify nanostructured materials is a promising approach that is generalized for arbitrary pattern types but the current state of the art, the response distance method, requires user/researcher supervision. This need for supervision, along with the computational complexity of the method, make it infeasible for use in fully-automated large-scale computational analysis of nanostructured surface images. The development of a fully-automated unsupervised method for quantification of nanostructured surfaces would enable the development of broadly applicable structure-property relationships for nanomaterials, leveraging large amounts of previously captured data (so-called meta-analyses). In this work, these issues with the shapelet-based response distance method are resolved through the development and validation of a multi-step machine learning process which enables unsupervised (fully-automated) analysis of nanostructure imaging. The presented method removes the need for any researcher knowledge regarding order symmetries and enables truly large-scale analysis (e.g., meta-analyses) of the vast amount of nanostructure imaging data currently available. The presented unsupervised shapelet-based response distance method also results in at least an order-of-magnitude reduction in computational complexity compared to the existing method, at the cost of some degree of generality. A software implementation of the presented unsupervised method is provided in the open-source shapelets Python package.
Radiative cooling for buildings, which leverages long-wave infrared (LWIR) toward the sky, offers innovative solutions for passive thermal regulation. However, traditional passive radiative cooling systems, characterized by static thermal emissivity (epsilon), fail to automatically regulate LWIR thermal radiation across varying hot/cold seasons, exacerbating extra cooling/heating costs. In this study, we developed a photonic structure incorporating thermochromic vanadium dioxide and germanium. This structure can intelligently regulate epsilon based on ambient temperature, turning "on" and "off" radiative cooling in the atmospheric transparency window due to the metal-insulator transition of VO2. We crafted a meta-surface consisting of an alternating three-layered dielectric/metal of VO2/Ge composition using the principles of slow-light waveguide and Fabry-Perot resonators. This design achieves near-unity absorption of unpolarized light, offering a more efficient cooling effect with a simplified structure. The epsilon LWIR is adjusted at the critical phase transition temperature within the 8-13 mu m wavelength range. Notably, epsilon LWIR can reach up to 0.998 above the critical phase transition temperature and drop to as low as 0.1 below it. Besides, the net cooling power of a metallic VO2-based structure can attain 127.52W/m2, which is 3-4 times greater than that of an insulating VO2-based system. This work provides a promising prospect for temperature-adaptive radiative cooling via regulating epsilon LWIR with a photonic structure for all-season applications.
Shallow spherical cap (SSC) is a shell-type component extensively used in various fields, especially in ship and aerospace structures. In spite of practical importance, investigation on the nonlinear free vibration of functionally graded material (FGM) spherical caps with elastically restrained edges is evidently absent. As a first attempt, this paper deals with the nonlinear free vibration of FGM SSCs exposed to thermal environments, including the effects of porosity and tangential constraints of boundary edges. Pores are presented in FGM through even and uneven distributions. The effective properties of porous FGM are determined using a modified rule of mixture. Governing equations are derived using classical shell theory, taking into account von K & aacute;rm & aacute;n-Donnell nonlinearity, geometric imperfection, and interactive pressure from elastic foundations. Analytical solutions are assumed to satisfy clamped boundary conditions, and the Galerkin method is applied to obtain a nonlinear ordinary differential equation. This equation is numerically solved adopting fourth-order Runge-Kutta integration scheme to determine the frequencies of nonlinear free vibrations. Numerous influences on both natural and nonlinear frequencies of porous FGM SSCs are analyzed through parametric studies. It is found that the geometry ratios, imperfection, and tangential constraint of edges dramatically affect the frequencies and type of nonlinear free vibration behavior of porous FGM SSCs.
