Behavior Of Materials Research Articles (Page 1)

The uterine cervix is a soft biological tissue with critical biomechanical functions in pregnancy. It is a mechanical barrier that supports the growing fetus. As pregnancy progresses, the cervix becomes more compliant and eventually opens in late pregnancy to facilitate childbirth. This dual function is facilitated by extensive remodeling of the cervical extracellular matrix (ECM), giving rise to its complex time-dependent material properties. Premature cervical remodeling is known to result in preterm birth, defined as birth before 37 weeks of gestation. While previous work has studied cervical remodeling by various biomechanical methods, it remains unclear how the cervix's intrinsic or flow-independent viscoelastic behavior is influenced by cervical remodeling. In this study, an anisotropic reactive viscoelastic material model was formulated and investigated under tensile deformation to understand material behavior in cervical remodeling. To calibrate the model, experimental force relaxation data was used from uniaxial tension tests on Rhesus macaque cervical specimens from four gestational time points. Results show that cervical tissue equilibrium and instantaneous elastic moduli significantly decreased from the non-pregnant to late pregnancy. Also, cervical tissue in the late third trimester relaxed faster to equilibrium than the other gestational groups, particularly at prescribed tensile strains greater than 30\%. This fast relaxation to equilibrium helps the cervix dissipate tensile hoop stresses induced by the fetus during labor, preventing its rupture. This work provides insights into time-dependent cervical remodeling features, crucial for developing diagnostic methods and treatments for preterm birth.

Abstract A multi-agent artificial intelligence (AI) model is developed to automate the discovery of new metallic alloys, integrating multimodal data and external knowledge, including insights from physics via atomistic simulations. The system consists of (a) large language models (LLMs) for tasks such as reasoning and planning, (b) AI agents with distinct roles collaborating dynamically, and (c) a newly developed graph neural network (GNN) model for rapid retrieval of physical properties. We chose the ternary NbMoTa body-centered-cubic alloy as our model system and developed the GNN to predict two fundamental materials properties: the Peierls barrier and the solute/screw dislocation interaction energy. Our GNN model efficiently predicts these properties, reducing reliance on costly brute-force calculations and alleviating the computational demands on the multi-agent system. By combining the predictive capabilities of GNNs with the collaborative intelligence of LLM-driven reasoning agents, the system autonomously explores vast alloy design spaces, identifies trends in atomic-scale properties, and predicts macroscale mechanical strength, as demonstrated by several computational experiments. This synergistic approach accelerates the discovery of advanced alloys and holds promise for broader applications in other complex systems, marking a step forward in automated materials discovery and design. Impact statement Traditional deep learning models, such as graph neural networks and convolutional neural networks, operate within the confines of their training data sets, making single-step inferences for regression or classification. Our work introduces a multi-agent strategy that transcends these limitations by integrating deep learning with reasoning and decision-making capabilities. This intelligent system actively interprets results, determines subsequent actions, and iteratively refines predictions, accelerating the materials design process. We demonstrate its effectiveness in exploring the vast compositional space of a ternary alloy, where the model dynamically solicits data, analyzes trends, generates visualizations, and derives insights into materials behavior. By enabling accurate predictions of key alloy characteristics, our approach advances the discovery of novel metallic systems and underscores the critical role of solid-solution alloying. More broadly, it represents a major step toward integrating artificial intelligence with scientific reasoning, moving closer to artificial general intelligence in engineering. This paradigm shift has profound implications for materials science, enabling more efficient, autonomous, and intelligent exploration of complex materials spaces. Graphical Abstract

The interface properties of the material will significantly affect essential steps in the coagulation reaction, such as platelet activation, fibrin assembly, and erythrocyte adsorption. Diatoms biosilica (DBs) is an emerging inorganic porous hemostatic material that has achieved fast hemostasis because of its strong water absorption-concentration effect. Changing the interfacial properties of diatoms can expand the application scenarios to meet different hemostatic needs. Herein, three metal mineralized DBs including Ca mineralized DBs (Ca-DBs), Zn mineralized DBs (Zn-DBs) and Cu mineralized DBs (Cu-DBs) were developed through a hydrothermal method. It was found that the surface metal mineralization composition had a significant effect on the bioactivity of DBs, which was reflected in the ultrafast coagulation of Ca-DBs, and the different levels of anticoagulant and bacteriostatic properties of Zn-DBs and Cu-DBs. Metal-mineralized DBs retain most of their original microstructure and carry a decreased charge on the surface. In vitro results showed that metal mineralization significantly reduced the hemolysis rate of DBs, but Cu decreased the cytocompatibility. The three metal-mineralized materials affect endogenous and exogenous coagulation pathways as well as thrombin activity to different degrees. Among them, Zn-DBs promoted intrinsic coagulation at low concentrations and competitively inhibited thrombin activity at high concentrations. The animal studies confirmed the above result, with Ca-DBs having the most significant coagulation effect in the in vivo hemostatic assessment (coagulation time of only 37 s and blood loss of only 0.17 g). This study not only elucidated impact of surface metal composition on the blood coagulation activity of DB, but also provided a strategy to regulate the coagulation behavior of inorganic porous materials.

This study conducts a thorough analysis of the nonlinear fractional complex Heisenberg ferromagnetic-type Akbota (FCHFA) model to clarify its dynamic behavior. Through rigorous bifurcation analysis, we identify stability transitions and determine critical parameter thresholds, revealing the system’s sensitivity to perturbations. Employing advanced nonlinear dynamics techniques, we explore the fundamental mechanisms governing magnetization evolution. By integrating numerical simulations with analytical methods, we critically evaluate the role of fractional calculus in modeling long-range interactions and temporal memory effects in ferromagnetic systems. The FCHFA model, which incorporates nonlinear spin-wave phenomena and phase transitions, offers a robust framework for analyzing magnetization dynamics. Numerically validated solutions confirm the effectiveness of our methodology, providing new insights into phase transitions and nonlinear wave phenomena. Our findings highlight the crucial role of fractional derivatives in capturing complex magnetization behaviors, thereby enhancing theoretical understanding and broadening the applicability of fractional models in condensed matter physics. This work integrates fractional calculus with ferromagnetic theory, establishing a mathematically rigorous foundation for modeling systems with memory and nonlocal interactions. By rigorously validating numerical and analytical approaches, the study sets a precedent for investigating critical phenomena in fractional-order systems, with significant implications for device design in spintronics and magnetic materials. Furthermore, it demonstrates how fractional derivatives can effectively encapsulate the intricate dynamics of magnetization, including the interplay between memory effects and nonlinearity. By bridging theoretical developments with practical applications, this research not only advances the mathematical framework for studying complex magnetic materials but also opens avenues for innovative technological applications in the field of condensed matter physics.

Additive manufacturing of polymer materials, also known as 3D printing, is becoming a key technology for the production of functional parts with the ability to customize the structure and properties according to the application requirements. Polylactide (PLA) is one of the most commonly used materials in this field due to its biodegradability, ease of processing, and adequate strength for lightweight functional components. An important factor that affects the resulting properties of parts is not only the filler structure and density but also the angle at which the material is deposited during the printing process. This article focuses on investigating the influence of the printing angle (0°, 30°, 60° and 90°) and the bulk density of the filler (20%, 40%, 60% and 80%) on the mechanical properties of PLA samples. Two series of samples were prepared—the first was subjected to direct mechanical tests, and the second series was first exposed to freezing conditions and then tested to evaluate the effect of freezing on the material behavior. The samples were tested for tensile strength according to ASTM D638 and for bending strength according to ASTM D790. The results showed that the highest values were achieved in tensile strength in the 60°/80% configuration with a strength of 39.27 MPa, which represents more than a twofold improvement over the weakest configuration (0°/20%–19.58 MPa). In the bending test, the best results were achieved by the 90°/80% sample with a strength of 58.89 MPa, approximately 18% higher than 0°/20%. Cryogenic treatment caused a deterioration of all monitored parameters, especially at low infill densities and at an angle of 0°, where the decrease in strength reached up to 10–13%. These results confirm that the combination of a higher printing angle and a higher infill density is key to optimizing the mechanical properties of PLA parts, while cryogenic treatment has a negative impact on their behavior.

Microporo-mechanical approaches can be employed to simulate the behavior of porous media, such as lung parenchyma, with respect to their microscopic morphological and mechanical features. In this work, we propose a general micromechanical framework to describe the behavior of a porous hyperelastic material in large strains, including surface tension, and adapt its parameters to reproduce lung parenchyma behavior. We illustrate the method on a two-dimensional (2D) periodic microstructure. The modeling framework is adaptable to any microstructure and any combination of stress, strain, and pressure loadings. The identification of the model parameters in the context of lung parenchyma, based on existing experimental morphological and pressure-volume data, is performed sequentially. Twelve parameters related to morphology, alveolar wall constitutive behavior, and surface tension are calibrated to reproduce pressure-volume curves in various conditions, for a porosity in the unloaded state set to Φf0=63%. The calibrated alveolar diameter is Dalv=54 μm. The identifiability of the Neo-Hookean and Ogden-Ciarlet-Geymonat hyperelastic potential parameters is studied; their values are β1=88.6 Pa, β2=11.0 Pa, β3=628 Pa, and α=3.41. The hysteretic response of lung to pressure is reproduced thanks to the formulation of a surface-dependent surface tension. This work paves the way for a better understanding of the relationship between microscopic features and the macroscopic response of lung, in healthy and pathological conditions. Further experimental investigations could help confirm the ranges of parameters obtained in this study.

Inadequate characterization of the behavior of granular materials under accumulated plastic deformation in flexible pavements can lead to excessive rutting, severely affecting the structural and functional performance of highways. Permanent deformation (PD) is normally measured using repeated triaxial load tests, as required by Brazilian standards, which specify single-stage tests with 150,000 cycles in nine stress states per soil. Although effective, especially for tropical soils that exhibit rapid initial PD accumulation followed by rutting stabilization, the procedure requires substantial time, personnel, and laboratory resources. This study proposes reducing the number of cycles required for this characterization through prediction models. Seven soil samples were tested at nine pairs of stresses: five samples for model development and two for validation. From this, Artificial Neural Networks were trained using the data, removing the first 1,000 cycles to avoid the effects of rapid initial growth. The models generated PD predictions similar to the results obtained in tests of 150,000 load cycles using only 30,000 cycles for these predictions, with errors below 0.09 mm under severe traffic. The results confirm that the proposed approach can reduce the time required for PD characterization by approximately 50%, while maintaining reliable performance estimates.

This study investigates the influence of various flooring materials on indoor environmental quality (IEQ) through a comparative analysis based on in-situ measurements. The primary objective was to assess the concentration of total volatile organic compounds (TVOCs), perceived air acceptability, and odor intensity associated with selected flooring types, specifically wooden flooring, floating flooring, and carpet. The results demonstrated that the highest TVOC concentrations were consistently observed with carpet. However, the lowest air acceptability was recorded for wooden flooring. Notably, when wooden flooring was combined with other materials, improved sorption efficiency resulted in more favorable air acceptability outcomes than the standalone materials. Sensory evaluation of material combinations under in-situ conditions produced more favorable results than those obtained in controlled chamber tests. This suggests that real indoor environments may reduce or moderate the perceived effects of chemical emissions. Over a 36-hour monitoring period, TVOC concentrations from in-situ measurements exhibited a decreasing trend, whereas chamber test concentrations stabilized at higher levels after approximately 18 hours. After 36 hours, TVOC levels from in-situ measurements were lower than those from chamber tests for both standalone and combined flooring materials. Floating flooring demonstrated the lowest TVOC emissions and the most favorable air acceptability and odor intensity, suggesting that it provides an optimal balance between chemical emissions and sensory comfort. The findings underscore the need to complement chamber-based assessments within in-situ evaluations. Real indoor environments introduce variables — such as ventilation rates, humidity levels, and material interactions — that can significantly influence the emission behavior of materials and how these emissions are perceived. Without considering these contextual factors, indoor environmental quality assessments may overlook key aspects of real-world performance.

Asphalt airfield pavements undergo significant loading conditions because of the heavy loading and tire pressure conditions applied by various aircraft. Increasing aircraft wheel load and tire pressures along with environmental aging of asphalt can result in top-down surface fatigue cracking. The surface cracking can not only compromise the structural integrity of the pavements but also lead to the development of foreign object debris, which can affect the safety of aircraft operations. To better understand the behavior of materials used for airfield pavements, the Federal Aviation Administration (FAA) built the National Airport Pavement and Materials Research Center that houses a state-of-the-art Heavy Vehicle Simulator—Airfields (HVS-A) capable of loading test pavements under aircraft loading conditions. In this study, the FAA utilized the HVS-A to evaluate the fatigue cracking potential of a polymer-modified P-401 asphalt mixture produced with different warm mix asphalt technologies. Four different test sections were constructed, artificially and environmentally aged, and then loaded to induce fatigue cracking. Two of the four sections showed signs of fatigue cracking, and cracks became visible a couple of months after completion of the traffic tests. Extensive material characterization conducted on pre- and post-trafficked field cores showed a good correlation between different asphalt binder tests and the visual distress observations. The results of the study indicate existing asphalt binder and mixture characterization methods are sensitive enough to identify asphalt mixtures prone to top-down fatigue cracking distress and could be included in both purchase specifications and mixture design / acceptance specifications.

All-solid-state batteries frequently encounter mechanical instability due to the inherent brittleness and low elasticity of inorganic ceramic electrolytes, such as sulfides, oxides, and halides. These electrolytes struggle to accommodate the volumetric fluctuations of positive electrode materials during cycling, potentially leading to performance degradation and premature failure. To address this challenge, we propose a defect-based toughening approach for resilient halide solid electrolytes. By meticulously controlling the cooling rate during synthesis, we successfully increase the defect density within the electrolyte, enhancing its mechanical properties and mitigating the risk of mechanical failure. Mechanical property testing, high-resolution transmission electron microscopy characterization, and synchrotron radiation diffraction analysis reveal that the quenched material exhibit not only a higher Young's modulus, rendering it less susceptible to deformation under stress and a higher capacity for energy absorption before plastic deformation or fracture due to its increased dispersed defect density. Consequently, it demonstrates better adaptability to the volumetric changes associated with the positive electrode material during battery cycling, effectively mitigating strain-induced material behavior. Here we show the effectiveness of defect-enhanced toughening strategies in optimizing the mechanical properties and microstructure of electrolyte materials, thereby enhancing the overall integrity of solid-state batteries without requiring modifications to their chemical composition.

Purpose The additive manufacturing of concrete (AMoC) is a promising technology, but the parts are primarily designed without considering manufacturing constraints (e.g. minimum overhanging angle) and material behavior. This paper aims to propose an integrated workflow that includes a surface-based slicing method for AMoC while following the design for additive manufacturing of concrete (DfAMoC) guidelines. Design/methodology/approach The surface models of parts are generated based on DfAMoC guidelines. Three types of geometries are selected: planar surface, non-planar and geometries with features such as embossing or debossing. The surface models are optimized and further processed to generate the custom G-code required for AMoC. This integrated approach enables the seamless manufacturing of concrete parts via an extrusion-type gantry 3D printer, which was validated by considering different planner and non-planner designs. Findings A new DfAMoC has been proposed to provide designers a structured framework to fully exploit the AMoC potential while considering specific limitations. The proposed method demonstrated the potential to build concrete structures with better print quality (continuous print path, uniform material deposition, stable and modular printing of self-supporting features) compared to the results using commercial fused deposition modeling-based slicing methods. The varieties of printed structures considered in this study not only exhibited strong and consistent interlayer bond strength but also possessed higher dimensional accuracy, underscoring the reliability of the proposed method. Originality/value The study proposed a streamlined workflow for AMoC that encompasses the complete process, from creating a surface model of the part to slicing and G-code generation. The preference for the surface model over the existing solid model approach yields positive outcomes, demonstrating the manufactured part’s excellent print quality without any visible defects. The workflow is implemented using 3D printing experiments and is practicable in AMoC.

The mechanical properties of anodic oxide films of Nb, Ta and Zr were studied by the nanoindentation method. Anomalously high elastic recovery after deformation was observed for oxides with thickness of 20 nm. An analogue of this behavior can be elastic membrane fixed on soft base that does not prevent the membrane from bending. Increase of the oxide thickness to 300 nm reduced the effect associated with the high elasticity of oxide and easy deformation of the soft metal substrate, and was accompanied by an increase in the plastic component of deformation, which is similar to the behavior of ceramic materials with low elastic and significant residual plastic deformation.

Condensed matter physics continues to seek new frustrated quantum materials that not only deepen our understanding of fundamental physical phenomena but also hold promise for transformative technologies. In this review article, we highlight the unique features of chiral spin topology and review the topological phenomena recently identified in trillium lattice compounds. Based on the unique spin states realized in these systems, we explore the potential for realizing various theoretically proposed chiral quantum phases. We examine representative materials-including the magnetic insulating compound K 2 Ni 2 (SO 4 ) 3 and and the intermetallic EuPtSi-discussing both experimental findings and theoretical predictions, while outlining several key questions. Finally, we offer a perspective on promising research directions aimed at uncovering novel emergent behavior in chiral trillium lattice-based materials.

This study investigated the effect of tool shape on stirring performance and wear in Friction Stir Welding (FSW) of steel using particle-based numerical simulation. Two probe shapes were analyzed: a conventional cylindrical probe and a spherical probe, which may reduce stirring area and extend tool life while maintaining joint quality. The simulation, based on particle dynamics, evaluated material behavior, rotation speed, and welding speed effects. Stirring performance was measured by material dispersion; wear was assessed using adhesive wear theory. Results showed higher stirring intensity on the advancing side (AS) than the retreating side (RS), consistent with prior experiments. Probe tip shape significantly influenced stirring. Higher rotation speed improved stirring, while higher welding speed reduced it. Tool wear peaked during the plunge phase and was greater on the RS and tool edges due to lower flow stress and increased temperature. The spherical probe showed less wear than the cylindrical one under all conditions. Wear trends matched literature: rotation speed increased wear, welding speed decreased it. Overall, the spherical tool demonstrated lower wear, especially at the probe tip, indicating its promise as a durable alternative for FSW of steel.

The automotive industry increasingly demands lightweight, strong materials capable of large-scale production. Therefore, the objective of this work is to develop polyamide 6 (PA6) composites reinforced with graphene nanoplatelets (GNPs) that can be processed by injection molding. This study evaluates the effect of incorporation the GNPs reinforcement on the performance of PA6. The one-way ANOVA statistical analysis was used to evaluate the mechanical properties as a function of the composition of the composites obtained. A co-rotating twin-screw extruder was used to prepare the set of PA6/GNPs composites, followed by the injection molding process. Conventional characterization techniques were used to determine the properties of the composites, such as thermogravimetrical analysis (TGA), differential scanning calorimetry (DSC), Fourier-transform infrared spectroscopy (FTIR), Field Emission Gun-Scanning Electron Microscopy (FEG-SEM), X-ray diffraction (XRD), tensile, flexural and impact strength tests. The GNPs were characterized using Raman spectroscopy, the Brunauer-Emmett-Teller (BET) method, field emission gun-scanning electron microscopy (FEG-SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and thermogravimetrical analysis (TGA). The results suggest that the graphene used has a sparse number of stacked layers and defects, classifying it as a better-quality material. The composites showed the best tensile and flexural properties. The addition of 5.25 wt% GNPs to PA6 resulted in a 22% improvement in Young´s modulus and a 10% improvement in tensile strength compared to neat PA6. Regarding flexural properties, the incorporation of 7 wt% GNPs into PA6 generated a 34% increase in flexural modulus of elasticity and a 17% increase in flexural strength. On the other hand, GNPs reinforced PA6 composites exhibited brittle material behavior, identified by low notch impact strength compared to neat PA6.

To enhance the efficient utilization of industrial solid waste and support the low-carbon transition of cementitious materials, this study used steel slag, coal-fired slag, and desulfurization gypsum as the primary raw materials. A high-performance composite cementitious material system was developed based on the synergistic effects of physical activation (mechanical grinding) and chemical activation (alkali stimulation). This study systematically investigates the raw material characteristics, mix proportion optimization, mechanical behavior, and durability of composite cementitious materials through the integration of response surface optimization design and multi-scale analysis methods. The results indicate that the optimal mix proportions of the composite cementitious material are: 37.2% steel slag, 33.2% coal-fired slag, 9.6% desulfurized gypsum, 20% cement, 4% sodium silicate, and 0.1% superplasticizer. At this mix proportion, the measured 28-day average compressive strength of the composite cementitious material was 40.8 MPa, which closely matched the predicted value of 41.2 MPa from the response surface regression model, thereby confirming the model’s accuracy and applicability. The composite cementitious material demonstrated superior volume stability compared to ordinary cement under both water-curing and drying conditions. However, its freeze–thaw resistance and carbonation resistance were lower than those of cement. Therefore, considering these factors comprehensively, the composite cementitious material is recommended for application in road base and subbase layers.

Numerical simulation and parametric optimization of hot deformation behavior of copper-based composite material

Mamuju Regency, Indonesia, is among the world’s notable high natural background radiation areas (HBNRAs). This study examines the environmental and geochemical processes responsible for the accumulation of uranium (U), thorium (Th), and potassium (K) in surface soils. Through systematic field sampling, geochemical characterization, and radiological measurements, we found that the distribution of radionuclides is primarily governed by weathering intensity and lateritization. High U and Th concentrations occur in clay-rich, acidic soils, whereas K is enriched in less weathered profiles. High Purity Germanium (HPGe) gamma spectrometry confirmed elevated of ²²⁶Ra, ²³²Th, and ⁴⁰K activity, while survey meter measurements of ambient gamma dose rates exceeded global averages. Multivariate analyses (PCA and HCA) revealed strong correlations between radiological parameters and geochemical indicators, confirming that weathering and lateritization are the dominant factors. These findings advance the understanding of Naturally Occurring Radioactive Material (NORM) behavior in tropical soils and provide essential data for radiological risk assessment and environmental monitoring in HBNRAs.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-21431-6.

Capillary forces, arising from surface tension and wetting interactions, play a crucial role in nanoscale material behavior, particularly during solvent evaporation in nanowire-based systems. In this study, capillary-induced deformation in silver nanowires is analysed through a simplified two-step finite element model using ANSYS Static. The effects of these forces during the film formation and moisture treatment stages are investigated for silver nanowires with diameters of 25 nm, 40 nm, and 100 nm. Results show that smaller-diameter nanowires experience significantly higher capillary pressures, leading to greater plastic deformation despite their greater mechanical strength. In contrast, larger wires exhibit lower effective pressure due to reduced capillary efficiency, although the total capillary force is higher. The model contact angle after the film formation step allows for accurate estimation of capillary pressure during the moisture treatment step, enabling the calculation of final strain and stress distributions. Nanometre gaps between wires, which influence cold welding and contact resistance, are also quantitatively analysed.

Bake hardening is an efficient method for enhancing the strength and providing surface protection for automobile components. However, accurately predicting fatigue life after bake hardening remains a significant challenge. In this study, a finite element-based fatigue life prediction model is developed, integrating the Chaboche mixed hardening constitutive framework with bake hardening effects and cyclic elastoplastic damage theory. The model accounts for stress, strain, and damage evolution, enabling accurate predictions of fatigue life post-bake hardening. The results demonstrate that bake hardening significantly improves the material’s yield strength and delays the onset of plastic deformation, effectively extending fatigue life at low strain amplitudes. At higher strain amplitudes, despite the increase in yield strength, improvements in fatigue life are limited due to reduced plasticity. The model is validated using both experimental data and simulation results, showing high predictive accuracy both before and after bake hardening. This study bridges the gap between fundamental material behavior and practical engineering applications, offering a robust and reliable approach for predicting fatigue life after bake hardening. The proposed model holds significant potential for applications in automobile and other industries where fatigue resistance is critical.