Behavior Of Polymeric Materials Research Articles

Mechanical Behavior of Polymeric Materials: Recent Studies

This Special Issue is devoted to one of the most exciting fields in polymer science and technology: the many factors that influence the properties of polymer-based materials [...]

Nonlinear mathematical modeling of frequency-temperature dependent viscoelastic materials for tire applications

Understanding and accurately reproducing the realistic response of rubber materials to external stimuli is a crucial research topic that involves all the engineering fields and beyond where these materials are used. This study introduces an innovative nonlinear fractional derivative generalized Maxwell model designed to effectively capture and replicate the experimental behavior of viscoelastic materials. The proposed model addresses the limitations observed in conventional fractional models, providing greater versatility which makes it more suitable for describing the intricate behavior of polymeric materials. Through rigorous mathematical validation, the proposed model demonstrates coherence with the underlying physics of the viscoelastic behavior. To address the identification procedure, the pole-zero formulation is adopted, employing a multi-objective optimization to obtain the optimum, able to replicate the dynamic moduli trends. Satisfying results have been validated over a wide dataset of 10 different materials, demonstrating an extended capability of adapting to different variations than classical widely-used fractional models. Furthermore, the model has proven to be valid even employing a reduced amount of experimental data limited only to low, high-frequency plateaus and around the glass transition temperature, which could be fundamental for optimizing resources in experimental investigations.

Prediction of Mechanical Properties of Nano-Clay-Based Biopolymeric Composites.

An understanding of the mechanical behavior of polymeric materials is crucial for making advancements in the applications and efficiency of nanocomposites, and encompasses their service life, load resistance, and overall reliability. The present study focused on the prediction of the mechanical behavior of biopolymeric nanocomposites with nano-clays as the nanoadditives, using a new modeling and simulation method based on Comsol Multiphysics software 6.1. This modeling considered the complex case of flake-shaped nano-clay additives that could form aggregates along the polymeric matrix, varying the nanoadditive thickness, and consequently affecting the resulting mechanical properties of the polymeric nanocomposite. The polymeric matrix investigated was biopolyamide 11 (BIOPA11). Several BIOPA11 samples reinforced with three different contents of nano-clays (0, 3, and 10 wt%), and with three different nano-clay dispersion grades (employing three different extrusion screw configurations) were obtained by the compounding extrusion process. The mechanical behavior of these samples was studied by the experimental tensile test. The experimental results indicate an enhancement of Young's modulus as the nano-clay content was increased from 0 to 10 wt% for the same dispersion grades. In addition, the Young's modulus value increased when the dispersion rate of the nano-clays was improved, showing the highest increase of around 93% for the nanocomposite with 10 wt% nano-clay. A comparison of the modeled mechanical properties and the experimental measurements values was performed to validate the modeling results. The simulated results fit well with the experimental values of Young's modulus.

Influence of Yarn and Fabric Properties on Mechanical Behavior of Polymer Materials and Its Retention over Time.

The mechanical properties of textile materials play a crucial role in determining their comfort, functionality, performance, safety, and aesthetics. Understanding and optimizing these properties is essential to meet consumer demands. Key aspects of mechanical properties, such as surface roughness, abrasion resistance, and compression, have a significant impact on the touch and durability of the material, as demonstrated by various research studies. This study focuses on analyzing the mechanical properties of materials produced of different polymer yarns and their changes under combined aging factors. The findings emphasize the significance of textile abrasion resistance and surface roughness measurement, particularly for aged materials. Although the use of recycled polyester yarn is sustainable and offers advantages such as higher tensile strength, the results have shown that the use of conventional polyester yarn is more advantageous overall as it has higher abrasion resistance, a smoother surface texture, and better elasticity retention after aging. The insights presented are vital for designing high-performance sportswear, which is crucial in today's competitive environment.

Fractional modeling of cyclic loading behavior of polymeric materials

Fractional modeling of cyclic loading behavior of polymeric materials

A viscoelastic-viscoplastic thermo-mechanical model for polymers under hypervelocity impact

In hypervelocity impact (HVI) events, the thermo-mechanical behaviors of polymer materials are significantly influenced by viscous effects, thereby affecting energy absorption and shielding performance. A viscoelastic-viscoplastic thermo-mechanical model was newly developed to simulate the mechanical behavior and heat generation of polymer materials under HVI. The viscoelastic-viscoplastic constitutive equation and the Mie-Grüneisen equation of state (EOS) were applied to describe the deviatoric deformation and strongly nonlinear volumetric compression behavior. Moreover, the heat generation was calculated considering contributions from plastic work, irreversible entropy increase, and viscous dissipation which was not considered in the previous HVI numerical model. The temperature increase was calculated through adopting the apparent heat capacity method. The model was coded and seamlessly integrated into the LS-DYNA program through a user-defined material subroutine. Additionally, the FEM-SPH adaptive method was adopted to accurately simulate the fragmentation which always accompanies in the HVI event, wherein the finite element method (FEM) handles contact and deformation behavior, while the smoothed particle hydrodynamics (SPH) method addresses the formation and motion of debris clouds. Ultra-high molecular weight polyethylene (UHMWPE) was selected as the sample material, and its model parameters were calibrated based on the data from quasi-static and dynamic experiments. Good effectiveness and accuracy have been validated through comparing the simulation results and experimental results from the literatures. A noteworthy feature is that the model has the capability to capture the temperature distribution and various energy components induced by the impact, including viscous dissipation, plastic work, and residual energy. Finally, a comparison was made to explore the influence of viscosity on the impact response of UHMWPE plates. The analysis results indicate that material viscosity significantly improves energy absorption capabilities and overall shielding performance.

Crystallization manipulation of poly(butylene succinate) via hydrazide-based self-assembled hydrogen-bonding interactions

Self-assembled nucleating agents based on hydrogen bonding interactions have been increasingly used to manipulate the crystallization behavior of polymer materials, yet the specific working mechanism is not well unrevealed. In this study, the hydrazide-based nucleator structure of bis-(mono-methyl succinyl)dihydrazide (MSAD) is randomly incorporated in poly(butylene succinate) (PBS) chains to prepare a series copolymers of PBS-co-MSAD. It is confirmed that the multiple hydrogen-bonding interactions strengthen as the content of the MSAD segment increases, leading to a remarkable elevation of crystallization temperature. Meanwhile, the primary nucleation density of PBS is significantly raised without changing crystal modification. The formation of a pre-ordered network of hydrogen-bonding structures during non-isothermal crystallization is confirmed by rheological measurement and Fourier transform infrared spectroscopy investigation, which immediately induces the crystallization of PBS segments once strong enough. In addition, the melt crystallization mechanism of PBS-co-MSAD with heterogeneous hydrogen-bonding segments is elucidated in detail by combining a self-nucleation protocol.

Energy renormalization for temperature transferable coarse-graining of silicone polymer.

The bottom-up prediction of thermodynamic and mechanical behaviors of polymeric materials based on molecular dynamics (MD) simulation is of critical importance in polymer physics. Although the atomistically informed coarse-grained (CG) model can access greater spatiotemporal scales and retain essential chemical specificity, the temperature-transferable CG model is still a big challenge and hinders widespread application of this technique. Herein, we use a silicone polymer, i.e., polydimethylsiloxane (PDMS), having an incredibly low chain rigidity as a model system, combined with an energy-renormalization (ER) approach, to systematically develop a temperature-transferable CG model. Specifically, by introducing temperature-dependent ER factors to renormalize the effective distance and cohesive energy parameters, the developed CG model faithfully preserved the dynamics, mechanical and conformational behaviors compared with the target all-atomistic (AA) model from glassy to melt regimes, which was further validated by experimental data. With the developed CG model featuring tremendously improved computational efficiency, we systematically explored the influences of cohesive interaction strength and temperature on the dynamical heterogeneity and mechanical response of polymers, where we observed consistent trends with other linear polymers with varying chain rigidity and monomeric structures. This study serves as an extension of our proposed ER approach of developing temperature transferable CG models with diverse segmental structures, highlighting the critical role of cohesive interaction strength on CG modeling of polymer dynamics and thermomechanical behaviors.

Shear strength of geomembrane-cohesive soil interfaces

Despite the increasing use of geosynthetics, the behavior of polymeric materials inserted in the soil is complex, and the wide availability of products on the market makes it difficult to standardize and predict the behavior of the interaction. Therefore, understanding the fundamental mechanisms that involve each type of geosynthetic and its use is a way of directing test conditions to obtain accurate parameters to investigate the behavior of interactions between soil-geosynthetic. In this article, tests were carried out to estimate the interface strength of the soil-smooth geomembrane and soil-textured geomembrane were conducted with cohesive lateritic soil in direct shear equipment with a shear box measuring 300 mm x 300 mm and a reduced area model. The objective of the study was to investigate the influence of soil particle size distribution, the type of geomembrane surface (smooth or textured), and the filling in the lower halfbox (with soil or rigid base) on the resistance of the soil-geosynthetic interface. The results demonstrated the efficiency of the interaction between cohesive soil and a textured geomembrane. For the interaction with a smooth geomembrane, cohesive soil did not exhibit significant mobilization of resistance.

Viscoelastic constitutive artificial neural networks (vCANNs) – A framework for data-driven anisotropic nonlinear finite viscoelasticity

The constitutive behavior of polymeric materials is often modeled by finite linear viscoelastic (FLV) or quasi-linear viscoelastic (QLV) models. These popular models are simplifications that typically cannot accurately capture the nonlinear viscoelastic behavior of materials. For example, the success of attempts to capture strain (rate)-dependent behavior has been limited so far. To overcome this problem, we introduce viscoelastic Constitutive Artificial Neural Networks (vCANNs), a novel physics-informed machine learning framework for anisotropic nonlinear viscoelasticity at finite strains. vCANNs rely on the concept of generalized Maxwell models enhanced with nonlinear strain (rate)-dependent properties represented by neural networks. The flexibility of vCANNs enables them to automatically identify accurate and sparse constitutive models of a broad range of materials. To test vCANNs, we trained them on stress-strain data from Polyvinyl Butyral, the electro-active polymers VHB 4910 and 4905, and a biological tissue, the rectus abdominis muscle. Different loading conditions were considered, including relaxation tests, cyclic tension-compression tests, and blast loads. We demonstrate that vCANNs can learn to capture the behavior of all these materials accurately and computationally efficiently without human guidance. Our source code is available at https://github.com/ConstitutiveANN/vCANN.

Approaches to Control Crazing Deformation of PHA-Based Biopolymeric Blends.

The mechanical behavior of polymer materials is heavily influenced by a phenomenon known as crazing. Crazing is a precursor to damage and leads to the formation of cracks as it grows in both thickness and tip size. The current research employs an in situ SEM method to investigate the initiation and progression of crazing in all-biopolymeric blends based on Polyhydroxyalkanoates (PHAs). To this end, two chemically different grades of PHA, namely poly(hydroxybutyrate) (PHB) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBHV), were melt-blended with polybutyrate adipate terephthalate (PBAT). The obtained morphologies of blends, the droplet/fibrillar matrix, were highly influenced by the plasticity of the matrices as well as the content of the minor phase. Increasing the concentration of PBAT from 15 to 30 wt.% resulted in the brittle to ductile transition. It changed the mechanism of plastic deformation from single craze-cracking to homogeneous and heterogeneous intensified crazing for PHB and PHBHV matrices, respectively. Homogeneous tensile crazes formed perpendicularly to the draw direction at the initial stages of deformation, transformed into shear crazes characterized by oblique edge propagation for the PHBHV/PBAT blend. Such angled crazes suggested that the displacement might be caused by shear localized deformation. The crazes' strength and the time to failure increased with the minor phase fibers. These fibers, aligned with the tensile direction and spanning the width of the crazes, were in the order of a few micrometers in diameter depending on the concentration. The network of fibrillar PBAT provided additional integrity for larger plastic deformation values. This study elucidates the mechanism of crazing in PHA blends and provides strategies for controlling it.

Toward a practical method for measuring glass transition in polymers with low-frequency Raman spectroscopy

Glass transition temperature is one of the most important characteristics to describe the behavior of polymeric materials. When a material goes through glass transition, conformational entropy increases, which affects the phonon density of states. Amorphous materials invariably display low-frequency Raman features related to the phonon density of states resulting in a broad disorder band below 100 cm−1. This band includes the Boson peak and a shoulder, which is dominated by the van Hove peak, and quasi-elastic Rayleigh scattering also contributes to the signal. The temperature dependence of the ratio of the integrated intensity in proximity of the Boson peak to that of the van Hove peak shows a kink near the glass transition temperature as determined by differential scanning calorimetry. Careful analysis of the Raman spectra confirms that this is related to a change in the phonon density of states at the transition temperature. This makes low-frequency Raman a promising technique for thermal characterization of polymers because not only is this technique chemically agnostic and contactless but also it requires neither intensity calibration nor deconvolution nor chemometric analysis.

Atypical brittle failure and compression characteristics of polymer based on acoustic emission

Foamed polymer is frequently used for waterproofing repairs in underground engineering. Since the polymer acts as a plug in structure fractures, the mechanical characteristics of its foaming are essential. Its compressive damage constitutive equation is closely related to the engineering structure life. This study divides polymers into two types based on whether or not the molded skin is removed. Based on acoustic emission monitoring, quasi-static cyclic loading compression tests were conducted under a specific engineering background. Then, the failure modes were classified, and an energy-based constitutive analysis of two damage factors was conducted. The test results show that the presence of molded skin led to “atypical-brittle” failure characteristics, maintaining stepwise stress drop after yielding without densification. Acoustic emission energy-based damage factors can be incorporated into the constitutive model to reflect the damage process in advance. Furthermore, the conventional damage factor-based constitutive model was modified by a compaction factor to describe the characteristics from the elastic state to the atypical-brittle failure. From the perspective of the failure behavior of polymer materials, this work sheds light on grouting materials’ design.

Effect of broaching pre-cracking process on fracture behavior of polymeric materials

Effect of broaching pre-cracking process on fracture behavior of polymeric materials

Fractional-order model for temperature-dependent rheological behaviors of polymeric materials

Rheological behaviors of polymeric materials have been observed to be temperature-dependent, which demands considering temperature effects in constitutive models. In this article, a fractional rheological model is developed to describe creep and relaxation behaviors, of which linear criterion between model parameter and temperature is established. Temperature-dependent fractional orders of the model are naturally interpreted by the well-known master curve. Numerous experimental data in a limited range of temperatures are employed to certificate the applicability of the proposed models. Simulation results demonstrate that the models are equipped with a simpler formulation and high fitting accuracy compared with existing models.

Effects of pH and Crosslinking Agent in the Evaluation of Hydrogels as Potential Nitrate-Controlled Release Systems.

Water scarcity and the loss of fertilizer from agricultural soils through runoff, which also leads to contamination of other areas, are increasingly common problems in agriculture. To mitigate nitrate water pollution, the technology of controlled release formulations (CRFs) provides a promising alternative for improving the management of nutrient supply and decreasing environmental pollution while maintaining good quality and high crop yields. This study describes the influence of pH and crosslinking agent, ethylene glycol dimethacrylate (EGDMA) or N,N'-methylenebis (acrylamide) (NMBA), on the behavior of polymeric materials in swelling and nitrate release kinetics. The characterization of hydrogels and CRFs was performed by FTIR, SEM, and swelling properties. Kinetic results were adjusted to Fick, Schott, and a novel equation proposed by the authors. Fixed-bed experiments were carried out by using the NMBA systems, coconut fiber, and commercial KNO3. Results showed that on the one hand, no significant differences were observed in nitrate release kinetics for any system in the selected pH range, this fact allowing to apply these hydrogels to any type of soil. On the other hand, nitrate release from SLC-NMBA was found to be a slower and longer process versus commercial potassium nitrate. These features indicate that the NMBA polymeric system could potentially be applied as a controlled release fertilizer suitable for a wide variety of soil typologies.

Deep learning based nanoindentation method for evaluating mechanical properties of polymers

In this study, a deep learning based nanoindentation method is proposed to reduce the complexities in evaluating mechanical properties of polymers. To uniquely identify the material parameters, a set of nanoindentation simulations are performed by employing spherical and Berkovich tips. A database that represents the material behavior of polymers under nanoindentation is generated for a set of Drucker-Prager model parameters. A deep neural network (DNN) is trained based on optimized hyper-parameters identified through Bayesian hyperparameter tuning process. The performance of trained DNN model is experimentally validated by performing nanoindentation tests on PC and PMMA. From nanoindentation load-depth (P-h) data, the trained DNN model accurately predicts the material parameters, which are in good agreement with those in the literature.

Effect of various parameters on fracture toughness of polymer composites: A review

As the engineering polymers are initiated to use in most of the critical structural applications analysis of fracture toughness behaviour of polymeric materials has become important in recent years. This review article gives an insight about effect of various parameter on fracture toughness polymer composites. It comprises the various test methods of fracture toughness and describes the utmost important parameters effect on fracture toughness of polymer matrix composites. Fracture toughness of polymer matrix composites mainly depends on type of constituents used as matrix and reinforcement. Addition of fillers modifies the mechanical properties and fracture toughness of polymer matrix composites. In addition to material parameters, operating temperature, thickness also plays the crucial role on fracture toughness of polymer composites. This article gives the comprehensive review on parameters which effects the fracture toughness of polymer composites.

Effects of Microwave‐Assisted Cross‐Linking on the Creep Resistance and Sensing Accuracy of a Coaxial‐Structured Fiber Strain Sensor

AbstractNanocomposites have been widely studied for sensor applications because of their high design flexibility. However, the creep behavior of polymeric materials is a crucial issue of nanocomposite‐based strain sensors in terms of sensing accuracy in high‐temperature environments, which acts as a bottleneck for the use of nanocomposites in embedded type sensors for monitoring the curing reactions and structural health of composite. Herein, we suggest a microwave‐assisted cross‐linking method for enhancing the reliability of polymer‐based coaxial‐structured fiber strain sensors in high temperatures. A dry‐jet wet‐spun ultra‐high molecular weight polyethylene core fiber with a microwave post‐treatment in a peroxide bath provided superior strength (2.1 GPa), modulus (69 GPa), and enhanced creep resistance (>94%). By using a creep resistance‐enhanced core fiber, coaxial‐structured fiber strain sensors, which have multi‐layer structured shell parts, could be fabricated via the simple dip‐coating method. The fabricated coaxial‐structured fiber strain sensor showed a high gauge factor (4.12 with strain range of 0%–1%), a low‐temperature sensitivity coefficient (−6.5 × 10−4), and an improved sensing accuracy (<3% error at 80 °C) in high‐temperature environments. From these results, it can be confirmed that the microwave‐assisted cross‐linking method is very promising for use in nanocomposite‐based strain sensors for various application fields.

Tuning mechanical behavior of polymer materials via multi-arm crosslinked network architectures

Crosslinked polymer materials provide tremendous opportunities for delivering unique material properties for a wide variety of applications such as shape-memory and self-healing materials, aerospace materials, and biomedical systems. Most crosslinked polymer networks investigated to date are designed based on covalent or noncovalent crosslinkers that include two-ended connections to the backbone polymer base. In this paper, we investigate the topic of multi-arm crosslinking of polymer materials using a computational platform. We take advantage of molecular dynamics simulations and graph theory to define computational models that describe potential architectures for multi-arm crosslinked polymer networks. We also discuss feasible experimental routes for implementation of these approaches. We investigate how the polymer network architecture affects the mechanical and self-healing behavior of the material. Our results show that the angular stiffness of multi-arm crosslinkers can be adjusted to control the mechanical strength of the overall polymer network. Additionally, using graph theory, we find that the complex connections that exist between the crosslinkers and the backbone polymer chains define the overall connectivity of the polymer network, which in turn dictates the mechanical behavior of the material. Furthermore, we find that increasing the number of crosslinking arms may improve the self-healing behavior, but it can reduce the overall mechanical strength of the polymer network. The findings of this paper can motivate future experimental and data-driven studies to realize more sophisticated crosslinked polymer materials with a large number of degrees of freedom to deliver a variety of tunable properties.