Properties Of Soft Materials Research Articles

Review of the Anti-Candida albicans Activity and Physical Properties of Soft Lining Materials Modified with Polyene Antibiotics, Azole Drugs, and Chlorohexidine Salts.

This review examined the current state of knowledge on the modifications of commercial soft lining materials (SLMs) with a variety of antifungal compounds: (i) polyene antibiotics, including nystatin and amphotericin B, (ii) azole drugs, including fluconazole, itraconazole, clotrimazole, ketoconazole, and miconazole, and (iii) antiseptics, including chlorhexidine salts to give them anti-Candida albicans properties. The effect of such modifications on the SLMs' physical properties, such as drug release, water sorption, surface properties, bond strength, tensile strength, and hardness, was also analyzed. In effect, this study provided a unique compilation of research results obtained for numerous properties of SLM modified with antifungal compounds that differ in their chemical structure and mechanism of antifungal action. These results might also be useful for prosthetic dentistry, where SLMs are used to prevent and treat candidiasis, the most common disease among denture wearers.

Investigating the influence of bending stiffness and processing parameters on single-leg bending geometries of additively manufactured composites

In less than a decade, additive manufacturing technologies have been developed so successfully that they already produce small and medium-sized lot sizes of prototypes or customised components in the industry. With its new possibilities such as combining materials and technologies, the benefits of different technologies can be merged and broaden future application fields. One way is to print with one technology on a part produced by another. Thus, a thermally bonded part composed of, e.g., selectively sintered and material-extruded components can be created. This combination of technologies was considered in this study to investigate the bonding strength of extrusion-based layers on selectively sintered surfaces and the impact of nozzle temperature, orientation, and thickness of imprinted materials. The extrusion-based layers covered stiff (fibre-reinforced polyamide) and soft (thermoplastic polyurethane) material properties with pre-cracked single-leg bending (SLB) specimens to study their effect on delamination tendencies. This combination of materials and additive manufacturing technologies has not yet been studied for the SLB testing method. The scope of this study is to obtain more information on how relatively hard-hard and hard-soft combinations behave for this special testing method. The results of this testing method were evaluated using two different fracture mechanical approaches to determine the energy release rate. A clear trend towards an improved bonding, thus, higher values, was found for higher nozzle temperatures. Furthermore, a limitation regarding the required bending stiffness of the composites was found. Overall, the single-leg bending testing method enables a relative ranking for material combinations with a certain bending stiffness.

Investigation of structural frustration in symmetric diblock copolymers confined in polar discs through cell dynamic simulation

Nanotechnology has opened new avenues for advanced research in various fields of soft materials. Materials scientists, chemists, physicists, and computational mathematicians have begun to take a keen interest in soft materials due to their potential applications in nanopatterning, membrane separation, drug delivery, nanolithography, advanced storage media, and nanorobotics. The unique properties of soft materials, particularly self-assembly, have made them useful in fields ranging from nanotechnology to biomedicine. The discovery of new morphologies in the diblock copolymer system in curved geometries is a challenging problem for mathematicians and theoretical scientists. Structural frustration under the effects of confinement in the system helps predict new structures. This mathematical study evaluates the effects of confinement and curvature on symmetric diblock copolymer melt using a cell dynamic simulation model. New patterns in lamella morphologies are predicted. The Laplacian involved in the cell dynamic simulation model is approximated by generating a 17-point stencil discretized to a polar grid by the finite difference method. Codes are programmed in FORTRAN to run the simulation, and IBM open DX is used to visualize the results. Comparison of computational results with existing studies validates this study and identifies defects and new patterns.

Relation of synthesis and fatigue property in elastic soft materials

Fatigue has long been studied for many materials, but many aspects are not well understood. Our recent study of the distinct roles of crosslinks and entanglements in the synthesis-property relation of a polymer network under monotonic load leads to a fundamental question: how do crosslinks and entanglements affect the synthesis-property relation of a polymer network under cyclic load? Here we study the relation of synthesis and fatigue property of elastic soft materials without precut cracks. We prepare polyacrylamide hydrogels by free radical polymerization as a model system, and swell the hydrogels in water to equilibrium or to a certain amount of polymer content. The synthesis parameters include the crosslinker-to-monomer molar ratio and the water-to-monomer molar ratio in the precursor, as well as the polymer content in the hydrogel. Three series of hydrogels are prepared. For each hydrogel, the stress-stretch curve under cyclic stretch of various amplitudes and the number of cycles to rupture are measured, giving four properties: fatigue life, endurance stretch, endurance stress, and endurance work. When the crosslinker-to-monomer molar ratio in the precursor is high, the degree of network imperfection on average is low. When the water-to-monomer molar ratio in the precursor is low, the number of entanglements per polymer segment on average is large. We show that crosslinks decrease the susceptibility to fatigue and entanglements increase the endurance stress. By contrast, both crosslinks and entanglements negligibly affect the endurance stretch and the endurance work.

Superdiffusion and heterogeneous dynamics in liquid crystals by microrheology

Microrheology (MR) has emerged as a powerful tool for unraveling the intricate local viscoelastic properties of various soft materials. By tracking the free (passive MR) or forced (active MR) diffusion of a tracer, valuable insights into the mechanical characteristics of the host system can be obtained. In this study, we investigate the forced diffusion of a spherical tracer within isotropic and smectic liquid crystal phases of hard rod-like particles. Our findings reveal superdiffusive behaviour induced by external forces, particularly pronounced when these are aligned parallel to the nematic director. Analysis of the dynamical susceptibility unveils heterogeneities strongly correlated with the magnitude and orientation of applied forces, highlighting the system's critical dependence on structural ordering. Intriguingly, we observe that tracer superdiffusion, driven by external forces and evident across all relevant system directions, does not demonstrate a strong correlation with resulting dynamical heterogeneities.

Deep Indentation Tests of Soft Materials Using Mobile and Stationary Devices.

Measurements of the properties of soft materials are important from the point of view of medical diagnostics of soft tissues as well as testing the quality of food products and many technical materials. One of the frequently used techniques for testing such materials, attractive due to its non-invasive nature, is the indentation technique, which does not puncture the material. The difficulty of testing soft materials, which affects the objectivity of the results, is related to the problems of stable positioning of the studied material in relation to the indentation apparatus, especially with a device held by the operator. This work concerns the comparison of test results using an indentation apparatus mounted on mobile and stationary handles. The tested materials are cylindrical samples of polyurethane foams with three different stiffnesses and the same samples with a 0.5 or 1 mm thick silicone layer. The study presented uses an apparatus with a flat cylindrical indenter, with a surface area of 1 cm2, pressed to a depth of 10 mm (so-called deep tests). Based on the recorded force changes over time, five descriptors of the indentation test were determined and compared for both types of handles. The tests performed showed that the elastic properties of foam materials alone and with a silicone layer can be effectively characterized by the maximum forces during recessing and retraction and the slopes of the recessing and retraction curves. In the case of two-layer materials, these descriptors reflect both the characteristics of the foams and the silicone layer. The results show that the above property of the deep indentation method distinguishes it from the shallow indentation method. The repeatability of the tests performed in the mobile and stationary holders were determined to be comparable.

Improvement of superhydrophobic properties of soft materials based on surface texture design

Superhydrophobic surfaces of rigid materials have been widely studied, which usually have excellent physical properties. However, the soft materials are rarely investigated. In order to explore ways to improve the superhydrophobic properties of soft materials, different textured structures were fabricated by the inverted molding method on three materials, including food-grade silica gel, industrial-grade mold silica gel, and Polydimethylsiloxane (PDMS). Further, a series of experiments were carried out, including water contact angle measurement, self-cleaning test, and drop-bounce test. The results show that hydrophobicity is highly related to both material and texture characteristics. For the same material, the contact angle can be greatly improved by surface micro-texture, and the contact angle of PDMS can be increased to 159.3°. Moreover, texture features and parameters are introduced to quantitatively analyze the contact angle of textured soft materials. It can be predicted that the soft materials with textured superhydrophobic properties will have greater applications in biomedicine, bionic sensors, flexible solar cells, and other fields.

Chemical Botany: Bottlebrush Polymers in Materials Science

Molecular architectures known as bottlebrush polymers provide unique opportunities to tune the structure and properties of soft materials with applications ranging from rubbers to thin films and composites. This review addresses recent developments and future opportunities in the field with an emphasis on materials science enabled by contemporary bottlebrush chemistry.

The origin of good mechanical and soft magnetic properties in a CoFeNi-based high-entropy alloy with hierarchical structure

With the industry development, materials that combine good mechanical properties with new functional applications are in high demand. Here, a solution was provided to overcome low mechanical properties of soft magnetic materials through heterostructure design in CoFeNi high-entropy alloys (HEA). A CoFeNi-based HEA with a hierarchical structure with heterogeneous microstructure, consisting of body-centered cubic (BCC) bands, ultra-fine grained dual-phase and non-recrystallized lamellae (NRL) zones containing a high-density of nanoprecipitates, was fabricated and finely tuned by cold rolling (CR) and annealing. The HEA annealed at 850 °C (HEA-850) exhibits a noteworthy strength-ductility synergy than the counterparts with homogeneous dual-phase microstructures. The yield strength and elongation of the HEA-850 sample are nearly up to 1 GPa and 24%, respectively. Furthermore, because of the elimination of high-density dislocations, this alloy possesses good soft magnetic characteristics, and the saturation magnetization (Ms) and coercivity are 120 emu/g and 455 A/m, respectively. Detailed microstructure observations reveal that the high yield strength is mainly attributed to the interphase boundary strengthening from the dual-phase ultra-fine regions and nanoprecipitate strengthening as well as dislocation strengthening from NRL regions. Due to the mechanical and microstructural heterogeneity, multiple hetero-deformation induced hardening sustains persistent work hardening during tensile deformation, resulting in the reasonable ductility in the HEA-850 sample. The novel provides an important insight into developing high-performance materials with a superior combination of mechanical and functional properties using heterogeneous microstructure.

Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics-Informed Neural Networks.

The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full-field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in data-driven models for learning full-field mechanical responses, such as displacement and strain, from experimental or synthetic data. However, research studies on inferring full-field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, a physics-informed machine learning approach is proposed to identify the elasticity map in nonlinear, large deformation hyperelastic materials. This study reports the prediction accuracies and computational efficiency of physics-informed neural networks (PINNs) in inferring the heterogeneous elasticity maps across materials with structural complexity that closely resemble real tissue microstructure, such as brain, tricuspid valve, and breast cancer tissues. Further, the improved architecture is applied to three hyperelastic constitutive models: Neo-Hookean, Mooney Rivlin, and Gent. The improved network architecture consistently produces accurate estimations of heterogeneous elasticity maps, even when there is up to 10% noise present in the trainingdata.

High-resolution three-dimensional imaging of topological textures in nanoscale single-diamond networks.

Topological defects-extended lattice deformations that are robust against local defects and annealing-have been exploited to engineer novel properties in both hard and soft materials. Yet, their formation kinetics and nanoscale three-dimensional structure are poorly understood, impeding their benefits for nanofabrication. We describe the fabrication of a pair of topological defects in the volume of a single-diamond network (space group Fd m) templated into gold from a triblock terpolymer crystal. Using X-ray nanotomography, we resolve the three-dimensional structure of nearly 70,000 individual single-diamond unit cells with a spatial resolution of 11.2 nm, allowing analysis of the long-range order of the network. The defects observed morphologically resemble the comet and trefoil patterns of equal and opposite half-integer topological charges observed in liquid crystals. Yet our analysis of strain in the network suggests typical hard matter behaviour. Our analysis approach does not require a priori knowledge of the expected positions of the nodes in three-dimensional nanostructured systems, allowing the identification of distorted morphologies and defects in large samples.

A Study on the Optimal Powder Metallurgy Process to Obtain Suitable Material Properties of Soft Magnetic Composite Materials for Electric Vehicles

This study systematically investigates the impact of the material properties of soft magnetic composites (SMCs) on the powder metallurgy forming process. It proposes a suitable material selection process for various motor types and shapes and determines the optimal forming conditions for each SMC material. This study employed the Taguchi design method to identify key control factors such as powder type, forming temperature, and forming speed, and analyzed their effects on relative density. Simulation results indicated that AncorLam HR exhibited superior properties compared with AncorLam and Fe-6.5wt.%Si. The optimal conditions determined through signal-to-noise ratio (SNR) calculations were AncorLam HR at 60 °C and five cycles per minute (CPMs). Validation through simulation and SEM analysis confirmed improved density uniformity and reduced defects in products formed under optimal conditions. Final prototype testing demonstrated that the selected conditions achieved the target density with minimal variance, enhancing the mechanical properties and performance of the motors. These results suggest that the appropriate application of SMC materials can significantly enhance motor efficiency and reliability.

Pushing with Soft Robotic Arms via Deep Reinforcement Learning

Soft robots can adaptively interact with unstructured environments. However, nonlinear soft material properties challenge modeling and control. Learning‐based controllers that leverage efficient mechanical models are promising for solving complex interaction tasks. This article develops a closed‐loop pose/force controller for a dexterous soft manipulator enabling dynamic pushing tasks using deep reinforcement learning. Force tests investigate the mechanical properties of a soft robot module, resulting in orthogonal forces of N. Then, the policy is trained in simulation leveraging a dynamic Cosserat rod model of the soft robot. Domain randomization mitigate the sim‐to‐real gap while careful reward engineering induced pose and force control even without explicit force inputs. Despite the approximate simulation, the sim‐to‐real transfer achieved an average reaching distance of mm (), an average orientation error of rad () and applied pushing forces up to N. Such performance is reasonable for the intended assistive tasks of the manipulator. The experiments uncovered that the soft robot interacting with the environment exhibited torsional and counter‐balancing movements. Although not explicitly enforced, they emerged from the mechanical intelligence of the manipulator. The results demonstrate the potential of soft robotic manipulation via reinforcement learning.

Evaluation of rheological properties of soft lining materials with different composition under various temperatures

PurposeThe aim of this in vitro study was to evaluate the changes the rheological properties of some soft lining materials, to compare the rheological properties and viscoelastic behaviour at different temperatures.Materials and methodsFive soft lining materials (acrylic and silicone based) were used. the storage modulus (G’), loss modulus (G”), tan delta (tan δ) and complex viscosity (η’) were chosen and for each material, measurements were repeated at 23, 33 and 37 °C, using an oscillating rheometer. All data were statistically analyzed using the Mann Whitney U test, Kruskal Wallis test and Conover’s Multiple Comparison test at the significance level of 0.05.ResultsSoft lining materials had different viscoelastic properties and most of the materials showed different rheological behavior at 23, 33 and 37 °C. At the end of the test (t¹5), at all the temperatures, Sofreliner Tough M had the highest storage modulus values while Visco Gel had the highest loss Tan delta values.ConclusionsThere were significant changes in the rheological parameters of all the materials. Also temperature affected the initial rheological properties, and polymerization reaction of all the materials, depending on temperature increase.Clinical implicationsTemperature affected the initial rheological properties, and polymerization reaction of soft denture liner materials, and clinical inferences should be drawn from such studies conducted. It can be recommended to utilize viscoelastic acrylic-based temporary soft lining materials with lower storage modulus, higher tan delta value, and high viscosity in situations where pain complaint persists and tissue stress is extremely significant, provided that they are replaced often.

A Calculation Method for Static Hysteresis Loop and its Applications

Background: Due to experimental equipment limitations, it is usually difficult to measure the quasi-static hysteresis loop at extremely low frequencies, which inevitably introduces errors in the static hysteresis model. Objective: This study aims to propose a method for calculating the static hysteresis loops of grainoriented silicon steel sheets so as to improve the accuracy of the static and dynamic hysteresis model. Methods: The parameters n0 and V0 in the magnetic field strength expression of the excess loss are determined by the measured losses within the range of 0-100Hz. Subsequently, the dynamic components in the quasi-static hysteresis loops are eliminated. Furthermore, a dynamic hysteresis model is established based on the loss and field separation theory, which incorporates the inverse Preisach model. Results: The dynamic hysteresis loops of the grain-oriented silicon steel sheets are measured in the range of 8-250Hz and compared with the results of both the improved model and the original model. Conclusion: Experimental results demonstrate the effectiveness and accuracy of the improved model. The proposed method for calculating static hysteresis loops has universal applicability and is meaningful for accurate simulation and prediction of magnetic properties in soft magnetic materials.

Temperature-dependent dielectric response of free standing PVDF-MoS2 nanocomposite films

Poly(vinylidene fluoride) (PVDF) and its copolymers are known for possessing the highest electroactive response including dielectric constant, piezoelectric and ferroelectric effects which has a wide range of applications such as in energy transfer, energy generation and storage, monitoring and control, and include the development of capacitors, sensors, actuators and so on. We report an enhancement in dielectric properties in PVDF generated by the addition of MoS2 nanosheets. The addition of 1 wt% MoS2 nanosheets as a filler in PVDF, stabilize its beta phase over the alpha phase, resulting in a significant increase in dielectric constant (ε’) across a wide range of frequencies (100 Hz to 1 MHz) and temperatures (140 K- 340 K). The high dielectric constant was achieved by the PVDF-MoS2 nanocomposite while maintaining a relatively low tangent loss factor of 0.94. Such properties in soft polymer-based piezoelectric materials were previously challenging and unachievable. The nanocomposite exhibits excellent ferroelectric properties with 11 times enhancement in remnant polarization compared to pristine PVDF in ambient conditions. The stored energy density (Us) is 39.43 mJ/cm3 for the PVDF-MoS2 film. These lead-free and soft polymer-based nanocomposites can bring remarkable advances in various flexible sensors, data storage, and energy harvesting applications.

Crack-insensitive fracture of elastomer-based soft network materials under monotonic and cyclic loads

In various applications of stretchable electronics and soft robots, soft materials are subjected to long-term load-bearing conditions, requiring them to possess good extreme mechanical properties such as highly stretchable, strong, fracture-resistant and anti-fatigue. Conventional soft materials with a single polymer network typically exhibit relatively low fracture resistance, while commonly employed toughening method involves introducing mechanical dissipation into original polymer networks often require complicated chemical synthesis and result in significant hysteresis under cyclic loads. Designing soft solid materials into soft network materials with periodic lattice structure paving another effective route to improve the fracture-resistant performance of soft materials, which has not been reported in the literature. This work provides a combined experimental and computational study on the mechanical properties and fracture behaviors of elastomer-based soft network materials with/without a precut crack under monotonic and cyclic loads, aiming to present a structural design strategy for fracture-resistant soft materials. The elastomer-based soft network materials are proven to be crack-insensitive, low-hysteresis and anti-fatigue. The nonlinear finite element analysis (FEA) is employed to reveal the underlying mechanism of crack-insensitivity in elastomer-based soft network materials. Both the mechanical properties and deformed configurations of soft network materials with/without a precut crack are accurately predicted by the FEA method. The effect of microstructure geometry and network topology on the mechanical properties of soft network materials is systematically studied. Under various amplitudes of cyclic applied strain, the elastomer-based soft network materials can survive even after individual microstructures ruptured, and the fatigue fracture process slows down as the applied strain amplitude decreases. In contrast to the behavior observed in monotonic loads where fracture initiates at the microstructure located ahead of the crack tip, fatigue fracture initiation in soft network materials with a precut crack exhibits a random distribution.

High-throughput experimental method for measuring fatigue crack growth rate curve of soft materials

The fatigue properties of soft materials, including fatigue crack initiation and propagation, fatigue life and threshold, have been a significant concern in the past few decades. Measuring the fatigue crack growth rate (FCGR) curve is a standard method to characterize fatigue fracture and has been extensively studied in the literature. Conventional methods employing a pure shear configuration measure only one data point on the FCGR curve per test run. Thus, obtaining the whole FCGR curve with numerous data points becomes excessively time-consuming. This work proposes a high-throughput experimental method for measuring the FCGR curve of soft materials. We prepare ten thermoplastic polyurethane (TPU) specimens in single edge notch tensile configuration with different precut crack lengths, use a mechanical mechanism to apply the same cyclic loads to the ten specimens simultaneously, and identify the crack growth of each specimen by image processing. The proposed high-throughput experimental method enables the measurement of over 90 data points on the FCGR curve in a single test run, facilitating the acquisition of the entire curve with just two runs. The obtained FCGR curve exhibits a linear relationship within its middle region on double logarithmic coordinates. The proposed method is further applied to study the effect of temperature on the FCGR curve of TPU specimens. The current experimental setup can be further optimized to accommodate a wider range of soft materials with diverse mechanical properties and specific loading conditions.

SWENet: A Physics-Informed Deep Neural Network (PINN) for Shear Wave Elastography.

Shear wave elastography (SWE) enables the measurement of elastic properties of soft materials in a non-invasive manner and finds broad applications in various disciplines. The state-of-the-art SWE methods rely on the measurement of local shear wave speeds to infer material parameters and suffer from wave diffraction when applied to soft materials with strong heterogeneity. In the present study, we overcome this challenge by proposing a physics-informed neural network (PINN)-based SWE (SWENet) method. The spatial variation of elastic properties of inhomogeneous materials has been introduced in the governing equations, which are encoded in SWENet as loss functions. Snapshots of wave motions have been used to train neural networks, and during this course, the elastic properties within a region of interest illuminated by shear waves are inferred simultaneously. We performed finite element simulations, tissue-mimicking phantom experiments, and ex vivo experiments to validate the method. Our results show that the shear moduli of soft composites consisting of matrix and inclusions of several millimeters in cross-section dimensions with either regular or irregular geometries can be identified with excellent accuracy. The advantages of the SWENet over conventional SWE methods consist of using more features of the wave motions and enabling seamless integration of multi-source data in the inverse analysis. Given the advantages of SWENet, it may find broad applications where full wave fields get involved to infer heterogeneous mechanical properties, such as identifying small solid tumors with ultrasound SWE, and differentiating gray and white matters of the brain with magnetic resonance elastography.

Stiffness and pre-stretching estimation from indentation test of hyperelastic membrane

Obtaining precise data on the mechanical properties of soft materials, often in the form of thin, pre-tensioned membranes, is crucial, especially when conventional testing methods have limited applicability. The article focuses on an innovative method for estimating the Young's modulus and pre-stretching the membranes using the indentation method, assuming an isotropic, incompressible hyperelastic material. The parameters were estimated for two models of indenter-membrane contact: with perfect slip and without slip. Experimental tests were performed for pre-stretched latex membranes glued to a stiffer ring to minimize the effect of attachment on membrane deformation. In order to validate the estimation method, the predictions of the model with the determined mechanical parameters were compared with the results of indentation tests with a liquid layer under the membrane. The discussion shows the consistency of the estimation results with the literature results for small latex deformations, and indicates the advantages and numerous limitations of the approach, especially related to the choice of the material model.