Characterization Of Material Properties Research Articles

A Comprehensive Review of the Simulation Methods for Analysis at the Pore-scale

Fluid flow through porous material is relevant in different fields of engineering, such as in engine and vehicle development, and can be supported through CFD simulation. Numerical simulations at the pore-scale can be used to replace or reduce expensive laboratory measurements. These methods offer a valuable opportunity to connect the pore-scale properties of the porous material with displacement processes on the continuum-scale. Furthermore, they allow researchers to specify crucial flow properties, e.g., capillary pressure, which are crucial for REV-scale research. Three main methods, direct numerical, pore network modeling, and hybrid approaches, are widely used in order to analyze the pore-scale mechanics of fluid flow behavior through porous materials with CFD simulations. The present comprehensive review demonstrates and highlights the significant advantages, disadvantages, and critical challenges in the pore-scale fluid flow simulations. The main challenges include the characterization of material properties, and up-scaling process from pore to continuum or field-scale.

Digitized design and mechanical property reprogrammability of multistable origami metamaterials

Origami-based multistable metamaterials have been recognized as a promising platform for diverse applications due to their exceptional mechanical properties. However, the current state of the art in designing origami metamaterials is mostly ad-hoc and narrowly focused on a particular mechanical property. In other words, there is a lack of basic research on deriving generic and systematic methodologies for the design, configuration tuning, and property programming of origami metamaterials, to explore their achievable ranges of mechanical properties comprehensively from the perspective of mechanics. In this work, a mathematically rigorous strategy for synthesizing and reconfiguring multistable origami metamaterials based on two basic modules is put forward, and a systematic approach to analyze and program the material properties is proposed. More specifically, by programming the binary design array of module arrangement and by mathematically expressing the connection constraints, a number of multistable origami cells with exceptional shape reconfigurability are generated. Building upon this, the fundamental mechanics properties, including the multistability, global stress-strain profiles, and tangent elastic moduli, are thoroughly investigated via examining the unit cell, i.e., the “infinitesimal element,” of the metamaterial. Considering the extreme cases of the unit cell architecture, boundaries of the material properties of the synthesized metamaterials at the stress-free configuration are derived, which are of vital importance for material selections and topological optimization. Moreover, due to the topologically different configurations, six categories of metamaterials with qualitatively different material densities and moduli programmability are revealed. In addition to the reprogrammable density and Young's modulus of conventional Miura origami metamaterial, more intriguing properties, such as reprogrammable shearing modulus, locking effect, and inner reconfiguration without modulus change, are discovered for the first time. A prototype made of dual-material 3D printing is created to experimentally evaluate and verify the effectiveness of the analysis method and the characterized material properties. Overall, this work provides a rigorous tool for designing multistable origami metamaterials and an effective strategy for characterizing their fundamental mechanical properties, which will greatly enhance the systematization of creating origami metamaterials.

Applications of physics-informed neural networks for property characterization of complex materials

The characterization of in-place material properties is important for quality control and condition assessment of the built infrastructure. Although various methods have been developed to characterize structural materials in situ, many suffer limitations and cannot provide complete or desired characterization, especially for inhomogeneous and complex materials such as concrete and rock. Recent advances in machine learning and artificial neural networks (ANN) can help address these limitations. In particular, physics-informed neural networks (PINN) portend notable advantages over traditional physics-based or purely data-driven approaches. PINN is a particular form of ANN, where physics-based equations are embedded within an ANN structure in order to regularize the outputs during the training process. This paper reviews the fundamentals of PINN, notes its differences from traditional ANN, and reviews applications of PINN for selected material characterization tasks. A specific application example is presented where mechanical wave propagation data are used to characterize in-place material properties. Ultrasonic data are obtained from experiments on long rod-shaped mortar and glass samples; PINN is applied to these data to extract inhomogeneous wave velocity data, which can indicate mechanical material property variations with respect to length.

Multi-Layer Material Characterization at Ka-Band Using Bayesian Inversion Method

This paper presents the implementation of the Bayesian inversion method for the characterization and estimation of different dielectric material properties. The scattering parameters of single and multi-layer materials are measured using a free-space experimental setup using a standard gain horn antenna and a Vector Network Analyzer (VNA) at Ka-band (26–40 GHz). The relative permittivity, material thickness, and material positioning error are defined as model parameters and estimated using the observed (measured) data. The FR4 Epoxy, Rogers RT/Duriod 5880, and Rogers AD600 with different relative permittivities and thicknesses are used in the measurement setup. The results displayed good agreement between model parameters and estimated properties of the presented materials, while the corresponding eigenvectors provided a level of confidence in model parameter values. The results were compared with different reported techniques to showcase the possible use of the presented method in microwave imaging, non-destructive testing, and similar applications.

Spray-coating of graphene nanoplatelets on sisal fibers and its influence on electromechanical behavior of biocomposite laminates

Spray coating and vacuum-assisted resin infusion processes were implemented in this work to develop multifunctional biocomposite laminates. The biocomposites were fabricated using a bio-based epoxy resin reinforced with natural sisal fibers coated with graphene nanoplatelets (GNPs). A systematic characterization of material properties was performed to evaluate the mechanical, thermomechanical, electrical, and piezoresistive behavior of biocomposites with different GNPs contents (0, 1, 4, 6, and 8 wt.%). The mechanical tests revealed that adding GNPs to biocomposites has a slight positive effect on their flexural properties compared to the neat biocomposites (without GNPs). The electrical and thermomechanical tests showed that the electrical conductivity and glass transition temperature of biocomposites containing GNPs were enhanced significantly, achieving average values of 5.19 x 10-4 S/m and 63.29°C (26%), respectively. Regarding electromechanical tests, the biocomposites with 8 wt.% GNPs exhibited an excellent piezoresistive behavior under monotonic loading conditions, achieving a gage factor (strain sensitivity) of 3.56. Bending tests with cyclic loading were also performed, and cyclic reproducibility of the piezoresistive response of the biocomposites after 10 cycles was demonstrated, evidencing that the incorporation of GNPs onto sisal fibers by spray-coating produces an effective formation of conductive networks into biocomposites suitable for sensing applications.

Determination of thermal diffusivity of thermoelectric materials using a micro four-point probe method

To develop materials with higher thermoelectric efficiency, a comprehensive characterization of material properties on the relevant spatial scales is pertinent. In this study, we develop a new method based on a micro four-point probe (M4PP) to determine the thermal diffusivity of a bulk material using the phase delay of the second harmonic voltage. The method is demonstrated on two relevant thermoelectric materials i.e. skutterudite and bismuth telluride. Individual measurements on skutterudite and bismuth telluride are characterized by a precision better than 9% and 12%, with mean thermal diffusivities of 1.87 ± 0.17 mm2/s and 1.14 ± 0.13 mm2/s for 100 measurements, respectively. These values are found to be in good agreement with their independent estimates. M4PP electrical characterization of bismuth telluride shows signs of percolation network behavior, thus we believe that the new M4PP methodology applies even to materials whose electrical resistivity is nonuniform and erratic.

Unexpected Photocatalytic Degeneration of NAD + for Inducing Apoptosis of Hypoxia Cancer Cells

Unexpected Photocatalytic Degeneration of NAD ⁺ for Inducing Apoptosis of Hypoxia Cancer Cells

Multiscale model for ultrashort pulsed parallel laser structuring—Part III. Characterization of material properties

In the field of microstructuring and nanostructuring ultrashort pulsed (USP) laser processing attracts increasing attention due to its ability to generate high-precision structures. A simulation of the USP process can lead to a reduced process development time and help to achieve a better geometrical quality of the manufactured microstructures. To predict the ablation shape, temperature distribution, and distortion in a USP ablation process, a detailed simulation of the physical processes during and after the laser ablation is required. Different simulation tools such as multiscale simulations are already established but still need different and accurate input parameters regarding the material properties of the workpiece to be machined. A material characterization procedure that can be used in a standardized way for different materials and processing stations needs to be developed. The procedure determines the absorption coefficient, penetration depth, and ablation threshold precisely matched to a USP machine and the material used. Based on the material characterization procedure a calibration of the used simulation has to be carried out as precisely as possible. The simulation can then be applied for a digital process development and subsequently validated with specific experiments.

An Improved Experiment for Measuring Lithium Concentration-Dependent Material Properties of Graphite Composite Electrodes.

The in situ curvature measurement of bilayer beam electrodes is widely used to measure the lithium concentration-dependent material properties of lithium-ion battery electrodes, and further understand the mechano-electrochemical coupling behaviors during electrochemical cycling. The application of this method relies on the basic assumption that lithium is uniformly distributed along the length and thickness of the curved active composite layer. However, when the electrode undergoes large bending deformation, the distribution of lithium concentration in the electrolyte and active composite layer challenges the reliability of the experimental measurements. In this paper, an improved experiment for simultaneously measuring the partial molar volume and the elastic modulus of the graphite composite electrode is proposed. The distance between the two electrodes in the optical electrochemical cell is designed and graphite composite electrodes with four different thickness ratios are measured. The quantitative experimental data indicate that the improved experiment can better satisfy the basic assumptions. The partial molar volume and the elastic modulus of the graphite composite electrode evolve nonlinearly with the increase of lithium concentration, which are related to the phase transition of graphite and also affected by the other components in the composite active layer. This improved experiment is valuable for the reliable characterization of the Li concentration-dependent material properties in commercial electrodes, and developing next-generation lithium batteries with more stable structures and longer lifetimes.

Characterization of material properties and deformation in the ANGUS phantom during mild head impacts using MRI

Traumatic brain injury (TBI) is a major health concern affecting both military and civilian populations. Despite notable advances in TBI research in recent years, there remains a significant gap in linking the impulsive loadings from a blast or a blunt impact to the clinical injury patterns observed in TBI. Synthetic head models or phantoms can be used to establish this link as they can be constructed with geometry, anatomy, and material properties that match the human brain, and can be used as an alternative to animal models. This study presents one such phantom called the Anthropomorphic Neurologic Gyrencephalic Unified Standard (ANGUS) phantom, which is an idealized gyrencephalic brain phantom composed of polyacrylamide gel. Here we mechanically characterized the ANGUS phantom using tagged magnetic resonance imaging (MRI) and magnetic resonance elastography (MRE), and then compared the outcomes to data obtained in healthy volunteers. The direct comparison between the phantom's response and the data from a cohort of in vivo human subjects demonstrate that the ANGUS phantom may be an appropriate model for bulk tissue response and gyral dynamics of the human brain under small amplitude linear impulses. However, the phantom's response differs from that of the in vivo human brain under rotational impacts, suggesting avenues for future improvements to the phantom.

Effectiveness of the analytical models used in standard techniques for mechanical property characterization of 2D materials

Two dimensional (2D) materials are usually assumed to be continuum thin films to study their mechanical behavior. Freestanding indentation testing and pressure bulge testing are two standard techniques for characterizing the mechanical properties of thin films. Compared with the former, the latter is far less often used to characterize the elastic moduli of 2D materials, even though there is no solid evidence to show that the former is more effective than the latter. In the present work, the effectiveness of the analytical models adopted in both approaches on determining the elastic moduli of 2D materials are investigated using numerical analysis, which can be evaluated based upon the accuracy of the determined elastic moduli, and the effects of Poisson’s ratio and the applied loading range are also considered. To account for the effect of the adhesive boundary condition of freestanding 2D materials in the testing, the numerical simulations of the initially unstressed samples, pre-stretched samples and slack samples are all analyzed in the present work. It is found that the bulge testing model is actually more effective than the indentation model on determining the mechanical properties of 2D materials.

Impact of Carbon Nanotubes on the Mechanical and Electrical Properties of Silicone

Abstract This paper presents the results of a structure study of a dispersion composite on a silicone matrix with a filler in the form of multi-walled carbon nanotubes (MWCNTs). The study aims to determine the effect of the filler on the composite mechanical properties and electrical conductivity. Materials that are electrically conductive and exhibit high mechanical properties can find applications in high-strain sensors. During the study, the characteristic properties of the susceptible materials, silicone alone and silicone with different filler contents (4%, 6%, and 8% by weight), were determined after curing. Microscopic observations were performed to assess the influence of carbon fillers on the material structure and to determine the level of homogeneity of the material. Examination of mechanical properties facilitated the determination of the Shor A hardness (ShA), stiffness, and Poisson’s ratio of the cured composites, depending on the nanotubes’ content. In parallel with the study of mechanical properties, the effect of loading, and the associated deformation of the samples, on the conductivity of the composite was investigated. Based on the results obtained, a discussion was carried out on the type of conductivity characteristic of silicone with different filler content as well as depending on the level of deformation of the samples.

Characterization of Mechanical Material Properties with Fractal Analysis

Material selection is one of the main factors in the building structure. In this study, an alternative method was implemented using fractal analysis method. The use of this method can be used for cost savings and accident rates to identify the mechanical properties of each material. The purpose of this research is to study the time series resulting from experiments using piezo film sensors using fractal analysis and investigating the properties of different mechanical materials (poisson ratios) with different impact forces using fractal dimensions. There are four types of selected materials namely brass, copper, mild steel and stainless steel which is in round in shape. Different impact forces are generated by using an impact hammer and subsequently a vibration signal is obtained from a piezo film sensor. Using Matlab software, analysis using the fractal method was performed. The fractal dimension was obtained from the gradient values of the log-log plot and the fractal dimension was calculated for each impact force applied to each specimen. Then, fractal dimension values were compared using CES Edupack2012 for characterization of the properties of each material. It can be concluded that the value of fractal dimension increases when the impact forces increase too whereas a decrease in the poisson ratio occurs when the fractal dimensions of each material increase.

A general theory for the bending of multilayer van der Waals materials

Multilayer van der Waals (vdW) materials exhibit nontrivial out-of-plane responses and deformability due to their inherent atomic scale thickness coupled with weak interlayer vdW interactions, which call for a general mechanical framework to quantify such extraordinary characteristics given the structure and material properties. Here, we demonstrate that by considering the competing mechanisms between intralayer stretching, interlayer shearing, and monolayer bending, a general centerline-based theory for multilayered structures is developed to portray the bending landscape of multilayer vdW materials. Combined with large-scale molecular dynamics simulations of three-point bending, we find that when the slenderness ratio or the interlayer shear rigidity is smaller or the intralayer elasticity is higher, the multilayered structures exhibit pronounced nonplanar section deformation due to the interlayer shear capacity. To elucidate quantitative correlations between deformation distribution and structural dimensions and material properties, a critical criterion is proposed to depict the transformation from planar to nonplanar section deformation through a dimensionless characteristic parameter. The effective bending stiffness is then systematically explored in the space of characteristic geometries and material properties to reveal the underlying mechanism of the anomalous size effect. We identify that the effective bending stiffness follows a unified scaling law regarding the dimensionless characteristic parameter. More importantly, a series of deformation-mode phase diagrams are constructed using two universal characteristic lengths, intuitively illustrating the transition of dominated deformation modes and the synergism of geometries and inherent material properties on the bending responses. Validated by several typical vdW materials and novel semiconducting two-dimensional materials, the proposed diagrams not only deepen the understanding of bending in multilayer vdW materials but also provide guidelines for the design and optimization of vdW material-based devices.

Equivalent characterization of pre-strained material properties and mechanical behavior prediction of steel/aluminum self-piercing riveted joints

Self-piercing riveting formation results in additional straining and property variations of the sheet material and rivet of the joint. As the riveted forming strain was concentrated within a limited area, it was difficult to characterize the properties of the pre-strained materials through conventional tensile tests. The present study dealt with the effects of considering riveted forming strain on the mechanical behavior prediction accuracy of the self-piercing riveted (SPR) joints of DP590 high-strength steel and AC43500-T7 cast aluminum alloy sheets. Main efforts were put on the evaluation of the failure parameters and behaviors of the pre-strained sheet materials. Equivalent specimens of the two sheet materials with different pre-strain levels were prepared via a flat rolling process for the characterization of the sheet deformation during the riveting process. Based on the material parameters obtained from the tensile tests of the rolled sheets and the compression tests of the rivet, a three-dimensional simulation model of the SPR joints was established. The mechanical behaviors of the joints were investigated under three loading conditions of 0° pull-out, 90° lap shear, and 45° tension–shear. The prediction accuracies of pull-off displacement under the three loading conditions reached 94.07%, 91.61% and 94.02%, and were 5.57%, 26.43% and 6.18% higher than those predicted by the model without considering the riveted forming history, respectively. The prediction accuracies of the peak force under the three loading conditions were 98.43%, 89.70% and 95.35%, respectively. The error difference of predicted peak force of the two models was less than 3%.

Effect of cross-head speed on mechanical properties of photosensitive resins used in three-dimensional printing of a stereolithographic elastomer

Abstract It is known that the strain rate affects significantly the mechanical properties of the engineering materials. The aim of this study is to investigate the effect of the cross-head speed on the mechanical properties of acrylate and methacrylate-based oligomer/monomer materials of the specimens produced by Stereolithography (SLA) printer. The characteristic mechanical properties of materials, such as modulus of elasticity, Poisson’s ratio, and the ultimate strength depending on low strain rate levels were explored. The fractured surfaces of the tensile test specimens were examined by Scanning Electron Microscopy (SEM). Based on the experimental results, it is concluded the mechanical properties of the photosensitive resins are quite sensitive to the tensile test speeds.

Optimization and evaluation of Xiaoer Pudilan Xiaoyan Syrup based on characterization of material properties

According to the taste analysis of Pudilan Xiaoyan Oral Liquid, the unpleasant taste of the oral liquid is mainly caused by the inherent taste of Chinese medicine and the taste introduced in the preparation process, which leads to its unpopularity among children. Therefore, aiming at the special children patient group, Xiaoer Pudilan Xiaoyan Syrup was developed via technology optimization and dosage form improvement to improve the unpleasant taste and enhance the medication compliance among children. Based on the material properties of Pudilan Xiaoyan Oral Liquid and Xiaoer Pudilan Xiaoyan Syrup extracts, the authors compared the properties(pH, density, turbidity, viscosity, chromaticity, particle size), taste, content of five quality markers and in vivo pharmacokinetic characteristics of these two preparations, to evaluate the suitability of Xiaoer Pudilan Xiaoyan Syrup. The results showed that compared with those of Pudilan Xiaoyan Oral Liquid, the pH, density, turbidity, viscosity and chromaticity of Xiaoer Pudilan Xiaoyan Syrup were significantly changed, and the unpleasant taste was reduced by 26%; the transfer rate of the main active ingredients chicoric acid was increased, while the transfer rate of baicalin had small difference from that of the oral liquid. In addition, pharmacokinetics revealed that the total absorption amount of baicalin in vivo was higher, and the time to peak T_(max) of baicalin and oroxindin in the syrup and the mean residence time MRT_(last )of corynoline in vivo were significantly prolonged. The absorption degree of Xiaoer Pudilan Xiaoyan Syrup and Pudilan Xiaoyan Oral Liquid in the body was the same: baicalin>oroxindin>corynoline. The new dosage form process was simpler than that of the original dosage form, safe, environmentally friendly, reasonable and feasible, meeting the mass production demand. This provided a basis for the reasonable and scientific optimization of Xiaoer Pudilan Xiaoyan Syrup, and also laid a foundation for its further safe and rational use, so as to expand the clinical application in children.

Torsional and compression loading of paperboard packages: Experimental and FE analysis

The present study investigates torsional and compressive loading of a paperboard package. Finite element (FE) analyses simulating the tests were performed to improve understanding of the stresses and deformations in the paperboard during loading. A simple experimental characterization of the necessary material properties could be performed to represent the multi‐ply paperboard as a single‐ply structure. The results from the single‐ply model were compared with a laminate model, and the differences between the models were small. Comparing experimental and FE simulations of box compression and torsion showed that the FE models could accurately predict the response curves. However, in the simulations, there was an overprediction of the maximum compressive force and maximum torque, which was expected since geometrical imperfections and the heterogeneous internal structure of the material were not accounted for in the material model or the FE model. Local yield lines formed at the onset of non‐linearities in the package load–displacement curves. Therefore, the strength of the paperboard affects the maximum compressive strength and maximum torque, and the bending stiffness of the paperboard only had a minor effect. When a first local maximum was reached, the number of FE that reached the failure stress increased exponentially. The simulations also showed that box compression was not an effect of package height, but higher packages had a lower maximum torque.

Effect of ammonia on the hydraulic conductivity and adsorption characteristics of lithomargic clay – bentonite barrier in landfills

Alteration of characteristic properties of the liner material occurs mainly due to interaction with contaminants present in the landfill leachate. The present study deals with the effect of inorganic contaminants on the behaviour of bentonite-based landfill liner material. Saturated hydraulic conductivity, unconfined compressive strength and compaction characteristics of the lithomargic clay and its blends permeated with an inorganic contaminant (ammonia) were evaluated. Experiments were conducted by blending lithomargic clay with 5%, 7.5% and 10% bentonite. The results showed that hydraulic conductivity decreased from an initial value of 3 × 10−8 m/s to 6.83 × 10−11 m/s. Unconfined compressive strength at wet of optimum and maximum dry density of blended lithomargic clay also reduced slightly. Adsorption studies were conducted, and the coefficient of distribution (Kd) for blended lithomargic clay with 5% and 10% was found to be 60.46 L/kg and 94.74 L/kg, respectively. The Kd values indicate good adsorption characteristics of bentonite-based material. Overall, the results showed that lithomargic clay blended with bentonite is a suitable alternative barrier for constructing engineered landfills to prevent environmental degradation and other health effects.

High-efficient low-cost characterization of composite material properties using domain-knowledge-guided self-supervised learning

Modern AI-assisted approaches have revolutionized our abilities to better understand the properties of concrete and composite materials. However, current machine learning models such as supervised learning (SL) models usually require a large amount of training data to feed the model. Here, we introduce self-supervised learning (SSL) to address the issue of lacking labeled data in concrete material characterization. We propose a generalized SSL-based framework with domain knowledge and demonstrate its robustness to predict the properties of a commonly-used composite material (concrete) with the fewest data possible. Our numerical results show that the performance of the proposed SSL model can match the commonly-used supervised learning model with only 5 % of data, and the SSL model is also proven with ease of implementation. Our study paves the way to expand further the usability of machine learning tools for composite material fields and the broader material science community.