Characterization Of Material Properties Research Articles

Uncertainty quantification of additive manufacturing post-fabrication tuning of resonator-based microwave sensors

Reconfigurability, especially in terms of the ability of adjusting the operating frequency, has become an important prerequisite in the design of modern microwave components and systems. It is also pertinent to microwave sensors developed for a variety of applications such as characterization of material properties of solids or liquids. This paper discusses uncertainty quantification of additive-manufacturing-based post-fabrication tuning of resonator-based sensors implemented using a microstrip technology. Therein, the operating frequency is altered by adding metallic patches of a specific size determined by the full-wave electromagnetic (EM) model of the system. The reliability of setting up the center frequency depends on both the accuracy of the patch size (manually cut out of the copper tape), and its allocation with respect to the resonator. A rigorous statistical analysis of the patch size and its allocation errors is carried out, including a quantification of their joint effects on the sensor operating frequency. Furthermore, the analysis of a possibility of compensating the patch size inaccuracies through its appropriate positioning is conducted. The details of the proposed approach are explained using a complementary symmetric split ring resonator (CSSRR)-based sensor designed to operate in X and Ku bands with the tuning range between 10 GHz and 20 GHz. The optimized sensor's fundamental resonant frequency is 9.4 GHz, its exterior size is 25 × 30 mm2, the quality factor of the fabricated sensor is 29, and the sensitivity of the considered design is 1.1 GHz/mm with the measurement error is 0.1 percent. The obtained measurement data are indicative of a practical utility of the additive-manufacturing-based tuning technique, in particular, a possibility of reliable center frequency tuning under mild assumptions on the accuracy of manual preparation of the tuning patches. Furthermore, a practical tuning scheme has been developed and experimentally validated, which allows for a precise allocation of the operating frequency with the error not exceeding 0.01 GHz (or 0.1% in relative terms), all under assumptions of a manual preparation and placement of the tuning patch.

Non-uniform image reconstruction for fast photoacoustic microscopy of histology imaging.

Photoacoustic microscopic imaging utilizes the characteristic optical absorption properties of pigmented materials in tissues to enable label-free observation of fine morphological and structural features. Since DNA/RNA can strongly absorb ultraviolet light, ultraviolet photoacoustic microscopy can highlight the cell nucleus without complicated sample preparations such as staining, which is comparable to the standard pathological images. Further improvements in the imaging acquisition speed are critical to advancing the clinical translation of photoacoustic histology imaging technology. However, improving the imaging speed with additional hardware is hampered by considerable costs and complex design. In this work, considering heavy redundancy in the biological photoacoustic images that overconsume the computing power, we propose an image reconstruction framework called non-uniform image reconstruction (NFSR), which exploits an object detection network to reconstruct low-sampled photoacoustic histology images into high-resolution images. The sampling speed of photoacoustic histology imaging is significantly improved, saving 90% of the time cost. Furthermore, NFSR focuses on the reconstruction of the region of interest while maintaining high PSNR and SSIM evaluation indicators of more than 99% but reducing the overall computation by 60%.

Correction: Review: 2D material property characterizations by machine-learning-assisted microscopies

Correction: Review: 2D material property characterizations by machine-learning-assisted microscopies

Review: 2D material property characterizations by machine-learning-assisted microscopies

Review: 2D material property characterizations by machine-learning-assisted microscopies

Material Property Characterization and Parameter Estimation of Thermoelectric Generator by Using a Master–Slave Strategy Based on Metaheuristics Techniques

Thermoelectric generators (TEGs) have gained significant interest as a sustainable energy source, due to their ability to convert thermal energy into electrical energy through the Seebeck effect. However, the power output of TEGs is highly dependent on the thermoelectric material properties and operational conditions. Accurate modeling and parameter estimation are essential for optimizing and designing TEGs, as well as for integrating them into smart grids to meet fluctuating energy demands. This work examines the challenges of accurate modeling and parameter estimation of TEGs and explores various optimization metaheuristics techniques to find TEGs parameters in real applications from experimental conditions. The paper stresses the importance of determining the properties of TEGs with precision and using parameter estimation as a technique for determining the optimal values for parameters in a TEG mathematical model that represent the actual behavior of a thermoelectric module. This methodological approach can improve TEG performance and aid in efficient energy supply and demand management, thus reducing the reliance on traditional fossil fuel-based power generation.

A phase field model for fractures in ice shelves

AbstractIce shelves are large floating ice masses, that are formed when glaciers are becoming afloat at the margin of ice sheets. One dominating mass loss mechanism of ice shelves is calving, describing the detachment of icebergs at the front. Ice shelves stabilize inland ice glaciers due to buttressing. If the stabilizing effect of an ice shelf vanishes because of disintegration or thinning, the corresponding glacier accelerates resulting in sea level rise.To describe calving and disintegration of ice shelves, it is important to investigate fracture propagation in ice. A powerful method in fracture mechanics is the phase field method which is based on Griffith's theory. It approximates cracks in a diffuse manner by using a continuous scalar field. We propose a phase field fracture model for ice considering its characteristic material properties. The material behavior of ice depends on the considered time scales. On short time scales it behaves like a solid and while it acts like a fluid on long time scales, which classifies it as a viscoelastic material of Maxwell type. This has been verified by observations. The phase field method allows us to simulate typical fracture situations of ice shelves in Antarctica and Greenland.

Superposition of induced polarization signals measured on pyrite–sand mixtures

SUMMARY Induced polarization (IP) is a common method in ore exploration. IP spectra measured over a wide frequency range can be used to characterize material properties of ores, slags and other residual material from mines and processing facilities. Previous studies have shown that IP parameters are sensitive to type, content or grain size of electronically conductive or semi-conductive minerals. Up to now, a variety of experiments has been performed on sand mixtures with fractions of ore minerals. Most experiments consider only a single fraction with a fixed grain size. We continue a series of experiments that have been done with sand–pyrite mixtures. The presented study compares IP spectra recorded for samples either with a single grain radius fraction (E-samples) or with two different grain radii fractions (Z-samples). The spectra are fitted to Pelton models. A Debye decomposition that provides a relaxation time distribution (RTD) is applied to the complex conductivity spectra. The RTD indicates separated maxima only if the ratio of mean grain radii is larger than a factor five. The resolution of the phase spectra and the spectra of imaginary part of conductivity is lower. Even though the volumetric pyrite content is equal in each fraction, the phase spectra and RTD of the Z-samples indicate much higher signals for the pyrite fraction with smaller grain radius. The same observation is made for the chargeability that shows larger values for decreasing grain radii. This finding contradicts existing theories that consider the chargeability as a suitable proxy of the volumetric content of ore minerals. We explain the observed effect by an interaction between neighbouring pyrite particles. The conductivity of the mixtures of the E-samples increases with decreasing grain radius of the pyrite fraction. This effect is attributed to dissolution effects on the surface of the pyrite particles during the sample preparation. We find that the additive superposition of the phase spectra of two E-samples is in good agreement with the measured phase spectra of the Z-samples (measured superposition) containing the two corresponding pyrite fractions. The agreement is slightly worse for the spectra of imaginary part of conductivity, where the measured superposition overestimates the mathematical superposition. The experimental results of our study motivate a further improvement of existing mechanistic models.

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.