Material Parameters Research Articles

Modeling Electronic Devices with a Casimir Cavity

The Casimir effect has been exploited in various MEMS (micro-electro-mechanical system) devices, especially to make sensitive force sensors and accelerometers. It has also been used to provide forces for a variety of purposes, for example, for the assembly of considerably small parts. Repulsive forces and torques have been produced using various configurations of media and materials. Just a few electronic devices have been explored that utilize the electrical properties of the Casimir effect. Recently, experimental results were presented that described the operation of an electronic device that employed a Casimir cavity attached to a standard MIM (metal–insulator–metal) structure. The DC (direct current) conductance of the novel MIM device was enhanced by the attached cavity and found to be directly proportional to the capacitance of the attached cavity. The phenomenological model proposed assumed that the cavity reduced the vacuum fluctuations, which resulted in a reduced injection of carriers. The analysis presented here indicates that the optical cavity actually enhances vacuum fluctuations, which would predict a current in the opposite direction from that observed. Further, the vacuum fluctuations near the electrode are shown to be approximately independent of the size of the optical cavity, in disagreement with the experimental data which show a dependence on the size. Thus, the proposed mechanism of operation does not appear correct. A more detailed theoretical analysis of these devices is needed, in particular, one that uses real material parameters and computes the vacuum fluctuations for the entire device. Such an analysis would reveal how these devices operate and might suggest design principles for a new genre of electronic devices that make use of vacuum fluctuations.

A new method for identifying elastic parameters of isotropic materials based on square specimens

This paper proposes a new impulse excitation technique using a square plate. First, the functional relationship between the modal frequency of the specimen and the geometrical dimensions and mechanical parameters was established by using the finite element method. Then, the continuous functional relationship derived by a homotopy method allowed the frequency ratios to be related to the thickness-to-length ratio and Poisson’s ratio. By measuring the frequency ratios and thickness-to-length ratio, Poisson’s ratio could be calculated using this functional relationship. When the density and Poisson’s ratio were known, Young’s modulus could be identified inversely in conjunction with the finite element analysis. Finally, a comparison test between this method and the traditional impulse excitation technique was designed and implemented, and the results showed that this method has advantages in both testing efficiency and accuracy. The study provides a new idea for system identification, which has important application value and promotion significance.

Broadband surface wave manipulation by periodic barriers in unsaturated soil.

Periodic wave barriers have been widely used to manipulate elastic waves propagating in saturated and single-phase soil due to their attenuation zone properties. However, it is difficult to promote application of periodic barriers in unsaturated soils due to their complex constitutive relationship. In this study, manipulation of surface waves by periodic in-filled trench barriers in unsaturated soil has been studied based on the periodic theory. The dispersion relations of a periodic structure for surface waves in unsaturated soil are determined. The attenuation mechanism of evanescent surface waves is revealed. Next, the effects of several key parameters of unsaturated soil on the attenuation zones of the periodic in-filled trench barriers are comprehensively discussed. It is found that in a particular range for material parameter, the surface waves are attenuated over the entire frequency range due to the viscosity of fluid. Finally, a periodic in-filled trench barrier is designed according to a field test of ground vibration induced by a train, and its performances in mitigating surface waves propagating in unsaturated and saturated soils are conducted and compared by conducting analysis in time domain. This investigation provides a new insight for manipulating surface waves by periodic barriers. This article is part of the theme issue 'Current developments in elastic and acoustic metamaterials science (Part 1)'.

Predicting PbS Colloidal Quantum Dot Solar Cell Parameters Using Neural Networks Trained on Experimental Data

Recent advances in machine learning (ML) have enabled predictive programs for photovoltaic characterization, optimization, and materials discovery. Despite these advances, the standard photovoltaic materials development workflow still involves manually performing multiple characterization techniques on every new device, requiring significant time and expenditures. One barrier to ML implementation is that most models reported to date are trained on computer simulated data, due to the difficulty in experimentally collecting the massive data sets needed for model training, limiting the ability to assess the limitations and validity of these methods, as well as to access new potential physical mechanisms absent in simulations. Herein, several neural networks trained on experimental data from PbS colloidal quantum dot thin‐film solar cells are introduced. These models predict multiple, complex materials properties, including carrier mobility, relative photoluminescence intensity, and electronic trap‐state density, from a single, simple measurement: illuminated current–voltage curves. The measurement system considers the spatial distribution of the materials parameters to gather and predict large amounts of data by treating an inhomogeneous device as a series of thousands of micro‐devices, a novel feature compared to existing solutions. This model can be extended to other materials and devices, accelerating development times for new optoelectronic technologies.

High-Sensitivity Janus Sensor Enabled by Multilayered Metastructure Based on the Photonic Spin Hall Effect and Its Potential Applications in Bio-Sensing.

The refractive index (RI) of biological tissues is a fundamental material parameter that characterizes how light interacts with tissues, making accurate measurement of RI crucial for biomedical diagnostics and environmental monitoring. A Janus sensor (JBS) is designed in this paper, and the photonic spin Hall effect (PSHE) is used to detect subtle changes in RI in biological tissues. The asymmetric arrangement of the dielectric layers breaks spatial parity symmetry, resulting in significantly different PSHE displacements during the forward and backward propagation of electromagnetic waves, thereby realizing the Janus effect. The designed JBS can detect the RI range of 1.3~1.55 RIU when electromagnetic waves are incident along the +z-axis, with a sensitivity of 96.29°/refractive index unit (RIU). In the reverse direction, blood glucose concentrations are identified by the JBS, achieving a sensitivity of 18.30°/RIU. Detecting different RI range from forward and backward scales not only overcomes the limitation that single-scale sensors can only detect a single RI range, but also provides new insights and applications for optical biological detection through high-sensitivity, label-free and non-contact detection.

Digital twin-based identification of crystal plastic material parameters for weld joints of orthotropic steel decks

The identification of crystal plasticity (CP) material parameters is indispensable for using CP models to simulate and understand the microcrack initiation and propagation of orthotropic steel decks (OSD). This study proposes a digital twin (DT)-based framework for identifying CP material parameters of weld joints of OSD by fusing multiscale CP finite element model (CPFEM) with macroscale stress–strain material tests. The material tests of the specimen (physical entity) cut from the weld joints of OSD and sliced to the centimeter scale are carried out using the standard dynamic test system. The CPFEM simulation is used to develop a multiscale virtual entity to map the physical entity. The particle swarm optimization algorithm is used to fuse the CPFEM simulation with the material test data to identify CP material parameters and produce a DT. The results demonstrate that the CP material parameters identified by the proposed framework are more accurate than those identified by a single representative volume element method. The results also show that the DT-based identification of CP material parameters has high applicability.

Optimal design of laminated thermoelectric cylindrical device based on neural network and thermo-electric-mechanical coupled model

The laminated thermoelectric cylindrical shell is favorable for solar thermal energy harvesting, but the thermo-electric-mechanical coupling field is relatively complex. An optimization design model of thermoelectric structure is required to promote its practical application. In this paper, based on variable separation method, thermo-electric-mechanical coupled model of laminated thermoelectric cylindrical device under thermal shock is constructed. Transient temperature field, thermal stress and power generation efficiency are obtained. Numerical results show that both power generation efficiency and thermal stress are affected by material parameters simultaneously. In order to analyze efficiency and structural reliability comprehensively, a fitness function is introduced in this paper. Based on the data which are collected from thermoelectric coupled model, a neural network is established and trained, which can predict and evaluate the performance of thermoelectric cylindrical shell based on the fitness function. The optimal material parameters for thermoelectric shells with high efficiency and low thermal stress are obtained. Combining neural network technique and the theoretical model developed in this paper, the computation time of numerical results can be reduced from 50 to 3 min, and this neural network optimization method has higher computational efficiency. These research results will provide guidance for the optimization design of cylindrical thermoelectric device.

Shear wave velocity in a functionally graded piezoelectric semiconductor plate clamped on a rigid base

Abstract This paper investigates the propagation of horizontally polarized shear waves in a Piezoelectric Semiconductor (PSC)
layered structure. The modal consists of a pre-stressed PSC thin plate atop an elastic dielectric half-space joined
perfectly at the interface. It is postulated that the material parameters and initial stress exhibit an exponential
variation exclusively along the depth. The velocity equation of the considered wave is analytically obtained based
on the traction-free boundary conditions. Numerical examples have been employed to examine the influences of
several parameters, including semiconducting properties, material gradient index, initial stresses, external biasing
electric field, and PSC film thickness, on the characteristics of the wave. Graphs have been generated to visualize
the dependency of wave velocity and attenuation on these factors. The wave’s velocity and damping properties are
significantly influenced by the thickness and steady state carrier density of the PSC plate. Besides yielding critical
results, current findings are instrumental in designing high-frequency SAW devices.

Application of non-ionic liquids-based modified dielectrics during electric discharge machining (EDM) of Ti6Al4V alloy to enhance machining efficiency and process optimization

The non-conventional manufacturing technologies are notorious when it comes to productivity and processing time in production-related industries. However, the aerospace and other high-end sectors enjoy another quality matrix of these processes and compromise on the processing time. For instance, the machinability of hard-to-cut materials such as Ti6Al4V aerospace alloy for micro-impressions is challenging and commonly carried out through non-conventional processes. Among these processes, the electric discharge machining (EDM) is famous for machining Ti6Al4V. In the current study, the EDM process is enhanced through modified dielectrics such as kerosene with non-ionic liquids (span 20, 60, and 80) and cryogenically treated tool electrodes (aluminum and graphite), and is compared to the conventional kerosene-based process. A three-phase experimental campaign is deployed to explore parametric effects including modified dielectric conditions (non-ionic liquid type and concentration), tool material, and machine parameter pulse ON:OFF time. A total of 60 experiments (54 modified dielectrics and 6 as baseline) were performed to explore process physics. The statistical analyses show that the unmodified process (kerosene dielectric-based) results in the least favorable results 0.58 mm3/min against cryo-graphite and 1.2 mm3/min against cryo-aluminum electrodes. However, the modified dielectrics outperform and improve process dynamics by altering dielectric conditions through hydrophilic-lipophilic balance. Surface morphological analysis shows significantly shallow craters on the machined surface showing evidence of effective flushing through a modified dielectric (S-20) as compared to a kerosene-based dielectric. A thorough microscopical, statistical, and scanning electron-based analysis is carried out to explain the process and correlate significant improvements.

Uncertain seismic response and robustness analysis of isolated continuous girder bridges

When calculating the seismic response of structures, conventional methods often neglect uncertainty and suffer from excessive computation. Although robustness metrics have been effective in evaluating structural safety, they are not suitable for isolated continuous girder bridges (ICGB). To address this issue, this paper introduces an uncertain seismic response and robustness assessment method tailored to ICGB: Firstly, the method considers uncertainty in material parameters and ground motion by constructing a data set using Monte Carlo simulation. Secondly, a genetic algorithm for parallel optimisation of radial basis function neural network (GAPO-RBFNN) is trained to predict the seismic responses of the bridges. Thirdly, a robustness metric specifically designed for bridges is derived and applied to bridge. Finally, the proposed robustness metric is validated on lead rubber bearing (LRB) and shape memory alloy-lead rubber bearing (SMA-LRB) bridges. Results indicate that the GAPO-RBFNN model accurately approximates the mapping relationship between structural parameters and seismic response, with an average error of only 0.32% and a 70% reduction in computation time compared with traditional methods. Moreover, the new robustness metric overcomes the limitations of the dismantled member method and is applicable to bridge. The robustness of the SMA-LRB-ICGB, strengthened by SMA, is improved, providing evidence of the effectiveness of the proposed robustness metric.

Signatures of magnetism control by flow of angular momentum

Exploring new strategies to manipulate the order parameter of magnetic materials by electrical means is of great importance not only for advancing our understanding of fundamental magnetism but also for unlocking potential applications. A well-established concept uses gate voltages to control magnetic properties by modulating the carrier population in a capacitor structure1–5. Here we show that, in Pt/Al/Fe/GaAs(001) multilayers, the application of an in-plane charge current in Pt leads to a shift in the ferromagnetic resonance field depending on the microwave frequency when the Fe film is sufficiently thin. The experimental observation is interpreted as a current-induced modification of the magnetocrystalline anisotropy ΔHA of Fe. We show that (1) ΔHA decreases with increasing Fe film thickness and is connected to the damping-like torque; and (2) ΔHA depends not only on the polarity of charge current but also on the magnetization direction, that is, ΔHA has an opposite sign when the magnetization direction is reversed. The symmetry of the modification is consistent with a current-induced spin6–8 and/or orbit9–13 accumulation, which, respectively, act on the spin and/or orbit component of the magnetization. In this study, as Pt is regarded as a typical spin current source6,14, the spin current can play a dominant part. The control of magnetism by a spin current results from the modified exchange splitting of the majority and minority spin bands, providing functionality that was previously unknown and could be useful in advanced spintronic devices.

Love wave propagation in a smart composite structure of linear and exponential functionally graded porous piezoelectric material

Functionally graded materials (FGMs) have emerged as a promising avenue for enhancing the performance and longevity of surface acoustic wave (SAW) devices. This importance is gained due to gradual variation in functional properties of FGMs. In this paper, a mathematical model of a piezoelectric layer overlying a functionally graded porous piezoelectric (FGPP) layer resting on the elastic substrate is presented for the study of Love waves. The material parameters of the FGPP layer are considered to vary linearly and exponentially with the thickness of the layer. The solutions of governing equations corresponding to the FGPP layer are obtained by the Wentzel Kramers Brillouin (WKB) method. Dispersion equations are derived for both electrically open and shorted boundaries, linear and exponential gradation in both mechanical and electrical parameters, and for gradation in mechanical parameters alone as well. After numerical computation, dispersion curves are plotted to investigate the influence of wavenumber, type of gradation, extent of gradation, and type of boundaries on the phase and group velocity. The phase velocity decreases with wavenumber and is found more for linear gradation in comparison to exponential gradation. The electromechanical coupling factor is analyzed for different propagating modes of Love waves, in the case when gradation in mechanical properties is considered, the electromechanical coupling factor is greater than that when variation in both mechanical and electrical properties is considered. It is also observed that the tailoring of gradation can help to get the suitable value of the electromechanical coupling factor. The insights obtained from these findings can offer valuable contributions toward advancing the functionality and efficiency of SAW devices, there by bolstering their applicability in various technological domains.

Parametric Analysis of Critical Buckling in Composite Laminate Structures under Mechanical and Thermal Loads: A Finite Element and Machine Learning Approach.

This research focuses on investigating the buckling strength of thin-walled composite structures featuring various shapes of holes, laminates, and composite materials. A parametric study is conducted to optimize and identify the most suitable combination of material and structural parameters, ensuring the resilience of structure under both mechanical and thermal loads. Initially, a numerical approach employing the finite element method is used to design the C-section thin-walled composite structure. Later, various structural and material parameters like spacing ratio, opening ratio, hole shape, fiber orientation, and laminate sequence are systematically varied. Subsequently, simulation data from numerous cases are utilized to identify the best parameter combination using machine learning algorithms. Various ML techniques such as linear regression, lasso regression, decision tree, random forest, and gradient boosting are employed to assess their accuracy in comparison with finite element results. As a result, the simulation model showcases the variation in critical buckling load when altering the structural and material properties. Additionally, the machine learning models successfully predict the optimal critical buckling load under mechanical and thermal loading conditions. In summary, this paper delves into the study of the stability of C-section thin-walled composite structures with holes under mechanical and thermal loading conditions using finite element analysis and machine learning studies.

Material parameters affecting Li plating in Si/graphite composite electrodes

Silicon is a frequently used active material in the negative electrode of lithium-ion batteries which provides significant improvements in the energy density. Due to large volume changes during cycling, it is typically mixed with graphite. Understanding the interactions of composite materials during battery operation is key to optimizing battery performance and predicting aging phenomena. Even though lithium plating is a critical degradation process, which influences both lifetime and safety, a systematic analysis of electrochemical material parameters affecting plating in composite electrodes is missing. Therefore, a parameter study is performed using 3D microstructure-resolved simulations. We investigate the influence of critical material parameters such as open-circuit potential, electronic conductivity, chemical diffusion coefficient, and exchange current density in simple and interpretable model geometries. The simulations reveal the importance of chemical diffusion and exchange current density, as well as their ratio, in predicting plating. Finally, we apply the set of material parameters to a complex electrode geometry reconstructed from FIB/SEM tomography data of commercial anode material. The analysis shows that preferential plating on the electrode surface dominates all other factors.

The change in thermal-induced interchain distance boosts the gas barrier property of PEI-derived hollow fiber membranes

In order to fulfill diverse special applications (e.g. food packaging, anticorrosion coatings, flame retardant coatings, biomedical industry and lighter-than air applications), the superior gas barrier property is principal requirement. Based on previous researches, the gas barrier property of membranes could be improved to some extent by changing their component, chemical structure, physical property, fabrication parameters or incorporating impermeable fillers. The above-mentioned approaches provide the enhanced gas barrier property owing to increment in resistance for diffusion of tested gas. That implies the intrinsic factors of the membrane such as free volume fraction of membrane are determined effect on permeability to gas. Accordingly, present work investigated the impact of thermal treatment on enhancement of gas barrier property by manipulating the temperature in the range of 200 °C to 240 °C to adjust the orientation of molecular structure. Based on a series of analysis as well as pure gas measurement, the hydrogen permeability of as-treated membrane decreased by almost 2700 times than that of neat membrane, which is due to the absence of amorphousity. The exceptional gas barrier properties of resultant membrane exhibited great potential for special applications and paved the feasible way for other amorphous precursor to prepare the gas barrier membrane.

Creep reliability assessment of structural components at elevated temperatures considering the time dependent feature of representative stress

Creep reliability assessment of structural components at elevated temperatures is essential to guarantee the long-term safe operation of the system. Current studies are limited to continuum damage mechanics methods at the material level, while the reliability assessment method for creep design at the component level is rarely reported. In this work, the framework for creep reliability assessment of structural components is extended, where the time dependent feature of the representative stress is included. The effect of the time dependent feature of the representative stress on creep reliability assessment is discussed. Sensitivity analyses of material parameters on creep reliability assessment results are conducted based on the Sobol and Morris global methods. Results indicate that for the same creep design life, the component presents a higher failure probability when the time dependent feature of the representative stress is considered. Parameters D and d in the creep rupture life equation have more significant effects on creep rupture life than other parameters for the case studied.

Flow and heat transfer of non-miscible micropolar and Newtonian fluid in porous channel sandwiched between parallel plates

An analytical approach has been considered to inspect the flow and heat transfer of immiscible Newtonian and micropolar fluid in a porous medium. This study aims to elucidate the Darcy effect on the model in which isotropic porous regions with different permeability are used. The model of the current problem explains that the flow region is divided into two regions: Newtonian fluid flows in the upper layer, and micropolar fluid flows in the lower layer. Different constant temperatures imposed at boundary walls, heat transfer does not affect the pressure gradient. The governing equations are solved analytically by applying a linear differential equation (LDE). The effect of associated physical parameters such as material parameter, Darcy number, Eckert number, Prandtl number, and viscosity ratio on velocity, micro-rotation, heat transfer, flow rate, heat transfer rate, wall shear stress, and Nusselt number have been inspected. The most significant finding of this research work is that increasing the Darcy number signifies enhanced permeability, resulting in a higher flow rate. The heat transfer rate at the top occurs maximum when the material parameter’s range is minimum while raising the viscosity ratio leads to an increasing heat transfer rate at the bottom. Enhancement in material parameter influences Nusselt number and decreases in nature. The findings of our study are verified with the previously established results. The present work has a setup that is useful in petroleum extraction, transport problems in reservoir rock of an oil field, improving nutrient transport and thermal regulation in tissue engineering, and designing more efficient drug delivery systems and biomedical devices.

Acoustic capsule: A structure that can carry different objects but obtain the same acoustic radiation force

An acoustic capsule is designed utilizing equivalent anisotropy of density and of sound speed. The designed capsule can carry any object with dimensions smaller than those of its inner cavity. The capsule achieves internal shielding of the sound field, thus ensuring that the capsule and its contents experience the same acoustic radiation force (ARF), regardless of any changes in the contents. An acoustic capsule design method is proposed and its feasibility is verified using a combination of theoretical analysis and finite element simulations. Additionally, optimization strategies to enhance the ARF and enable selection of appropriate material parameters are discussed, and these strategies pave the way toward practical applications of the ARF in life sciences and other fields.

Magnetic interactions in chromium adsorption on silicene

Silicon is a material with well established technological applications. Thus, obtaining this material in nanostructured form increases its possibility of integration in the current technology. Silicene, a two-dimensional allotrope of silicon, has a natural compatibility with current silicon-based technology. In this work we use density-functional theory to identify the electronic and magnetic properties of chromium atoms on monolayer and bilayer silicene. We investigate several properties of chromium in silicene, such as dopant solubility limits, site preference for adsorption and charge transfer. We find that magnetization depends on key parameters such as buckling, interlayer distance and adsorption site. Chromium intercalated in silicene bilayers showed a very interesting relation between the buckling of silicene and the total magnetic moment. The larger is the buckling the smaller is the magnetic moment. Our results demonstrate that it is possible to manipulate the magnetic properties of silicene by changing chromium concentration, adsorption site and structural parameters of the host material.

Stretchable Metamaterials with Tunable Infrared Emissivity for Dynamic Thermal Management.

Manipulation of infrared emissivity, which is closely related to surface structure and optical parameters of materials, is a crucial approach for realizing dynamic thermal management. In this study, we design a metamaterial consisting of an array of aluminum disks embedded on a surface of a stretchable elastomeric substrate. Mechanical stretching-induced deformation allows dynamic modification of the surface structure and equivalent optical parameters, thus enabling dynamic control of the emissivity. By utilizing the elastomer polydimethylsiloxane (PDMS) as the substrate, the microstructure interdisk gap can be altered by stretching the PDMS. Through theoretical calculations, the plausibility of this approach is explained by the excitation of plasmon resonance and the variation in the exposed area of highly absorbent PDMS, and the optimal structures for tuning the infrared emissivity are revealed to be 6 μm in diameter and 100 nm in height. Based on this design, we prepare samples with periods of 7 and 7.9 μm and experimentally demonstrate that a change in the period can cause a change in the emissivity and thus tunability in thermal control performance. The temperature difference between the two samples reaches 44.1 °C at a heating power of 0.28 W/cm2 for both samples. Furthermore, we construct a stretching platform that enables in situ mechanical stretching to realize dynamic changes in emissivity. The integral infrared emissivity of the sample increases from 0.32 to 0.5 at a biaxial tensile strain of 13%, achieving a 56% modulation rate of the integral infrared emissivity. The material is expected to enable dynamic thermal management.