Macroscopic Conductivity Research Articles

The Impact of Nanoscale Percolation in Yttrium‐Doped BaZrO3 on the Oxygen Ion and Proton Conductivities: A Density Functional Theory and Kinetic Monte Carlo Study

Yttrium‐doped BaZrO3 is known for its high proton conductivity, making it an advanced energy material for various applications, like electrolyzers, fuel cells, or methane conversion cells. In the previous study, density functional theory (DFT) and kinetic Monte Carlo simulations (KMC) show that nanoscale percolation of Y ions enhances the proton mobility by providing fast migration pathways for protons. To investigate the general impact of nanoscale percolation on ionic conductivities, the same methods, DFT and KMC, are now used to calculate oxygen ion conductivity of Y‐doped BaZrO3. The results explain on a microscopic level why the macroscopic oxygen ion conductivity exhibits a higher activation energy than the proton conductivity, as known experimentally. They also show that nanoscale percolation pathways are not beneficial for oxygen ion conduction due to trapping of oxygen vacancies by single Y ions and blocking of their motion by nanoscale percolation of Y ions. Finally, the understanding and comparison of the microscopic jump processes of oxygen vacancies and of protons in acceptor‐doped BaZrO3 are used to propose a simple descriptor for the influence of a dopant on the ionic conductivities, of oxygen ions and of protons as well. This new descriptor allows an easy screening of various dopants.

Ephaptic Coupling Is a Mechanism of Conduction Reserve During Reduced Gap Junction Coupling.

Many cardiac pathologies are associated with reduced gap junction (GJ) coupling, an important modulator of cardiac conduction velocity (CV). However, the relationship between phenotype and functional expression of the connexin GJ family of proteins is controversial. For example, a 50% reduction of GJ coupling has been shown to have little impact on myocardial CV due to a concept known as conduction reserve. This can be explained by the ephaptic coupling (EpC) theory whereby conduction is maintained by a combination of low GJ coupling and increased electrical fields generated in the sodium channel rich clefts between neighboring myocytes. At the same time, low GJ coupling may also increase intracellular charge accumulation within myocytes, resulting in a faster transmembrane potential rate of change during depolarization (dV/dt_max) that maintains macroscopic conduction. To provide insight into the prevalence of these two phenomena during pathological conditions, we investigated the relationship between EpC and charge accumulation within the setting of GJ remodeling using multicellular simulations and companion perfused mouse heart experiments. Conduction along a fiber of myocardial cells was simulated for a range of GJ conditions. The model incorporated intercellular variations, including GJ coupling conductance and distribution, cell-to-cell separation in the intercalated disc (perinexal width—WP), and variations in sodium channel distribution. Perfused heart studies having conditions analogous to those of the simulations were performed using wild type mice and mice heterozygous null for the connexin gene Gja1. With insight from simulations, the relative contributions of EpC and charge accumulation on action potential parameters and conduction velocities were analyzed. Both simulation and experimental results support a common conclusion that low GJ coupling decreases and narrowing WP increases the rate of the AP upstroke when sodium channels are densely expressed at the ends of myocytes, indicating that conduction reserve is more dependent on EpC than charge accumulation during GJ uncoupling.

Stochastic integrated machine learning based multiscale approach for the prediction of the thermal conductivity in carbon nanotube reinforced polymeric composites

We present a stochastic integrated machine learning based multiscale approach for the prediction of the macroscopic thermal conductivity in carbon nanotube reinforced polymeric composites (CNT-PCs). Seven types of machine learning models are exploited, namely Multivariate Adaptive Regression Splines (MARS), Support Vector Machine (SVM), Regression Tree (RT), Bagging Tree (Bag), Random Forest (RF), Gradient Boosting Machine (GBM) and Cubist. They are used as components of stochastic modeling constructing the relationship between all uncertain inputs variables and the output of interest, the macroscopic thermal conductivity of the composite. Particle Swarm Optimization (PSO) is used for hyper-parameter tuning to find the global optimal values leading to a significant reduction in the computational cost. We also analyze the advantages and disadvantages of various methods in terms of computational expense and model complexity. We believe that the presented stochastic integrated machine learning approach accounting for uncertainties is a valuable step towards computational design of new composites for application related to thermal management.

Microchannel insulating foams comprising a multifunctional epoxy/graphene‐nanoplatelet nanocomposite

AbstractGraphene nanomaterials have demonstrated simultaneous enhancement of both the thermal and mechanical properties of many base polymer systems, but for application as a reinforcement in multifunctional thermally insulating polymer foam the graphene additive must not significantly increase the macroscopic thermal conductivity. In an effort to study and decouple these mechanical and thermal properties, low loadings of reduced graphene oxide nanoplatelets have been added into epoxy formulations to comprise the matrix structure of hollow microchannel nanocomposite foams. Substantial improvements of 226% were achieved in the microchannel foam specific flexural modulus at only 0.15% graphene nanoplatelet loading without compromising the foam flexural strength and while also maintaining the low thermal conductivity of the baseline epoxy foam material. These results support the use of these nanocomposite foams as mechanically reinforced thermal insulation, with the addition of the graphene nanoplatelets potentially providing additional multifunctional improvements to the foam such as reduced UV‐radiation transmittance, improved electrical surface conductivity for diminished static charge buildup, and/or lowering of the coefficient of thermal expansion for enhanced structural stability in extreme environments. This particular combination of multifunctional properties makes these materials well suited as high‐performance structural thermal insulation materials in support of next generation space system applications.

A Mixed Wavelet-Learning Method of Predicting Macroscopic Effective Heat Transfer Conductivities of Braided Composite Materials

A Mixed Wavelet-Learning Method of Predicting Macroscopic Effective Heat Transfer Conductivities of Braided Composite Materials

Micromechanical Analysis of Thermal and Electro-Magnetic Properties of a Composite Material Containing Misoriented Reinforcements

Modified equivalent inclusion method is applied to a composite material containing various misoriented reinforcements. The interaction thermal flux induced in the composite and the total thermal gradient of the composite can be expressed as a function of orientation angle of reinforcements. The distribution function is devised to be continuous change from two-dimensional distribution to unidirectional one of reinforcements. By using the distribution function, macroscopic thermal conductivity λ are obtained. Combined with the previous results on macroscopic coefficient of linear thermal expansion α, the Relationship between λ and α is obtained.

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The safe operation of power equipment largely depends on the overvoltage protection level of the arrester. The ZnO varistors are widely used as the core components of the arresters in power systems because of the excellent nonlinear volt-ampere characteristics. In order to study the electrical properties of ZnO varistors under different external electric fields from the microstructure, the method of first-principles based on density functional theory (DFT) is used, and structure of ZnO/<i>β</i>-Bi<sub>2</sub>O<sub>3</sub> interface containing zinc interstitial (Zn<sub>i</sub>) and oxygen vacancy (V<sub>o</sub>) defects is built. The results show that the V<sub>o</sub> defect migrates after full relaxation. The Zn<sub>i</sub> shifts to the interface under an external electric field. The interface energy increases rapidly after the electric field intensity has exceeded 0.1 V/Å, which means that the interaction force between the interfaces becomes larger, the distance between ZnO and <i>β</i>-Bi<sub>2</sub>O<sub>3</sub> layers decreases, and the conductivity increases rapidly. The differential charge density, work function and Bader charge analysis method are used to calculate the barrier height at the interface, which proves that the built-in electric field is an important cause ingredient responsible for the non-linear volt-ampere characteristics of ZnO varistors. The effects of atomic orbital energy level, trap energy level and energy gap on the macroscopic conductivity of ZnO varistors are analyzed by using the method of density of states analysis. In this work are analyzed the different electrical parameters of the ZnO/<i>β</i>-Bi<sub>2</sub>O<sub>3</sub> interface with aggregation defects by adjusting the intensity of the external electric field, and a new idea is provided for learning the electrical characteristics of ZnO varistors.

Effects of Microscopic Properties on Macroscopic Thermal Conductivity for Convective Heat Transfer in Porous Materials.

Porous materials are widely used in many heat transfer applications. Modeling porous materials at the microscopic level can accurately incorporate the detailed structure and substance parameters and thus provides valuable information for the complex heat transfer processes in such media. In this study, we use the generalized periodic boundary condition for pore-scale simulations of thermal flows in porous materials. A two-dimensional porous model consisting of circular solid domains is considered, and comprehensive simulations are performed to study the influences on macroscopic thermal conductivity from several microscopic system parameters, including the porosity, Reynolds number, and periodic unit aspect ratio and the thermal conductance at the solid–fluid interface. Our results show that, even at the same porosity and Reynolds number, the aspect ratio of the periodic unit and the interfacial thermal conductance can significantly affect the macroscopic thermal behaviors of porous materials. Qualitative analysis is also provided to relate the apparent thermal conductivity to the complex flow and temperature distributions in the microscopic porous structure. The method, findings and discussions presented in this paper could be useful for fundamental studies, material development, and engineering applications of porous thermal flow systems.

Continuum micromechanics model for fired clay bricks: Upscaling of experimentally identified microstructural features to macroscopic elastic stiffness and thermal conductivity

Quantification of elastic stiffness and thermal conductivity of fired clay bricks is still often limited to empirical rules and laboratory testing, which becomes progressively more challenging given the large variety of raw materials used to optimize the properties of modern brick products. Applying a continuum micromechanics multiscale approach, we herein aim at upscaling of microstructural features to quantify the bricks’ macroscopic properties. Microstructural features such as assemblage and morphometry of mineral phases (quartz, feldspar, and micas), of pores, and of the binding matrix phase, respectively, as well as thermoelastic phase properties are provided by recently published results from extensive microscopic testing including electron microscopy imaging, mercury intrusion porosimetry, nanoindentation, and scanning thermal microscopy. These results are incorporated into the micromechanics model by introducing spheroidal phases with characteristic orientation distribution at two observation scales. The homogenized macroscopic stiffness and conductivity agree very well with independent results from novel macroscopic tests for all seven studied brick compositions. This corroborates the microstructure-informed multiscale model approach and its assumptions: the linear increase of the binding matrix properties with the material’s carbonate content, and the inability of large quartz with interface cracks to take over any mechanical loads.

Nonhomogeneous volume conduction effects affecting needle electromyography: an analytical and simulation study

Objective. Needle electromyography (EMG) is used to study the electrical behavior of myofiber properties in patients with neuromuscular disorders. However, due to the complexity of electrical potential spatial propagation in nonhomogeneous diseased muscle, a comprehensive understanding of volume conduction effects remains elusive. Here, we develop a framework to study the conduction effect of extracellular abnormalities and electrode positioning on extracellular local field potential (LFP) recordings. Methods. The framework describes the macroscopic conduction of electrical potential in an isotropic, nonhomogeneous (i.e. two tissue) model. Numerical and finite element model simulations are provided to study the conduction effect in prototypical monopolar EMG measurements. Results. LFPs recorded are influenced in amplitude, phase and duration by the electrode position in regards to the vicinity of tissue with different electrical properties. Conclusion. The framework reveals the influence of multiple mechanisms affecting LFPs including changes in the distance between the source–electrode and tissue electrical properties. Clinical significance. Our modeled predictions may lead to new ways for interpreting volume conduction effects on recorded EMG activity, for example in neuromuscular diseases that cause structural and compositional changes in muscle tissue. These change will manifest itself by changing the electric properties of the conductor media and will impact recorded potentials in the area of affected tissue.

Electrical and mechanical behaviour of metal thin films with deformation-induced cracks predicted by computational homogenisation

Motivated by advances in flexible electronic technologies and by the endeavour to develop non-destructive testing methods, this article analyses the capability of computational multiscale formulations to predict the influence of microscale cracks on effective macroscopic electrical and mechanical material properties. To this end, thin metal films under mechanical load are experimentally analysed by using in-situ confocal laser scanning microscopy (CLSM) and in-situ four point probe resistance measurements. Image processing techniques are then used to generate representative volume elements from the laser intensity images. These discrete representations of the crack pattern at the microscale serve as the basis for the calculation of effective macroscopic electrical conductivity and mechanical stiffness tensors by means of computational homogenisation approaches. A comparison of simulation results with experimental electrical resistance measurements and a detailed study of fundamental numerical properties demonstrates the applicability of the proposed approach. In particular, the (numerical) errors that are induced by the representative volume element size and by the finite element discretisation are studied, and the influence of the filter that is used in the generation process of the representative volume element is analysed.

A continuum-molecular model for anisotropic electrically conductive materials

An anisotropic model for two-dimensional electrical conduction, elasticity and fracture is proposed in the peridynamic theoretical framework. Material points interact through elastic non-central pair potentials and inelastic pair potential functions of pairwise elastic and inelastic deformation measure, allowing to obtain a bond-based type model for conductive Cauchy orthotropic media without restrictions in the number of independent material constants. The elasticity of pair interactions can be described mechanistically by equivalent normal and shear springs, whose stiffness varies continuously with the spatial orientation of the ligament and, preserving the elastic symmetries of the material, depends on four material parameters defining in-plane orthotropic classical elasticity. The macroscopic anisotropic conductivity is described instead by continuous functions of the micro-conductive properties of the interparticles interactions. Moreover, non-uniform material toughness is modeled adopting an anisotropic energetic failure criterion related to direction-dependent fracture energy functionals. The accuracy of the proposed model has been assessed by several problems including the anisotropic electrical conduction in multi-phases laminae with a central hole and evolving cracks, and the fracture and damage sensing in cortical bone considering different orientation of the material reference system.

Recent Advances in Molecular Design of Organic Thermoelectric Materials

Recent Advances in Molecular Design of Organic Thermoelectric Materials

Multi-Scale Numerical Modelling for Predicting Thermo-Physical Properties of Phase-Change Nanocomposites for Cooling Energy Storage

One major limitation of phase-change materials (PCM) for thermal energy storage comes from their poor thermal conductivity hindering heat transfer process and power density. Nanocomposites PCMs, where highly conductive nanofillers are dispersed into PCM matrices, have been exploited in the past decades as novel latent heat storage materials with enhanced thermal conductivity. A computational model based on continuum simulations capable to link microscopic characteristics of nanofillers and the bulk PCM with the macroscopic effective thermal conductivity of the resulting nanocomposite is the aim of this work. After preliminary mean-field simulations investigating the impact of the nanofiller aspect ratio on the thermal conductivity of the nanocomposite, finite element simulations at reduced aspect ratios have been performed with corrected thermal conductivity values of the filler, to take into account the thermal interface resistances between fillers and matrix. Finally, the thermal conductivity at the actual aspect ratios has been extrapolated by the results obtained at reduced aspect ratios thus saving computational time and meshing efforts. This method has been validated through comparison against previous literature evidence and new experimental characterizations of nanocomposite PCMs.

Anisotropic transport properties of graphene-based conductor materials

We analyzed nanographite-based materials in a combined study including experimental analysis via 4-point probe STM and simulation to provide a complete picture of microscopic and macroscopic properties of the material. The two- and three-dimensional transport regimes were determined and evaluated regarding the anisotropy of the conductivity. The experimental results yield the full macroscopic conductivity tensor. Microstructural simulations are used to map those macroscopic properties to the microscopic building blocks of the sample. By combining those two, we present a coherent and comprehensive description of the electrical material parameters across several length scales.

A stochastic multiscale method for the prediction of the thermal conductivity of Polymer nanocomposites through hybrid machine learning algorithms

In this paper, we propose a hybrid machine learning method to predict the thermal conductivity of polymeric nanocomposites (PNCs). Therefore, a combination of artificial neural network (ANN) and particle swarm optimization (PSO) is applied to estimate the relationship between variable input and output parameters. The ANN is used for modeling the composite while PSO improves the prediction performance through an optimized global minimum search. We select the thermal conductivity of the fibers and the matrix, the kapitza resistance, volume fraction and aspect ratio as input parameters. The output is the macroscopic (homogenized) thermal conductivity of the composite. The results show that the PSO significantly improves the predictive ability of this hybrid intelligent algorithm, which outperforms traditional neural networks.

Effect of the Agglomerate Geometry on the Effective Electrical Conductivity of a Porous Electrode.

The study of the microstructure of random heterogeneous materials, related to an electrochemical device, is relevant because their effective macroscopic properties, e.g., electrical or proton conductivity, are a function of their effective transport coefficients (ETC). The magnitude of ETC depends on the distribution and properties of the material phase. In this work, an algorithm is developed to generate stochastic two-phase (binary) image configurations with multiple geometries and polydispersed particle sizes. The recognizable geometry in the images is represented by the white phase dispersed and characterized by statistical descriptors (two-point and line-path correlation functions). Percolation is obtained for the geometries by identifying an infinite cluster to guarantee the connection between the edges of the microstructures. Finally, the finite volume method is used to determine the ETC. Agglomerate phase results show that the geometry with the highest local current distribution is the triangular geometry. In the matrix phase, the most significant results are obtained by circular geometry, while the lowest is obtained by the 3-sided polygon. The proposed methodology allows to establish criteria based on percolation and surface fraction to assure effective electrical conduction according to their geometric distribution; results provide an insight for the microstructure development with high projection to be used to improve the electrode of a Membrane Electrode Assembly (MEA).

Thermal and electrical characterization of poly(vinyl)alcohol)/poly(vinylidene fluoride) blends reinforced with nano-graphene platelets

Nano-graphene/polymer composites can function as pressure induced electro-switches, at concentrations around their conductivity percolation threshold. Close to the critical point, the pressure dependence of the electron tunneling through the polymer barrier separating nano-graphene's results from the competition among shortening of the tunneling length and the increase of the polymer's polarizability. Such switching behavior was recently observed in polyvinyl alcohol (PVA) loaded with nano-graphene platelets (NGPs). In this work, PVA is blended with α-poly(vinylidene fluoride) (PVdF) and NGPs. Coaxial mechanical stress and electric field render the nano-composite piezoelectric. We investigate the influence of heterogeneity, thermal properties, phase transitions and kinetic processes occurring in the polymer matrix on the macroscopic electrical conductivity and interfacial polarization in casted specimens. Furthermore, the effect of electro-activity of PVdF grains on the electric and thermal properties are comparatively studied. Broadband Dielectric spectroscopy is employed to resolve and inspect electron transport and trapping with respect to thermal transitions and kinetic processes traced via Differential Scanning Calorimetry. The harmonic electric field applied during a BDS sweep induces volume modifications of the electro-active PVdF grains, while, electro-activity of PVdF grains can disturb the internal electric field that free (or bound) electric. The dc conductivity and dielectric relaxation was found to exhibit weak dependencies.

Generalized Boltzmann transport theory for relaxational heat conduction

Relaxational heat conduction lacks a modified transport theory at the mesoscopic level. We establish a modified Boltzmann transport theory with the generalized collision term, which can give rise to the convolution relationship between the heat flux and temperature gradient as well as fractional Fourier law. The macroscopic relaxational behaviors are thereafter connected to mesoscopic memory effects in the generalized collision term. The modified Boltzmann transport theory not only provides an underlying explanation for macroscopic relaxational heat conduction but also possesses engineering applications to situations far from equilibrium. The generalized collision term is not unique framework for relaxational heat conduction, and generalizing the drift term in the Boltzmann transport equation (BTE) can also cover macroscopic models. However, this framework will be paired with anomalies in energy continuity and entropy balance, and as a consequence, it is not suggested for relaxational heat conduction.

Effects of general imperfect interface/interphase on the in-plane conductivity of thermal composites

The effects of general imperfect interphase/interface models on effective and local thermal conductive responses of fiber reinforced-composites are investigated. A physical interphase model is firstly introduced and then extended to the weakly conducting (WC) and highly conducting (HC) interfaces by satisfying the discontinuity conditions of temperature and normal heat flux, respectively. To simulate different geometric arrangements of fibers, hexagonal and square repeating unit cells (RUCs) are used in this paper. Regarding simulation technique, the locally-exact homogenization theory (LEHT) is extended by considering the aforementioned interphase/interface models to re-produce the localized temperature and heat flux distributions in the unit cells and then to predict the macroscopic thermal conductivities. The present method does not require mesh discretization and pre-processing, but directly obtains the accurate analytical expression of internal domains after solving the thermal differential equations. The unknown coefficients are obtained through applying imperfect interphase/interface conditions and weak-form periodic boundary conditions. Finally, the effective Fourier's formula is established to obtain the overall thermal conductivities of composites. The LEHT is advantageous in fast convergence, numerical stability and computational efficiency, thus providing a good numerical tool in studying the influence of interface parameters on composites. The generated numerical results are in good agreement with the analytical or numerical simulations in the literature. On this basis, the effects of interfacial geometric and physical parameters on the macroscopic coefficients and local thermal responses of the composites are tested. Finally, the thickness of a physical interphase is varied to test its applicability in generating equivalent effect with WC interface and HC interface. Generally, the imperfect interfacial effects play important roles in the thermal conductive response of composite materials. In the meantime, the proposed method is of great significance for the study of thermal conductions of composite microstructures.