Microstructure Of Porous Materials Research Articles

Predicting the effect of microstructural variations in porous sound absorbers on their sound absorption performance using the perturbation method

Noise reduction is one of the most important issues in various industries. Quantifying the variation in acoustic properties is an essential task to improve the quality of products. Porous sound-absorbing materials are commonly applied to reduce noise. Variations in the microstructure of porous materials have a significant impact on variations of sound absorption coefficient. Experimental methods have been used to predict manufacturing variability of sound-absorbing porous materials. However, they take a lot of time and cost. In this study, a perturbation method is applied to predict variations of sound absorption coefficient of sound-absorbing porous materials combined with the homogenization method based on an asymptotic expansion. Properties of microstructure of a sound-absorbing porous material, such as fiber diameter and cell size, are assumed to follow normal distribution and identify the expectation and variance from the observation of microscopic images obtained by scanning electron microscope. Calculated variability of sound absorption coefficient is compared with measured variability and they agree well in the frequency range of interest.

Establishment of the microstructure of porous materials and its relationship with effective mechanical properties

In this study, a porous structure for a porous liquid storage medium is generated, and the homogenization theory based on displacement boundary conditions is used to predict the effective mechanical properties. The relationship between the porous material’s macroscopic mechanical properties and microstructure is next analyzed. In order to establish the relationship between the microstructure of porous materials and their macroscopic mechanical properties, assuming that the pores grow along the z direction, a method is proposed to generate 3D open-cell porous materials based on six design parameters (i.e., the number of pores, porosity, irregularity of pore distribution, the randomness of pore growth in the x and y directions, and randomness of pore size). Since the porosity of oil-bearing materials ranges from 20 to 30%, the porosity of the RVE (Representative Volume Element) was kept under control at about 25%, and the effect of the six design factors on the mechanical properties of the RVE was investigated. Utilizing SLA 3D printing technology, specimens were produced, and compression tests were used to show how useful the results of the numerical analysis were. The results demonstrated that after the number of RVE pores reaches 9, the numerical results have good repeatability. The irregularity of the initial pore distribution has little effect on the effective mechanical properties of the RVE. At the same time, the increase in the randomness of pore growth and the randomness of pore size increases the degree of weakening of the mechanical properties in the z-direction, while reducing the degree of weakening in the x and y directions, but the latter has a smaller impact. Furthermore, there is a superimposition effect of design parameters on the RVE.

A cellular automata framework for porous electrode reconstruction and reaction-diffusion simulation

Due to the high charge transfer efficiency compared to that of non-porous materials, porous electrodes with larger surface area and thinner solid pore walls have been widely applied in the lithium-ion battery field. Since the capacity and charge-discharge efficiency of batteries are closely related to the microstructure of porous materials, a conceptually simple and computationally efficient cellular automata (CA) framework is proposed to reconstruct the porous electrode structure and simulate the reaction-diffusion process under the irregular solid-liquid boundary in this work. This framework is consisted of an electrode generating model and a reaction-diffusion model. Electrode structures with specific geometric properties, i.e., porosity, surface area, size distribution, and eccentricity distribution can be constructed by the electrode generating model. The reaction-diffusion model is exemplified by solving the Fick’s diffusion problem and simulating the cyclic voltammetry (CV) process. The discharging process in the lithium-ion battery are simulated through combining the above two CA models, and the simulation results are consistent with the well-known pseudo-two-dimensional (P2D) model. In addition, a set of electrodes with different microstructures are constructed and their reaction efficiencies are evaluated. The results indicate that there is an optimum combination of porosity and particle size for discharge efficiency. This framework is a promising one for studying the effect of electrode microstructure on battery performance due to its fully synchronous computation way, easy handled boundary conditions, and free of convergence concerns.

Reconstruction of 3D Random Media from 2D Images: Generative Adversarial Learning Approach

This paper presents an algorithm for stochastic reconstruction of three-dimensional material microstructure from its single two-dimensional cross-sectional image, based on the neural network operating on a principle of generative adversarial learning. The novelty of the proposed algorithm is in introduction of the reconstruction error, which is invariant to translational and rotational transformations and increases stability of the neural-network training and quality of generated structures. It is shown that a use of variational autoencoder helps to extract useful features from a cross-sectional image and provide additional information to a generator for accurate structure reconstruction. A set of 3D microstructures with corresponding 2D slice from each of them is required for model training. The model was trained and tested on sets of binary microstructures of porous materials with open-cell and closed-cell internal morphology. The obtained results for statistical evaluation of material microstructure demonstrate the effectiveness of the proposed algorithm.

Improvement and Optimization of a 3D Reconstruction Algorithm for SEM Images of Porous Materials

Porous materials have become increasingly common in people’s daily lives as the industry has advanced. Porous materials have numerous applications in the petroleum and chemical industries, as well as in everyday life. The study of diffusion, thermal conductivity, and percolation properties of porous materials has an important engineering application background and scientific value. The microstructure of materials affects their properties and attributes, so the description and visualization of the microstructure of porous materials is of great importance in the study of materials science. Due to the specificity of the internal structure of porous materials, many scenarios require 3-dimensional reconstruction of porous materials in practical engineering. In order to improve the effect of 3-dimensional reconstruction of porous materials, a 3D reconstruction method based on the improved generative adversarial neural network (GAN) is proposed in this paper for SEM images of porous materials. First, scanning electron microscope (SEM) images of porous materials are acquired, and then the acquired SEM images are preprocessed, including denoising and determining the boundary. Second, an improved GAN-based image super-resolution reconstruction model (ISRGAN) is used, and then the preprocessed images are fed into the ISRGAN model for training. Thus, multiple intermediate layer images are generated. Third, the 3D reconstruction of the intermediate layer images is performed using the slice combination method. The relationship between the unit cell pixels and the porosity is analyzed in the experiments to verify the effectiveness of the 3D reconstruction method used in this paper, and it is concluded that the porosity tends to be stable when the unit cell pixels converge to 110 and converge to the porosity of the real sample. The experimental results validate the feasibility and effectiveness of the method presented in this paper in the 3D reconstruction process.

Topology optimization of micro structure of porous material to maximize dissipated energy by using homogenization method

Topology optimization of micro structure of porous material to maximize dissipated energy by using homogenization method

Insights into heat management of hydrogen adsorption for improved hydrogen isotope separation of porous materials

Separating high-purity hydrogen isotopes from their mixture still remains a huge challenge due to almost the identical physicochemical properties. Much importance has been attached to tune microstructure of porous materials, while heat management during hydrogen isotope separation tends to be ignored. Herein, a porous material 5A molecular sieve (5A) is mixed with graphene (GE) under ball grinding to enhance its thermal conductivity for hydrogen isotope separation. The thermal conductivity increases from 0.19 W m−1 K−1 of neat 5A, 0.75 W m−1 K−1 of 5A/GE2 (2 wt% GE) to 1.23 W m−1 K−1 of 5A/GE8. In addition, introducing GE into 5A promotes hydrogen adsorption and D2/H2 adsorption ratio. 5A/GE2 shows the highest D2 adsorption capacity (5.40 mmol/g) and the largest D2/H2 adsorption ratio (1.07) among the composites. It also displays a high efficiency of heat transfer that contributes to a low energy consumption due to the shortened cycle time during hydrogen isotope separation. This work offers new insights into material design for improved hydrogen isotope separation, which is greatly crucial to scientific and industrial applications, such as fuel self-sustaining in fusion reactors.

Topology optimization of micro structure of porous material to maximize dissipated energy by using homogenization method

Topology optimization of micro structure of porous material to maximize dissipated energy by using homogenization method

Yield Stress Enhancement of a Ternary Colloidal Suspension via the Addition of Minute Amounts of Sodium Alginate to the Interparticle Capillary Bridges.

Capillary suspensions are ternary solid-liquid-liquid systems produced via the addition of a small amount of secondary fluid to the bulk fluid that contained the dispersed solid particles. The secondary fluid could exert strong capillary forces between the particles and dramatically change the rheological properties of the suspension. So far, research has focused on capillary suspensions that consist of additive-free fluids, whereas capillary suspensions with additives, particularly those of large molecular weight that are highly relevant for industrial purposes, have been relatively less studied. In this study, we performed a systematic analysis of the properties of capillary suspensions that consist of paraffin oil (bulk phase), water (secondary phase), and α-Al2O3 microparticles (particle phase), in which the aqueous secondary phase contained an important eco-friendly polymeric binder, sodium alginate (SA). It was determined that the yield stress of the suspension increased significantly with the increase in the SA content in the aqueous secondary phase, which was attributed to the synergistic effect of the capillary force and hydrogen bonding force that may be related to the increase in the number of capillary bridges. The amounts of SA used to induce a significant change in the yield stress in this study were very small (<0.02% of the total sample volume). The addition of Ca2+ ions to the SA-containing secondary phase further increased the yield stress with possible gelation of the SA chains-in the presence of excess Ca2+ ions, however, the yield stress decreased because of the microscopic phase separation that occurred in the aqueous secondary phase. The microstructures of the sintered porous materials that were produced by using capillary suspensions as precursors were qualitatively well correlated to the rheological behavior of the precursor suspensions, suggesting a new method for the subtle control of the microstructures of porous materials using the addition of minute amounts of polymeric additives.

Pressure Drop and Convective Heat Transfer in Different SiSiC Structures Fabricated by Indirect Additive Manufacturing

Abstract The microstructure of porous materials has a significant effect on their transport properties. Engineered cellular ceramics can be designed to exhibit properties at will, thanks to the advances in additive manufacturing. We investigated the heat and mass transport characteristics of SiSiC lattices produced by three-dimensional (3D) printing and replication, with three different morphologies: rotated cube (RC), Weaire–Phelan (WPh), and tetrakaidecahedron (TK) lattices, and a commercially available ceramic foam. The pressure gradients were measured experimentally for various velocities. The convective heat transfer coefficients were determined through a steady-state experimental technique in combination with numerical analysis. The numerical model was a volume-averaged model based on a local thermal nonequilibrium (LTNE) assumption of the two homogeneous phases. The results showed that for TK and WPh structures, undesirable manufacturing anomalies (specifically window clogging) led to unexpectedly higher pressure drops across the samples and increased thermal dispersion. Compared to the TK and WPh structures the manufactured RC lattice and the random foam had lower heat transfer rates but also lower pressure drops. These lower values for the RC lattice and foam are also a result of their lower specific surface areas.

Topology optimization of micro structure of porous material to maximize dissipated energy by using homogenization method

Topology optimization of micro structure of porous material to maximize dissipated energy by using homogenization method

Topology Optimization of Micro-Structured Materials Featured with the Specific Mechanical Properties

Micro-structured materials consisting of an array of microstructures are engineered to provide the specific material properties. This present work investigates the design of cellular materials under the framework of level set, so as to optimize the topologies and shapes of these porous material microstructures. Firstly, the energy-based homogenization method (EBHM) is applied to evaluate the material effective properties based on the topology of the material cell, where the effective elasticity property is evaluated by the average stress and strain theorems. Secondly, a parametric level set method (PLSM) is employed to optimize the microstructural topology until the specific mechanical properties can be achieved, including the maximum bulk modulus, the maximum shear modulus and their combinations, as well as the negative Poisson’s ratio (NPR). The complicated topological shape optimization of the material microstructure has been equivalent to evolve the sizes of the expansion coefficients in the interpolation of the level set function. Finally, several numerical examples are fully discussed to demonstrate the effectiveness of the developed method. A series of new and interesting material cells with the specific mechanical properties can be found.

Quality guided image recognition towards industrial materials diffusion

Ordered porous materials, especially mesoporous materials, molecular sieves and organometallic framework materials, are widely used in the fields of adsorption, membrane separation and catalytic reaction processes due to their unique properties. Molecular sieve porous material is a new type of engineering material with excellent performance, and its microstructure is one of the key factors affecting macroscopic physical properties and use effect. At present, in order to further broaden its application in the fields of separation, catalysis, sensors and micro-devices, at present, microscopic molecular level control of porous material composition and structure, macroscopically controlling its appearance and appearance has become a research of porous materials. An important development direction. However, the quantitative characterization of the molecular structure of molecular sieve porous materials and its influence on physical properties has always been the focus and difficulty in the field of materials science and engineering. At the same time, the microstructure of porous materials of molecular sieves is the key factor affecting its macroscopic physical properties, and the analysis of spatial structure characteristics. Has important research significance. Based on this paper, a method for predicting effective diffusion coefficient in porous materials by convolutional neural network is proposed. The training samples of porous material microstructure are generated by computer stochastic simulation, and the corresponding effective diffusion coefficients are calculated by finite element method. The image quality subjective evaluation model is used to control the microscopic picture precision of the molecular sieve porous material. The combination of the two methods can quickly and accurately calculate the effective diffusion coefficient.

Permeability of porous ceramics by X-ray CT image analysis

This work presents a method to 1) statistically characterize the complex geometry of porous material microstructures, 2) parameterize these random microstructures into statistically condense, feature rich metrics, and 3) relate these metrics to material properties such as permeability. This method is applied to porous synthetic cordierite and aluminum titanate materials covering a range of 40%–70% porosity and 8–18 μm median pore size (as measured by mercury intrusion porosimetry), using X-ray computed tomography images of the porous microstructure. Direct simulations of the microstructures were made to estimate effective permeability and correlations are presented to microstructural metrics. High quality relationships are established, specifically utilizing 2-point correlation functions and lineal path function characteristic dimensions. Results are compared to legacy methods such as the widely known relationships proposed by Kozeny-Carman and Kuwabara.

Permeability of Porous Ceramics by X-Ray CT Image Analysis

This work presents a method to 1) statistically characterize the complex geometry of porous material microstructures, 2) parameterize these random microstructures into statistically condense, feature rich metrics, and 3) relate these metrics to material properties such as permeability. This method is applied to porous synthetic cordierite and aluminum titanate materials covering a range of 40%-70% porosity and 8-18 μm median pore size (as measured by mercury intrusion porosimetry), using X-ray computed tomography images of the porous microstructure. Direct simulations of the microstructures were made to estimate effective permeability and correlations are presented to microstructural metrics. High quality relationships are established, specifically utilizing 2-point correlation functions and lineal path function characteristic dimensions. Results are compared to legacy methods such as the widely known relationships proposed by Kozeny-Carman and Kuwabara.

Topology optimization of micro structure of porous material to maximize dissipated energy by using homogenization method

Topology optimization of micro structure of porous material to maximize dissipated energy by using homogenization method

A Calculation Theory of Earth Pressure Considering Microstructure of Porous Materials

To explore a more reasonable earth pressure calculation theory, microstructure characteristics of soft clay in Hangzhou, Ningbo and Wenzhou had been observed using scanning electron microscope (SEM) and were quantitatively analysed using Image-Pro Plus (IPP) software. The relationship between porosity and contact area of soft clay was got by considering the microstructure. Combining soil lateral stress transfer principle, a calculation theory of earth pressure considering soil contact area was got. The possible reason of the differences between earth pressure and the actual monitoring earth pressure was analysis by the case.

A Calculation Theory of Earth Pressure Considering Microstructure of Porous Materials

To explore a more reasonable earth pressure calculation theory, microstructure characteristics of soft clay in Hangzhou, Ningbo and Wenzhou had been observed using scanning electron microscope (SEM) and were quantitatively analysed using Image-Pro Plus (IPP) software. The relationship between porosity and contact area of soft clay was got by considering the microstructure. Combining soil lateral stress transfer principle, a calculation theory of earth pressure considering soil contact area was got. The possible reason of the differences between earth pressure and the actual monitoring earth pressure was analysis by the case.

Rankine earth pressure theory considering microstructure of porous materials

Soil as an engineering material has very complex properties, such as non-continuous, non-uniformity and nonlinear mechanical. In a certain extent, macroscopic properties of soil are affected by the changes of the microstructure. And microscopic porosity of soft clay and its influencing factors, the relationship between macro and micro porosity, the average contact area rate and its influencing factors are studied. Some mechanics problems were analyzed by using the relationship between macro-porosity and the average contact area rate. Combining soil lateral stress transfer principle, a calculation theory of earth pressure considering soil contact area was got. The possible reason of the differences between earth pressure and the actual monitoring earth pressure was analyzed by the case.

A three-dimensional statistical model for imaged microstructures of porous polymer films.

A thresholded Gaussian random field model is developed for the microstructure of porous materials. Defining the random field as a solution to stochastic partial differential equation allows for flexible modelling of nonstationarities in the material and facilitates computationally efficient methods for simulation and model fitting. A Markov Chain Monte Carlo algorithm is developed and used to fit the model to three-dimensional confocal laser scanning microscopy images. The methods are applied to study a porous ethylcellulose/hydroxypropylcellulose polymer blend that is used as a coating to control drug release from pharmaceutical tablets. The aim is to investigate how mass transport through the material depends on the microstructure. We derive a number of goodness-of-fit measures based on numerically calculated diffusion through the material. These are used in combination with measures that characterize the geometry of the pore structure to assess model fit. The model is found to fit stationary parts of the material well.