Production, Characterization, and Applications of Porous Materials
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Geometric Models for Isotropic Random Porous Media: A Review
Abstract
Models for random porous media are considered. The models are isotropic both from the local and the macroscopic point of view; that is, the pores have spherical shape or their surface shows piecewise spherical curvature, and there is no macroscopic gradient of any geometrical feature. Both closedpore and openpore systems are discussed. The Poisson grain model, the model of hard spheres packing, and the penetrable sphere model are used; variable size distribution of the pores is included. A parameter is introduced which controls the degree of openporosity. Besides systems built up by a single solid phase, models for porous media with the internal surface coated by a second phase are treated. Volume fraction, surface area, and correlation functions are given explicitly where applicable; otherwise numerical methods for determination are described. Effective medium theory is applied to calculate physical properties for the models such as isotropic elastic moduli, thermal and electrical conductivity, and static dielectric constant. The methods presented are exemplified by applications: smallangle scattering of systems showing fractallike behavior in limited ranges of linear dimension, optimization of nanoporous insulating materials, and improvement of properties of openpore systems by atomic layer deposition of a second phase on the internal surface.
1. Introduction
The development of models for random porous media and the settingup of structureproperty relationships have benefitted substantially from the rich body of theoretical work on disordered matter. Beginning with Ziman’s famous book on models of disorder [1] treatises like that on the physics of structurally disordered matter [2], the physics of foam [3], mechanical properties of heterogeneous materials [4], effective medium theory for disordered microstructures [5], structureproperty relations of random heterogeneous materials [6], transport, and flow in porous media [7] followed. Parallel to this progress in physics, mathematical methods for the description of random systems have been improved as documented in textbooks on stochastic geometry and its applications [8–10]. Activities of bridging ideas developed in physics and materials science on the one hand and of methods established by mathematicians on the other hand can be found, for example, in [10–14].
In this review we consider models for random isotropic porous media of both closedpore and openpore type. The pore surfaces have, at least piecewise, spherical curvature with arbitrary distribution of radii of curvature (for simplicity, called pore size distribution below). There is no restriction regarding the volume fraction of pores. The considered models are based on rules for arranging spheres in the threedimensional space. Three types of rules are used: random arrangement of noninteracting spheres (Boolean model, this model is not restricted to spherical shape of pores), denserandom packing of hard spheres (DRP), and random packing of partially penetrable spheres each consisting of a hard core and a soft shell (cherrypit model). The latter one appears as a numerical interpolation scheme between the Boolean model and the DRP method. A model for a porous medium is made by arranging spheres according to one of the above rules and, thereafter, allotting solid material to the space of the model which is not covered by any sphere. Such a model can be refined by coating the pore surfaces with a second solid phase.
The models are characterized by their basic geometrical features—volume fraction of pores, , specific surface area, , pore size distribution, , and correlation function, . On one hand, these characteristics are required to interpret experimental data, for example, smallangle scattering curves. On the other hand, they are important for understanding and control of the interplay between structure and physical properties of random porous media.
Physical properties of the models are discussed in terms of an effective medium approach. Explicit expressions for macroscopic elastic moduli, thermal and electrical conductivity, and static dielectric constant are given.
The definitions of the geometrical characteristics are explained in Section 2, the models are described in Section 3, physical properties are discussed in Section 4, and applications to experimental situations are presented in Section 5.
2. Geometrical Characteristics
The pore space is described as a random set . We consider volume fraction , specific surface area , pore size distribution , and correlation function as geometrical characteristics of . It is useful to introduce the complement of consisting of the part of the space which does not belong to . The complement of is indicated by . This notation is standard in stochastic geometry [10]. For example, if denotes the pore space of the porous medium, and are the volume fraction of the pore space and the solid bulk phase, respectively. is the same for and .
The pore space is described by the shape function: The volume fraction of pores is defined by the probability of finding a random test point in : If is known can be calculated by The correlation function denotes the probability of finding two random test points () inside the pore space. It is calculated from the shape function by Because of isotropy it depends only on the distance between the test points where is the solid angle. Replacing index by and setting one obtains from ((1), (5), and (6)) for the correlation function of the solid phase
The specific surface area of is defined by where is the probability of finding a random test point in and is defined by all points of the threedimensional space the distance of which from the surface is less than . If the correlation function of the system is known, is related to by [10]
For illustration we consider the most simple case of a single pore with radius in a sample of volume as a model for a dilute system of equal pores; that is, . The volume fraction and the specific surface area are given by and , respectively. The integral in formula (5) is evaluated as the convolution integral of a spherical shape function [16]. Neglecting terms of the order one obtains
Polydisperse systems are additionally characterized by a size distribution function with mean value and variance
3. Models
3.1. Poisson Grain Model
The Poisson grain model (also known as Boolean model, see [10]) is an important and versatile model for generating random twophase structures. It is created in two steps. In the first step a random (Poisson) point field is generated (here in the threedimensional space) with number density of points. This point field has the following property. Considering an arbitrary finite region with volume , the number of points situated within is a random variable. The probability that takes the value is The numbers are independent random variables if the regions do not overlap. Practically, the point field, that is, the coordinates of the points, can be produced using a random number generator for numbers equally distributed in finite coordinate intervals.
In the second step, respectively, one sphere is placed on each point of the point field where the size distribution of the spheres is arbitrary. The settheoretical union of all spheres represents a random set with volume fraction where is the mean volume of the spheres.
Consider definition (2) of volume fraction for the special case that is generated using equal spheres with radius and choose a sphere of the same radius for in (13). Then the probability that does not contain any point of the Poisson point field, that is, in (13), is equal to the probability that the center of the sphere has always a distance to any point of the point field. This is exactly definition (2) for the volume fraction of which gives (14) for the considered special case. The general result (14) is obtained using the same idea.
The specific surface area of the model is where is the mean surface area of the spheres.
The correlation function as defined in (5) and (6) is where and is the correlation function of a single sphere averaged over the size distribution according to
The first formulation of the model was given by Porod [17] for equal spheres. A fundamental and general description can be found in [10].
Identifying the space covered by spheres in the Boolean model with the pore space of a random porous medium, one obtains Figure 1 shows a cutout of a Boolean model with constant distribution of radii in the interval from 1 to 2. Note that some of the small spheres may be not visible because they can be covered completely by large spheres.
3.2. Random Packing of Hard Spheres
The model of hard spheres is easily defined by the condition that two particles cannot overlap. Despite the simplicity of the model it appears very complex especially if the packing fraction of spheres is high. For equal spheres, packing fractions of 0.74 (regular hexagonal and facecentered cubic structure), 0.68 (regular bodycentered cubic), and 0.636 (denserandom packing) are obtained. While regular packing of spheres has been known as a model for the arrangement of atoms in simple crystal structures [18], Bernal [19] proposed the model of denserandom packed spheres as an approach to the structure of simple liquids. Early reviews on construction algorithms and applications of randomly packed sphere models can be found in [20–22]. More recently, Löwen [23] reviewed methods for analyzing hard sphere systems such as the PercusYevick theory, the densityfunctional approach, and methods of computer simulations from the physical point of view. Mathematical theories are described in [10].
In this work we use the forcebiased algorithm for the generation of computer models of hard spheres. This algorithm includes the simulation of systems with arbitrary size distribution of spheres and works very efficiently [24–26]. The algorithm starts with a dilute system of randomly distributed spheres in a parallelepipedal container with periodic boundary conditions applied. Initially, each sphere has an inner hard core diameter and an outer diameter . During iteration step , distances between two spheres are not allowed. No interaction occurs for . If , sphere is shifted from position to position according to where depends on the diameters of spheres and and on control parameters and is the number spheres which have overlapped with sphere . Quantity is only used to optimize the convergence of the algorithm. It does not represent any physical interaction. After each step the packing fraction is enhanced by adjusting the sphere diameters at constant box size. Denoting the maximum number of iteration steps by , for all if .
The algorithm is terminated if either the desired packing fraction is attained, or if no further progress can be achieved, or if . For details see [24–27].
The volume fraction of spheres and the specific surface area of a system of packed hard spheres with number density are simply given by respectively.
Figure 2 shows of systems of denserandom packed hard spheres with bimodal size distribution. For a size ratio of 0.45 of smalltolarge spheres the volume fraction has a maximum of 0.684 at a number fraction of 0.2 of large spheres. The maximum packing fraction decreases as the size ratio approaches 1 and takes the value of 0.636 for equal spheres.
There are no exact formulas for the correlation function of dense hard sphere systems. Current approximations and discussions of this problem can be found, for example, in [10, 11, 23]. MonteCarlo simulations based on the definition (4) can be used to calculate the correlation function for numerically simulated hard sphere systems.
3.3. Penetrable Sphere Models
Penetrable sphere models can be used to describe openpore structures. Widom and Rowlinson [28, 29] introduced a penetrable sphere model for the study of liquidvapor phase transitions. In this model, the spheres are allowed to overlap completely. The main difference to the Boolean model described above consists in the definition of a potential energy which is related to the volume occupied by the overlapping spheres. The WidomRowlinson model was first extended and applied to studies on phase behavior of complex fluids in [30, 31]. Similar models were described by a number of authors [32–40]. Note that the forcebiased algorithm described in the previous section shows features of a penetrable sphere model during the rearrangement process.
Torquato [41–43] introduced the penetrable concentricshell model which is called cherrypit model for brevity. In this model, each sphere of radius is composed of an impenetrable core of radius (the pit) surrounded by a perfectly penetrable shell of thickness . For the case of randomly distributed sphere centers, the limits of and correspond to the Boolean model and the random hard sphere one, respectively. Approximate analytical expressions for volume fraction and specific surface area of this model were presented in [6, 39, 40, 44]. Results of numerical simulations with equally sized spheres [45] agreed reasonably with the predictions for the volume fractions by [39, 40]. Formulas with improved accuracy for volume fraction and specific surface area of the cherrypit model with equally sized spheres have been given in [46, 47]. The cherrypit model is a variable model which appears as an interpolation scheme communicating between the Boolean model and the hard sphere packing paradigm. Also it allows to vary the degree of openporosity by changing the value of . While previous studies of the cherrypit model concentrated on using equally sized spheres actual progress has been achieved by introducing arbitrary size distributions of the spheres [48].
Samples of the cherrypit model can be generated by means of the forcebiased procedure for packing of hard spheres. After choosing the number of the spheres, the desired size distribution, and the intended packing fraction, the procedure is started. The result consists in the set of the coordinates , the radii of the spheres, and the achieved packing fraction given by (20). The corresponding cherrypit model is obtained by identifying the spheres of the simulated system with the pits of the cherrypit model. The union of all spheres with radii represents the pore space of the model. The procedure is illustrated in Figure 3. Figure 4 shows cherrypit models generated from a system of randomly packed pits with Gamma distributed radii and .
(a)
(b)
(c)
(d)
The samples shown in Figure 4 differ by the values of chosen for the construction of the cherrypit models. Clearly, the degree of openporosity rises with decreasing values.
The volume fraction of a simulated model is determined using the pointcount method where random test points are scattered in the simulation box. The number of test points situated in pores divided by the number of all test points gives an estimate for the volume fraction of pores. The specific surface area is calculated in a similar way. Here, the random test points are distributed on the surfaces of the spheres of the cherrypit model. The number of points on the surface of a sphere not covered by any other sphere contributes to the surface of the model. The number of contributing points to the number of all points gives an estimate for the surface area of the model. Figure 5 illustrates the statistical reliability of the simulations and its dependence on the size of the model measured by the number of spheres of the simulated cherrypit model. The standard deviation of both and , each divided by the mean value of the corresponding quantity, decreases with increasing box size. The statistical accuracy of the volume fraction is of the order of 0.25% for . The specific surface area is determined with an statistical accuracy of less than 2% for and 0.5% for .
Statistical analysis of large numbers of simulated monodisperse [47] and polydisperse [48] cherrypit models revealed that the volume fraction, , of spheres and the specific surface area, , are given by the approximate formulas:
Again, relations (18) apply if the spheres describe the pore space of a medium. Expressions (23) and (25) depend on the quantities and , which characterize the basic system of randomly packed hard spheres and the construction scheme of the cherrypit model, respectively. Parameter defines the unit of length of the system. For monodisperse systems it is equal to the sphere radius. In the polydisperse case the relation with , follows from fitting (25) to the values of the specific surface areas of simulated cherrypit models [48]. Parameters and are the mean value and the standard deviation of the radii distribution, respectively, defined by ((11), (12)). The error appearing for in (26) is below 2%.
Expression (23) does not depend on any parameter related to the size distribution of spheres. So it appears as quite a general relation. Detailed analyses in [48] showed that (23) is most reliable for systems with , and for size distributions with up to about 3.5 ( is the mean radius of spheres). Figure 6 compares (23) to simulated data obtained from systems with 10.000 spheres generated at periodic boundary conditions where the radii are distributed according to a Gamma distribution [48].
Formula (25) is accurate in the limits of few percent for . The impact of the skewness of the size distributions on the accuracy of (25) is negligible. Figure 7 illustrates the validity of (25).
3.4. Interphase Models
3.4.1. Limit of Very Thin Interphase Layer
We start with one of the models for a porous medium with a single bulk phase. The pore space is characterized by its volume fraction and the specific surface area . Then, the internal surface of the model is coated by a layer of a second solid phase of thickness . This layer is called interphase. Denoting the smallest radius of curvature of a pore of the model by , for , the volume fraction of the layer is approximately the volume fraction of the pores reduces to and the volume fraction of the bulk phase remains
3.4.2. Boolean Interphase Model
Another way to calculate the volume fraction of the interphase is to compare two models differing only by the size of the spheres used for the construction. Consider a Boolean model 1 generated using spheres with size distributions . The space not covered by any sphere defines the bulk phase 1. Reducing the radius of each of the spheres of exactly this model by a second model 2 is obtained with size distribution , where for is required. This model defines the pore space. Then, the interphase of thickness is given by the space neither belonging to the bulk phase 1 nor to the pore space and the volume fraction of the interphase follows as Figure 8 shows the intersection of a Boolean interphase model. For the special case of Boolean models with spheres of equal radius one obtains For , follows which corresponds to ((15), (27)).
3.4.3. Hard Sphere Interphase Models
For interphase models based one hard sphere systems the volume fraction of the interphase is calculated similarly. Assuming a Gaussian distribution of radii the volume fraction of the interphase is according to (20): In the limit one obtains with ((21), (33)) analogously to (27)
3.4.4. CherryPit Interphase Model
The cherrypit model with one bulk phase can be transformed to an interphase model in a similar way as the Boolean model and the hard spheres one. However, expression (14) for the volume fraction of pores for the cherrypit model does not depend explicitly on the pore size. Remembering that the hard core radius and the corresponding pore radius are related by and considering two monodisperse cherrypit models 1 and 2 characterized by the same values of and but different pore size with and , the volume fraction of the interphase of the corresponding cherrypit interphase models is
Setting , , replacing by and carrying out one obtains from ((23), (25)) which corresponds with (27).
3.5. Reconstruction
It is often important to generate models not from mathematical algorithms or rules but from experimentally obtained structural information. Such information is basically incomplete for random systems. The generation of models from experimental data or reconstruction must therefore focus on reproducing important structure parameters like volume fraction, pore size distribution, or correlation functions. The problem of reconstruction is related to the wellknown field of stereology (see chapter 10 in [10]). A typical problem of stereology is, for example, to estimate the size distribution of particles from the length distribution of chords inside particles where the chords are determined from linear sections of the considered sample. Few problems are exactly solved, for example, the determination of the size distribution of pores from planar sections of the sample if the pores have spherical shape. The size distribution of the pores determines, however, not yet the spatial distribution of the spheres. In this case, smallangle scattering data would be helpful to complete information about spatial correlations of the 3dimensional arrangement of the pores.
Procedures for the reproduction of random structures have been successfully developed in recent years. Roberts generated 3dimensional models from 2dimensional images [49]. Yeong and Torquato [50, 51] proposed a procedure applicable to very different types of random media. Hilfer and Manwart [52] used physical properties like conductivity and permeability in order to improve reconstruction. Reconstruction from microtomographic images was carried out by Øren and Bakke [53]. Ohser and Schladitz [14] presented general methods of computational image analysis. Methods for the reconstruction of random media of the type described by the Poisson grain model have been developed by Arns et al. [54, 55]. Problems occurring with reconstruction on the nanometer scale have been addressed by Prill et al. [56].
4. StructureProperty Relationships
Properties of random heterogeneous materials including porous media have been considered in many theoretical studies. The effective medium theory see, for example, [4, 5], and its combination with the maximum entropy approach [57] have been proven as a powerful tool to understand macroscopic properties, and various structure models have been considered for the study of structureproperty relations of heterogeneous materials [6, 7, 58–60]. Among the different structure models, the Poisson grain model appears as one of the most successful approaches for establishing structureproperty relationships of random media [58, 61–66]. Recent work on the optimization of property combinations of random media by means of structure variation can be found in [67, 68].
We consider the composite sphere assemblage model (see, e.g., [4]) and related effective medium theories (see, e.g., [5]) as most adequate for the models described in the previous sections. Explicit expressions for dielectric constants, thermal conductivity, and isotropic elastic moduli of models consisting of a matrix and spherical inclusions have been derived and discussed in [4, 5, 67–69]. Below, the idea of this approach is explained for the example of the thermal conductivity of a porous medium with coated pore surface.
A corresponding composite sphere element (CSE) is made up by a central sphere with radius filled with phase , a concentric spherical shell of phase with thickness , and a second shell of phase with thickness (see Figure 9). In our case, phase represents the pore content (vacuum, air, or something else), phase is the pore coating layer, and phase is the bulk material of the porous medium. The CSE is embedded in the effective medium. The volume fraction of the pores, the coating, and the bulk material are denoted by , respectively. Writing the volume fractions of the phases are The temperature distribution in the porous medium follows the Laplace equation And the corresponding intensity field is Heat flux and thermal conductivity are defined by According to [70] the solution of the Laplace (43) for the CSE unit embedded in the effective medium is The boundary conditions of the heat flux through the interfaces pore to coating, coating to bulk, and bulk to effective medium require continuity of the radial component of the heat flux, , and of the tangential component of the temperature gradient, . Denoting the values of the thermal conductivity of the pore phase, the coating interlayer, the bulk material, and the effective medium by , and one obtains a system of linear equations This system has a solution if the determinant of the coefficient matrix vanishes. Solving the resulting equation for one obtains for the effective thermal conductivity with Quantity can be interpreted as the effective conductivity of a subsystem consisting of spherical pores with conductivity embedded in a matrix with property and the corresponding volume fractions.
It is known that a number of physical problems are mathematically equivalent to the formulation of thermal conductivity [5]. Therefore, (43) to (49) apply also to electrical conductivity, static dielectric property, magnetostatics, and diffusion (see Table 1).

The elastic moduli of the considered model can be calculated using either the CSA model [4] or the extended replacement method [71]. The results can be expressed in a similar form as expressions ((48), (49)). Denoting the effective property of the system by , one can write [48, 68, 69]: The symbol stands for dielectric constant, thermal conductivity, and related properties, , denote elastic bulk and shear modulus for isotropic systems, and index stands for pore, interphase layer, and bulk phase.
Expressions ((50) to (53)) are rigorous results obtained for geometrical situations as given in the structure models described above. The results for the properties are unique and no uncertainty due to unknown topology of phase distribution occurs. Nevertheless there are relations to the HashinSthrikman bounds for properties of heterogeneous materials [72] which we discuss here for the case of the dielectric constant . For simplicity, we use a model consisting of a matrix and spherical inclusions. Choosing with , assuming , and changing the topology of the system by interchanging properties and volume fractions of matrix and inclusions, that is, , one obtains from ((50), (51)) the HashinSthrikman bounds for the effective dielectric constant, , of a twocomponent composite (see also [5], (2.3)) The same procedure can be carried out for the HashinSthrikman bounds for the isotropic elastic moduli (see [4], Sections 3.2 and ). For a given distribution of phases on pores, interphase layer, and bulk there is no uncertainty of the topology and, consequently, expressions ((50) to (53)) for the properties are unique.
5. Applications
5.1. FractalLike Pore Structure
Porous media may have fractal features (see, e.g., [73–75]). We consider here a model for surface fractals which is based on the Boolean model described in Section 3.1. This model is tunable with respect to the fractal dimension of the internal surface, and explicit expression for the geometrical characteristics defined in Section 2 is known [68, 76]. The size distribution of spheres used for the construction of the model is defined by selfsimilarity rules for the radii and the partial number density of spheres : where and are radius and number density of spheres belonging to the interval . Parameter describes the ratio of minimum and maximum sphere size. The normalized size distribution is then given by
Number density of spheres, volume fraction, specific surface area, and correlation function are given by ((14), (15), and (16)) and where The selfsimilar model described by ((14), (15), and (16)) and ((58), (59), (and 60)) exhibits the properties of a surface fractal if the specific surface area tends to infinity while the volume fraction remains finite for . This is the case for is the dimension of the fractal internal surface of the model according to the MinkowskiBouligand definition [77, 78]. Quantity is the same as in (8). Fractality of a porous medium can be detected by means of smallangle scattering methods [73]. In such cases the limit is of interest which determines the behavior of the scattering intensity at large scattering angles. One obtains from ((7), (60)) which is in accordance with [73, 79].
The behavior of the selfsimilar system is illustrated by Figures 10 and 11. The volume fraction of pores increases with increasing values of the selfsimilarity parameter and also with decreasing ratio of radii of smallest to largest pores (Figure 10). Of course, keeps limited in the fractal limit , where takes the meaning of the fractal dimension of the internal surface (see expressions ((14), (58)). In the extreme case of and the porosity tends to 1. The specific surface area given by ((15), (59)) is controlled by the term . Clearly, tends to infinity for independent of the value of the fractal dimension of the internal surface. The behavior of in the fractal limit appears also applying (9) to (63). Obviously, tends to infinity for each fractal dimension of the internal surface. Figure 10 illustrates the dependence of on the selfsimilarity parameter for different values of.
The scattering intensity of the selfsimilar system can be calculated [11, 68, 78] using where is the absolute value of the scattering vector defined in the usual way [74]. If the size of pores is on the nanometer scale, is called smallangle scattering intensity. Inserting ((16), (60)) in (64) an explicit expression for the smallangle scattering intensity can be obtained. The limit of small values given in (63) becomes apparent in in the form in the fractal limit for not to small values.
Figure 11 shows scattering intensities of a selfsimilar system with fractallike behavior for small. The scattering intensity is dominated in the region by the largest pores of the system ( in suitable units, e.g., in nanometers). The curve for the system with small range of selfsimilarity follows approximately expression (65) in the interval , and for the scattering intensity is determined by the smallest pores of size . Systems showing such scattering curves are sometimes called fractallike. Seriously speaking, the system is selfsimilar over about one order of magnitude of length scale. The curves for represent systems which are selfsimilar over 3 orders of magnitude. This is reflected by the linear behavior in the plot for where the limit (65) applies. Here, the term fractallike would be justified.
5.2. Optimization of Porous Dielectric Materials
The improvement of silicon based microelectronic devices like microprocessors has been a continuing process over decades [80]. This includes the further development of many components and also the replacement of components by new materials, for example, the replacement of aluminum wires for connecting billions of transistors in a microprocessor circuit by copper wires. This replacement was necessary because the performance of microprocessors is increasingly affected by the onchip interconnect design and technology. Signal delay caused by the resistorcapacitor (RC) interaction in the system of copper wires and insulating layers becomes more and more important compared to the transistors’ signal delay [81, 82]. Additionally, crosstalking and power dissipation worsen due to increasing RC coupling caused by continuous reduction of device dimensions [83, 84]. The introduction of the copper technology was one step to reduce these unfavorable implications of continuous miniaturization. Another one concerns the reduction of the dielectric constant of the insulating layers separating the copper wires. Manufacturable materials with effective dielectric constants of 2.90 to 2.60 are available at the present time; however, feasible solutions with are difficult to obtain [85].
Research is in progress to define dielectric materials with extremely low dielectric constant. Besides dense low materials like organosilicate glass and the challenging airgap technology, insulating materials with porosity on the nanometer scale are considered as a promising way to reduce the above problems [85]. Among possible nanoporous insulating materials [86–88] nanoporous silica has the advantage that it can be implemented comparably easily in the current silicon technology.
We consider the following problem: optimize the structure of nanoporous silica in such a way that the dielectric constant is as low as possible, for example, less than 2 (vacuum: , SiO_{2}: ), and the Young’s modulus as high as possible, for example, higher than 5 GPa (SiO_{2}: ) which would match future technological requirements. Using an interphase model specified by the volume fractions of pores, , and interphase, , and choosing a favorable second material additionally to silica, effective properties can be calculated for the models with ((50), (51), and (52)) and (53) with the corresponding values for ; the Young’s modulus is given by for isotropic systems. The properties of the components used for the simulations are quoted in Table 2. Figure 12 shows contour plots of effective property combinations in the plane spanned by and for a model consisting of porous Parylene coated with SiO_{2}. Due to the porosity and the low value of the bulk material very low values of the dielectric constant can be achieved. However, the Young’s modulus is too small for pure porous Parylene. Coating with SiO_{2} improves the effective modulus with the result that for and pore fraction of at least 0.65 excellent values for can be combined with reasonable values . The required porosity of at least 0.65 is, however, very high and cannot be achieved with random hard sphere packing models with equal spheres. Instead, the pores must have a polydisperse size distribution to obtain . The parameters of the required size distribution can be determined using the methods described in Section 3; see also Figure 2.

Starting with porous silica and coating it with Parylene another effective property map appears (Figure 13). Systems with an effective dielectric constant of can be achieved at moderate porosity of about 0.60 for values of the effective Young’s modulus of not less than 12 GPa, which appears as an excellent combination of dielectric and mechanical properties for the purpose under consideration.
5.3. Simulation of Coating Processes
The previous section showed for a specific case that introducing an interphase layer into a porous system may come out as a useful method to improve effective properties of a porous system. Coating of porous media has been proven to be a successful method to modify their properties in many fields of application. This applies not only to the properties discussed above but also to flow and transport in porous media [7], percolation [89], enzyme immobilization [90, 91], adsorption processes [92–94], and others. Different methods of coating and surface refinement of openpore materials are known, for example, spray coating [95] and microarc oxidation coating [96] of metallic foams [97], and atomic layer deposition [15, 98–101]. The latter method is of special interest for media which are characterized by openporosity on the nanometer scale.
We study the growth of a layer on the internal surface of openpore systems within the framework of the cherrypit model. Expression (37) for the volume fraction of a coating layer is written in the form where denotes the coating time. For simplicity we consider a system with narrow pore size distribution. In a continuous coating process the radius of a given pore reduces according to One obtains where is the growth rate of layer thickness measured in units of the pit radius of the cherrypit model. For discontinuous processes like atomic layer deposition, has to be replaced by , where is the number of the deposition cycle and is the increment of layer thickness achieved by one cycle measured in units of the pit radius. We apply the present approach to experimental results for atomic layer deposition of on porous polypropylene [15]. Figure 14 compares the evolution of the porosity and the volume fraction of the layer with the number of deposition cycles. The cycle numbers are corrected for the induction period [15]. The model curves reproduce the trend of the experimental data with an accuracy of about 20%.
The estimated volume fractions can now be used to predict effective properties of the coated material. Using known data (see Table 3) for bulk, , shear, , modulus, and the thermal conductivity of polypropylene and for the effective elastic and thermophysical properties of the coated material are calculated. Figure 15 shows the results for the effective thermal conductivity and the Young’s modulus. Both for the Young’s modulus and the thermal conductivity the essential increase is achieved within the first 20 deposition cycles. These results supplement the experimental data obtained in [15] by predicting the change of physical properties during the deposition process.

6. Conclusion
There are appropriate theoretical tools for a consistent description of the structure and effective properties of isotropic random porous media. These tools provide improved facilities to develop suitable models for porous materials from experimentally obtained structural parameters and to optimize effective properties by simulating the response of effective properties on structural modification.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
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Copyright © 2014 Helmut Hermann and Antje Elsner. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.