# Anomalous Grain Boundary Diffusion: Fractional Calculus Approach

Authors: Renat T. Sibatov
Journal: Advances in Mathematical Physics
 (2019)

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## Abstract

Grain boundary (GB) diffusion in engineering materials at elevated temperatures often determines the evolution of microstructure, phase transformations, and certain regimes of plastic deformation and fracture. Interpreting experimental data with the use of the classical Fisher model sometimes encounters contradictions that can be related to violation of Fick’s law. Here, we generalize the Fisher model to the case of non-Fickian (anomalous) diffusion ubiquitous in disordered materials. The process is formulated in terms of the subdiffusion equations with time-fractional derivatives of orderα∈(0,1] and β∈(0,1] for grain volume and GB, respectively. It is shown that propagation along GB for the case of a localized instantaneous source and weak localization in GB (β>α/2) is approximately described by distributed-order subdiffusion with exponents α/2 and β. The mean square displacement is calculated with the use of the alternating renewal process model. The tail of the impurity concentration profiles along the z axis is approximately described by the dependence ∝exp(-Az6/5) for all 0<α≤1, as in the case of normal GB diffusion, so the 6/5-law itself can serve as an identifier of a more general phenomenon, namely, anomalous GB diffusion.


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## Body

## 1. Introduction

Grain boundary (GB) diffusion can control many processes in materials and determine the evolution of microstructure, phase transformations, and some regimes of plastic deformation and fracture [1]. Usually, atoms diffuse along grain boundaries (GB) by orders of magnitude faster than in crystalline grains. In recent decades considerable progress has been made in the accuracy and reliability of measurements of GB diffusion parameters [1–3]. However, many fundamental aspects including mechanisms at the atomic level remain unclear. One of the reasons for this discrepancy is that experimental data on GB diffusion is usually interpreted with the Fisher model [4] and its modifications [1–3] based on the Fick law, which can be violated. The goal of the present paper is to generalize the classic model to the case of non-Fickian (anomalous) diffusion and compare predictions of the new model with the classic solutions.The Fisher model considers a system with a single isolated GB of thicknessδ placed in a semi-infinite medium. Usually, the diffusion coefficient Db along GB is assumed to be significantly larger than D in grain volume. The impurity atoms initially located on the surface of a semi-infinite medium diffuse along GB and in volume, at a constant annealing temperature T during the time t [5]. The propagation in such a system is usually described by a set of diffusion equations. The exact solutions for the Fisher model with constant and instantaneous source were found by Wipple and Suzuoka [6, 7]. Levin and McCallum [8] have shown that the dependence of the average concentration ln⁡C¯(z,t) on z6/5 is linear for sufficiently large z [1]. Such dependence often serves as an identifier of the GB diffusion phenomenon. It is natural to expect the anomalous GB diffusion to be characterized by index different from 6/5.Several papers discuss slowing down the diffusion of light elements due to GBs. In [9], various aspects of the interaction of hydrogen with grain boundaries in nickel (Ni) were investigated by first-principle calculations by means of the density functional theory. Localization and migration of hydrogen strongly depend on type of GB. GBs can act as a two-dimensional obstacle (wall) for H migration. In [10] molecular dynamics simulation of dissolution and diffusion of hydrogen in GB Σ5(310) of tungsten has shown that high local concentration of H in GB (for example, 30% at 900 K) leads to an unordered structure of GB that can provide trapping of H. Autoradiography [11] applied to polycrystals with different grain sizes showed that the increased GB area leads to the suppression of migration of 14C isotope in α-iron. The opposite effect is observed for 60Co isotope. The authors of [11] conclude that GBs in α-iron act as traps for interstitial atoms and slow down their diffusion.Besides that, a number of experimental facts indicate anomalous diffusion (mainly, subdiffusion) of impurities and defects in disordered materials (see, e.g., [12–16]). Usually the phenomenon is characterized by the expansion law for the diffusion packet Δ(t)∝tα/2. In the case of 0<α<1, the diffusion packet width increases with time more slowly (subdiffusion), and in the case α>1 faster (superdiffusion) than in the normal case. Diffusion equations with fractional-order derivatives are an effective tool to describe anomalous diffusion [15, 17]. GB diffusion also demonstrates anomalous diffusive behavior [1, 18]. In [19], the fractional calculus approach for GB diffusion is justified within the classic Fisher model.In the present paper, we consider a model of anomalous diffusion along the grain boundaries accounting for localization in grains and GB. The process is described by the anomalous diffusion equations containing time derivatives of fractional ordersα and β for grains and boundary, respectively. The mean square displacement 〈Z2〉 along GB and the penetration depth distribution are calculated for the case of a localized instantaneous source and weak localization (β>α/2). The propagation along the boundary is approximately described by subdiffusion with a mixture of dispersion parameters α/2 and β. The solutions are examined by the Monte Carlo simulation. Numerically, we also consider the cases of constant and instantaneous source on the surface. The impurity density and the mean square displacement are calculated with the use of the alternating renewal process model. It is remarkable that the tail of the impurity concentration profiles along GB (z axis) is approximately described by the same dependence ∝exp(-Az6/5) for all 0<α≤1, as in the case of normal GB diffusion.


## 2. Anoamlous Diffusion and Fractional Stable Distributions

Despite of different specific mechanisms generating anomalous diffusion processes in different physical phenomena its main features can be obtained from the continuous time random walk (CTRW) scheme as timet→∞ [20, 21].The basic idea of the simplest (one-dimensional decoupled) version of CTRW model is that different jump lengthsXj, as well as waiting times Tj between two successive jumps, are random variables independent of each other and among themselves. One supposes that jump lengths and waiting times have the following distributions: (1)PX>x∝x-α,α>0,x→∞;PT>t∝t-β,β>0,t→∞.and both directions for a test particle leaving a trap are equal in probabilities. If α>2 and β>1 we observe normal diffusion; all other values of α and β lead to anomalous diffusion with characteristic exponents α,β. The anomalous diffusion packet Δ(t)∝tβ/α for 0<α<2 and 0<β<1. When β/α<1/2 we have subdiffusion regime; when β/α>1/2 we observe superdiffusion regime. The asymptotic term of the probability density function p(x,t) (propagator) obeys the fractional diffusion equation (see, e.g., [22]):(2)∂px,t∂t=-D-Δα/2∂1-β∂t1-βpx,t,0<α≤2,0<β≤1.Here (3)∂νpx,t∂tν=1Γ1-ν∂∂t∫0tpx,τt-τνdτ,0<ν<1,is the Riemann-Liouville fractional derivative and -(-Δ)α/2 is a fractional power of the Laplace operator (see for details [23]). This equation has a solution in the self-similar form: (4)px,t=Dtβ-1/αqxDtβ-1/α;α,β,0,where the function(5)qx;α,β,0=∫0∞gα,0xτβ/αg+βττβ/αdτis the so-called fractional stable density [24, 25] expressed through the one-sided stable density g+(β)(τ) and symmetric stable density g(α,0)(x). The stable densities are usually defined by their characteristic functions. In the one-dimensional case (6)g^α,θk=∫-∞∞eikxgα,θxdx=exp-kαexp-iπ2θαsignk.Here, θ is an asymmetry parameter. The asymmetric fractional stable density is simply defined by(7)qx;α,β;θ=∫0∞gα,θxτβ/αg+βττβ/αdτ.The characteristic function of a fractional stable law(8)q^k;α,β,θ=Eβψk;α,θ,ψk;α,θ=-kαexp-iαθπ2signk,is expressed through the Mittag-Leffler function(9)Eβx=∑n=0∞xnΓ1+nβ.Further, we consider only subdiffusive case for transport in GB and bulk, because this type of anomalous diffusion is observed more frequently in disordered solids. General representation (7) will be helpful for calculation of MSD with the use of an alternating renewal process.


## 3. Fractional Generalization of the Fisher Model

In the Fisher model, GB is modeled as a high-diffusivity uniform slab of widthδ perpendicular to the surface; this slab is embedded into a low-diffusivity isotropic medium [1, 4]. Diffusive motions in GB and surrounded medium are characterized by coefficients Db and D such that Db≫D. In a typical experiment, a layer of impurity atoms or tracer atoms is created at the surface and then the sample is annealed at a constant temperature T during time t [2].Consider the modified Fisher model, accounting for the generalized diffusion in the grainsx>a and in GB x<a. The width of GB δ=2a. Equation for subdiffusion in grains is(10)∂C∂t=D∂1-α∂t1-α∂2C∂x2+∂2C∂z2,x>aThe same equation with β instead of α and Db instead of D is used for diffusion in GB (x<a).For small GB thicknesses, assuming slight variation in diffusant concentration across GB, we arrive at a single boundary condition for the modified Fisher model(11)∂Cg±a,z,t∂t=Da∂1-α∂t1-α∂Cgx,z,t∂xx=a+Db∂1-β∂t1-β∂2Cg±a,z,t∂z2Using the Fourier transformation onz and Laplace transformation on t of equation (10) and boundary condition (11) in the case of instantaneous source with initial condition C(x,z,t=0)=Kδ(z), we obtain (12)C~=Ksα-1sα+kz2D-Ksα-1kz2Dbsα/Dsβ-1exp-x-akz2+sα/DD/as1-αkz2+sα/D+s+kz2Dbs1-βkz2+sα/D.This transform solution generalizes Suzuoka’s solution [7]. For a particular case when parameters and concentration of traps in grains and GB coincide (α=β), the expression can be inverted and represented in the form(13)Cαx,z,t=∫0∞α-1tτ-1-1/αg+αtτ-1/αCSx,z,τdτ,where CS(x,z,τ) is the Suzuoka solution [7] with anomalous diffusion coefficient instead of the ordinary one and g+(α)(t) is the one-sided α-stable density [26]. The integral in (13) can be calculated by simple Monte Carlo algorithm (14)Cαx,z,t≈CSx,z,tαS+αby averaging over one-sided α-stable variables S+(α). The expression similar to (13) is applicable for the case of a constant source and the Whipple solution [6].


## 4. Fractional Alternating Renewal Process and Grain Boundary Diffusion

It is useful to consider the process in terms of a single-particle random walk. The displacement of a particle alongz depends on the distribution of times that the particle spends in GB and grains [19]. Let us simplify the situation by considering an instantaneous source at GB at z=0 (the case of a localized source). The time scale is divided into intervals of motion in grains and GB. To determine the distribution of total time spent in GB we consider the alternating renewal process [27]. Renewal events are associated with the transitions from the grains to GB and vice versa. For definiteness, we assume that at time t=0 the particle enters GB. In [27], we derived an expression for the double Laplace transform of PDF pb(tb∣t) of total residence time in one of the states (e.g., in GB):(15)p~bsb∣s=Ψ~bs+sb+ψ~bs+sbΨ~vs1-ψ~bs+sbψ~vs,where ψ~b(s) and ψ~(s) are the Laplace images of PDF’s of waiting times Tb and Tv in GB and, in the grain volume,(16)Ψ~bs=1-ψ~bss,Ψ~vs=1-ψ~vssare images of the corresponding distribution functions. The elementary times Tb and Tv are independent random variables.We consider the subordinated Brownian motion directed by fractional Poisson processes with different order and rate for grains and GB (see Appendix A). The density and distribution of waiting time in GB are characterized by the following asymptotic Laplace transforms:(17)ψ~bs~1-εδD⊥μbβθbsβ,Ψ~bs~εδD⊥μbβθbsβ-1,and in grain volume:(18)ψ~vs~1-εDμvαθvsα/2,Ψ~vs~εDμvαθvs-1+α/2,where small shift ε from the boundary is introduced to overcome the problem of return times for the Brownian motion (then we assume ε→0). Here, α and β are orders of fractional Poisson process for grains and GB, μv and μv are corresponding rates, and θv and θb can be considered as average times between subsequent localization events. Substituting these expressions into (15) ε goes down and we came to the following transform: (19)p~bsb∣s=s+sbβ-1+ηs-1+α/2s+sbβ+ηsα/2,where (20)η=D⊥μbβθbδDμvαθvThe inverse double Laplace transform leads to an expression (see details in [27])(21)pbtb∣t=1+2βt-tbαtbηtbβ-2/αqα/2,βt-tbηtbβ-2/α.containing one-sided fractional-stable densities (22)qα/2,βζ=∫0∞τ2β/αg+α/2ζτ2β/αg+βτdτ.IfT≫Tb, that is valid for t→∞ in the case β>α/2 (weak localization in GB), we have(23)pbtb∣t≈2βtαtbηtbβ-2/αqα/2,βtηtbβ-2/α.Knowing distribution of the displacement along GB, due to the motion in grains(24)gz∣tb=∫0∞exp-z2/4DμvαθvτπDμvαθvτt-tbατ1+1/αg+αt-tbτ-1/αdτand due to motion in GB (25)bz∣tb=∫0∞exp-z2/4DbμbβθbτπDbμbβθbτtbβτ-1/β-1g+βtbτ-1/βdτ,one can write PDF of the total displacement along GB as a convolution, (26)pz∣tb=∫0tgz-ζ∣tbbζ∣tbdζ.As a result, we have (27)pz∣tb=∫0∞dτ1∫0∞dτ2exp-z2/4Dμvαθvτ1+Dbμbβθbτ2πDμvαθvτ1+Dbμbβθbτ2×tbt-tbαβτ1-1/β-1τ2-1/α-1g+αt-tbτ1-1/αg+βtbτ2-1/β.The mean square displacement averaged over an ensemble of tracers is(28)z2tbens=2∫0∞dξ∫0∞dζDμvαθvt-tbαζα+Dbμbβθbtbβξβg+αζg+βξ.Taking the following property of the stable subordinator into account (29)∫0∞ξ-αg+αξdξ=1Γ1+α,we obtain the subdiffusive behavior with two fractional exponents:(30)z2tbens=2DbμbβθbΓ1+βtbβ+2DμvαθvΓ1+αt-tbα.Averaging over timetb with the use of (23) (see Appendix B) leads to(31)z2t=2DbδDμvαθvD⊥Γ1+α/2Γ1+β2tα/2+2DμvαθvΓ1+αtα.Density (23) is an asymptotic expression for the case β>α/2. It leads to MSD consisting of two power laws with exponents α/2 and α. This behavior is well confirmed by simulation for α≤1 and β=1. But for β<1, Monte Carlo computation indicates an additional component in 〈z2〉 proportional to tβ: (32)2DbμbβθbΓ1+βtβ.This term is clearly interpreted, when the process of propagation along GB is considered a distributed-order subdiffusion. When Db≫D, diffusion along GB is accompanied by trapping into sites distributed in GB and delay due to sojourn in grains characterized by time distribution (18). In this case, the process is approximately described by the following distributed-order fractional equation: (33)1μbβθb​0CDtβpz,t+D⊥δDμvαθv​0CDtα/2pz,t=Db∂2∂z2pz,t,where ​0CDtβ denotes the fractional Caputo derivative. So, the MSD evolution is characterized by three power laws with exponents β, α/2, and α.Ifβ=1, we have the following scaling:(34)z2=2DbηΓ1+α/2tα/2+2DμvαθvΓ1+αtα,which is well confirmed by Monte Carlo simulation (see Figure 1).Figure 1
The mean square displacement along the grain boundary for different values ofα for subdiffusion in grains in the case of an instantaneous localized source.


## 5. Monte Carlo Verification

The process of diffusion in grains and in GB was simulated as the subordinated Brownian motion. The motion during the operational timeτ (the delocalization time) is modeled according to the Langevin equation (35)r˙=2Dkwτ,where w(τ) is a two-dimensional white noise 〈w(τ)〉=0 and 〈wi(τ)wj(τ′)〉=δijδ(τ-τ′). The diffusion coefficients Dk along the k-th direction depend on the position of the particle (GB or grain). Diffusion in grains is isotropic with coefficient D for the parent process. In GB, the parent Brownian motion along GB is characterized by Db, and across GB the diffusion coefficient D⊥. The displacement for a small interval of the operational time Δτ is determined by the relation Δxk=2DkΔτNk, where Nk are standard normal random variables with zero mathematical expectation and unit variance. After each displacement, the particle can be trapped with probability cb in GB and probability cv in grain volume.Figures1 and 2 demonstrate the Monte Carlo simulation results for the cases β=1 and β=0.5, respectively. In Figure 1, parameters cv=0.2 and cb=0 (i.e., trapping in GB is absent), Db=0.5, Dv=0.002, Δτ=0.3, β=1, δ=1 and different values of subdiffusion exponent α in grains. The figure shows that the dependence (34) is confirmed.Figure 2
The mean square displacement along the grain boundary in the case of localized instantaneous source and subdiffusion in grains (α=0.75) and in GB (β=0.5) for different values of trap concentration c in GB.In Figure2 the parameters are the same, except α=0.75 with cα=0.2, and β=0.5 and different cβ indicated in figure. The influence of traps in GB is amplified for increased cβ and the intermediate asymptotic behavior of the mean squared displacement can be described by the law ∝tβ. GB subdiffusion along z can be approximately considered as subdiffusion with a mixture of localization time distributions characterized by two fractional orders α/2 and β. Suchbehavior can be described by fractional diffusion equations with multifractal memory kernelsconsidered in details in [28].Figure3 shows calculated PDF of the penetration depth along the grain boundary for the case of subdiffusive motion in grains and a localized instantaneous source. It is worthy to note that the tails of these distributions are also successfully approximated by the dependence ∝exp(-Az6/5), well known from the theory of normal diffusion along the grain boundaries [1–3]. The importance of this dependence is that it serves as an indicator of GB diffusion that distinguishes the phenomenon from ordinary (bulk) diffusion. Moreover, this fact is confirmed by the corresponding tails of the symmetric fractional stable density q(z;1,α/2,0), which is an approximate solution of the subordinated diffusion equation. These tails are also straightened out in the coordinates ln⁡p¯ vs z6/5 (Figure 4).Figure 3
Histograms of the penetration depth of impurity particles along GB for subdiffusion in grains from a localized instantaneous source (MC simulation).Figure 4
Tails of symmetric fractional-stable densityqz;1,α/2,0 in the coordinates ‘logp’ vs ‘z’ and their ‘straightening’ in coordinates ‘logp’ vs ‘z6/5’ for different values α of the subdiffusion index in grains.In Figure5, the results of Monte Carlo simulation for the density distribution of the tracer penetration depth along the grain boundary for subdiffusion in grains in the case of a constant distributed source on the surface for different values of α are shown. Other parameters β=1; Db=1, Dv=0.005, D⊥=Dv, Δτ=0.3, δ=1, Np=107, cv=0.2.Figure 5
Monte Carlo trajectories of grain boundary diffusion in disordered granular medium and the distribution of the penetration depth of impurity particles along the grain boundary at subdiffusion in grains in the case of a constant distributed source on the surface for different values ofα. Other parameters β=1; Db=1, Dv=0.005, D⊥=Dv, Δτ=0.3, δ=1, Np=107, cv=0.2.


## 6. Conclusion

Interpreting experimental data with the use of the classical Fisher model sometimes encounters contradictions that can be related to violation of Fick’s law. Here, we generalize the Fisher model to the case of non-Fickian (anomalous) diffusion ubiquitous in disordered materials. The process is formulated in terms of the subdiffusion equations with time-fractional derivatives of orderα∈(0,1] and β∈(0,1] for grain volume and GB, respectively. We found that, for the case of a localized instantaneous source and weak localization in GB (β>α/2), the GB diffusion along z is approximately described by distributed-order subdiffusion with exponents α/2 and β. It is remarkable that tails of the tracer concentration profiles along the z axis are approximately described by dependence ∝exp(-Az6/5) for all 0<α≤1, as in the case of normal GB diffusion, so the 6/5-law itself can serve as an identifier of a more general phenomenon, namely, anomalous GB diffusion.



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