Parameters for the plots of and in Figure 7.
From the formation of lightning-paths to vascular networks, diverse nontrivial self-organizing and self-assembling processes of pattern formation give rise to intricate structures everywhere and at all scales in nature, often referred to as fractals. One striking feature of these disordered growth processes is the morphological transitions that they undergo as a result of the interplay of the entropic and energetic aspects of their growth dynamics that ultimately manifest in their structural geometry. Nonetheless, despite the complexity of these structures, great insights can be obtained into the fundamental elements of their dynamics from the powerful concepts of fractal geometry. In this chapter, we show how numerical and theoretical fractal analyses provide a universal description to the well observed fractal to nonfractal morphological transitions in particle aggregation phenomena.
- entropic/energetic forces
- fractal growth
- morphological transitions
In nature, fractal structures emerge in a wide variety of systexms as a local optimization of diverse growth processes restricted to the entropic and energetic inputs from the environment. Even more, the fractality of these systems determines many of their physical, chemical, and/or biological properties. Thus, to comprehend the mechanisms that originate and control the fractality is highly relevant in many areas of science and technology [1–3].
One of the most successful approaches to this problem employs stochastic growth processes of particle aggregation. In general, aggregation phenomena are out-of-equilibrium processes of fractal pattern formation that are ubiquitous in nature . As such, since the introduction of the diffusion-limited aggregation (DLA) and ballistic aggregation (BA) models, a plethora of studies has been developed trying to understand the ultimate aspects of the aggregation dynamics that give rise to
In particular, one striking feature of these systems is the morphological transition that they undergo as a result of the interplay of the entropic and energetic aspects of their growth dynamics that ultimately manifest themselves in the geometry of their structure . It is here, where despite of their complexity, great insight can be obtained into the fundamental elements of their dynamics from the powerful concepts of fractal geometry [6, 7].
One example of this is the well-known dielectric breakdown model (DBM) or generalized Laplacian growth model, which has importantly contributed to our understanding of far-from-equilibrium growth phenomena, to such extent that seemingly unrelated patterns found in nature, as river networks or bacterial colonies, are understood in terms of a single framework of complex growth [8, 9]. However, we are still in need for a complete scaling theory of growth for systems far-from-equilibrium, as well as a comprehensive description of the fractality of systems that exhibits fractal to nonfractal morphological transitions .
In this chapter, starting from the mean-field result for the fractal dimension of Laplacian growth, we present a theoretical framework for the study of these transitions. Using a statistical approach to fundamental particle-cluster aggregation dynamics, under which it is possible to create four nontrivial fractal to nonfractal transitions that will capture all the main features of fractal growth, we show that, regardless of their space symmetry-breaking mechanism, they are well described by a universal dimensionality function, including the Laplacian one.
In order to show this, we consider the following: first, we introduce a general dimensionality function that is able to describe the measured fractal dimensions and scaling of clusters generated form particle aggregation. Second, we apply this equation to a set of fractal to nonfractal morphological transitions, created by identifying the fundamental dynamics that drive the fractal growth in particle aggregation and by combining three fundamental
2. Fundamental models
In the Laplacian theory of growth, the growth probability at a given point in space, , is given by the spatial variation of a scalar field, , i.e., . An example of such processes is the paradigmatic DLA model, where particles randomly aggregate one-by-one to a seed particle to form a cluster [8–10] (Figure 1). It has been found that the structure that emerges from this process exhibits self-similar properties described by a fractal dimension, , only dependent on the Euclidean dimension, , of its embedding space [13, 14] given by,
For , this expression predicts , different from the widely reported and numerically obtained value for off-lattice DLA, . Furthermore, it was found that is highly dependent on the mean square displacement of the particles’ trajectories, giving rise to a continuous screening-driven morphological transition that has been neatly described by extending the Laplacian theory to consider a general process where particles follow fractal trajectories . It was found that is related to the dimension of the walkers’ trajectories, , through the Honda-Toyoki-Matsushita (HTM) mean-field equation [16, 17]:
Here, for one gets , as expected for ballistic-aggregation dynamics (see Figure 1), whereas for , the value for DLA is recovered. This BA-DLA transition has been reproduced in diverse and equivalent aggregation schemes, e.g., of wandering particles under drift , or with variable random-walk step size , by imposing directional correlations [20, 21], and through probabilistically mixed aggregation dynamics .
However, one of the most challenging aspect of the theory comes when the growth is not purely limited by diffusion, e.g., when it takes place under the presence of long-range attractive interactions, where strong screening and anisotropic effects must be considered [1, 5, 7]. In this case, a clever generalization to the Laplacian growth process was proposed within the context of the DBM, assuming , where is a positive real number that keeps the information associated with all effects coming from screening and anisotropy [23, 24]. This process generates a characteristic fractal to nonfractal morphological transition from a compact structure with when , through DLA at , to a linear one with , as [25, 26]. In this scenario, the generalized HTM equation [17, 27], given as,
provides a good approximation to the dimensions of this transition but due to its mean-field limitations, it does not have a good correspondence with the numerical results [25, 26]. Nonetheless, as shown here, Eq. (3) is the starting point to clarify this aspect of the theory and, even, to establish a suitable and general framework to analyze more complex morphological transitions in stochastic growth processes.
This is done by considering that the fundamental dynamical elements of aggregation, which drive the fractal growth, are mainly two: a stochastic one, coming from the particles’ trajectories randomness, and an energetic one, coming from attractive interactions. With regard to the latter, there are two physical mechanisms related to these interactions and two models that are able to reproduce their effects. First, the model we will refer to as -model , incorporates the screening effects associated to long-range attractive interactions (such as those coming from an attractive radial potential) by means of an effective interaction radius , as illustrated in Figure 2a–c. Second, the model referred here to as the -model , incorporates anisotropy effects coming from surface-tension-like interactions by means of a Monte Carlo approach to aggregation using fundamental stochastic and energetic models as explained below and illustrated in Figure 2d. Therefore, by controlling the interplay of any of these two mechanisms with a pure stochastic model (in this case the DLA or BA models), one is able to generate fractal to nonfractal morphological transitions.
In the following and as explained below, all data for were measured over a large ensemble of clusters (with particles) for each value of the control parameters of the models proposed, by means of two standard methods: the two-point density correlation function, , and the radius of gyration, , where the scaling exponents are related to as , and .
3.1. Aggregation dynamics
In all of the numerical calculations, we choose as a unit of distance, the particles’ diameter here is set to one. For generating aggregates based on BA or MF (Figure 1a and 1c), a standard procedure was used in which particles are launched at random, with equal probability in position and direction of motion, from a circumference of radius , where is the distance of the farthest particle in the cluster with respect to the seed particle at the origin. As well, we used particle diameters to avoid undesirable screening effects. On the other hand, for the MF model, particles always aggregate to the closest particle in the cluster. This is determined by projecting the position of the aggregated particles along the direction of motion of the incoming particle (see Figure 1c). Finally, in the case of aggregates generated using DLA dynamics (Figure 1b), particles were launched from a circumference of radius with , while their mean free path was set to one particle diameter in the beginning. Further on, as typically done, their mean free path is modified as the particles wander beyond a distance larger than or in-between branches. As well, a killing radius is set at in order to speed up the aggregation process. For the special case of the -model, for aggregates generated with DLA dynamics, particles were launched from a circle of radius , with .
Regarding the -model, in order to mix different aggregation dynamics, a Monte Carlo scheme of aggregation is implemented using the BA, DLA, and MF models. The combination between pairs of models results in the DLA-MF and BA-MF transitions by varying the mixing parameter . This parameter is associated with the probability or fraction of particles aggregated under MF dynamics, , where is total number of particles in the cluster. Therefore, as varies from (pure stochastic dynamics given by the BA or DLA dynamics) to (purely energetic dynamics given by the MF model), it generates two transitions discussed below. The evaluation of the aggregation scheme to be used is only updated once and the particle has been successfully aggregated to the cluster under a given dynamics.
3.2. Fractal and scaling analysis
In all measurements, we performed an ensemble average over clusters containing particles each. In first place, we measured the fractal dimension from the two-point density correlation function,
where the double bracket indicates an average over all possible origins and all possible orientations. Here, it is assumed that , where the fractal dimension is given by with being the dimension of the embedding space. In second place, we also measured the radius of gyration given by
where is the number of particles, is the position of the
In particular, for the -model, linear-fits at different scales were performed in order to capture the main local fractal features. In addition, we averaged the results of 10 linear fits, distributed over a given interval, in order to improve the precision of the measurements. In both transitions, DLA-MF and BA-MF, was measured at short length-scales (regions in Figure 6) over the interval with fitting-length equal to 10, and (in particle diameters units). At long length-scales (), over with fitting-length equal to 40, and . For , measurements at medium scales () were performed over the interval with fitting-length equal to 104, and (in particle number). Finally, at large scales (), over the interval with fitting-length equal to , and .
4. Fractality prescriptions
Despite the complexity leading to morphological transitions, simple models can be established to describe their fractality or scaling as a function of the control parameter, in our case, the branching parameter for the -model (see below) or the mixing parameter for the -model. To do so, let us start by showing that Eq. (3) can in fact be recovered as the first-order approximation in of a general exponential form,
with . It easily follows that,
In this form, by setting , Eq. (3) can be fully recovered. Therefore, it is clear that in Eq. (6) keeps all the information associated with the structural symmetry-breaking of the clusters in the DBM. Under this formalism, let us introduce with control parameter , which takes a similar role as , i.e., it is associated with the net effect of all screening/anisotropy-driven forces of a more complex growth process, not necessarily corresponding to the DBM description. For simplicity, we propose , where is the parameter that controls the transition, and where and are two positive real numbers associated with the strength of screening/anisotropy-driven forces, that are to be determined either theoretically or phenomenologically according to the studied transition. This allows us to define a general dimensionality function, , that describes the fractal dimension of a structure collapsing toward under the effects of as,
where , with , is the fractal dimension of the clusters for . This equation predicts an inflection point at , given by , which defines the change in dynamical growth regimes. Additionally, the first-order approximation of Eq. (8), is,
Furthermore, let us introduce the reduced parameter, . Analytically, substituting , back into Eq. (8) leads to,
is an effective parameter associated to a generalized screening/anisotropy-driven force. Its first-order approximation is then,
where the effective parameter is now given as,
With this prescription, the dynamical change in growth regime is now located at for all transitions. Here, we can also include the DBM transition as well, for which .
5. Morphological transitions
5.1. The -model: screening-driven transition
In the first approach to morphological transitions, we will consider the case when long-range attractive interactions are introduced in the growth dynamics. In this case, the way to obtain self-similar clusters, that is, clusters with a single fractal dimension, is to maintain a proper balance between the energetic and entropic contributions to the growth process. This can be done by considering an aggregation radius, , associated with the range of the interaction for each particle in the growing cluster.
For example, for , or
It can be appreciated that this growth presents three well-defined stages as illustrated in Figures 3d and 4d. In the first one, the growth is limited by the interactions and is characterized by as . This is due to the fact that the radial size of the cluster is small compared to . In consequence, the individual interaction regions of the aggregated particles are highly overlapped, forming an almost circular envelope or effective boundary of aggregation around the cluster. This makes the last aggregated particle the most probable aggregation point in the cluster for the next incoming particle. Because of this, there is a tendency for the clusters to develop three main arms or branches, clearly seen as . This structural feature is reminiscent of a mean-field (MF) behavior. In the second stage, clusters exhibit a transition in growth dynamics. Here, the envelope starts to develop small deviations from its initial circular form, with typically three main elongations or growth instabilities associated with the main branches. When the distance between the tips of the two adjacent branches becomes of the order of , a bifurcation process begins, generating multiscaling growth. Then, when the interactive envelope develops a branched structure itself, particles are able to penetrate into the inner regions of the aggregate and another transition in growth dynamics takes place, from
However, taking into account that the spatial size of the clusters is proportional to the radius of gyration , the desired balance between entropic and energetic forces, the latter related to the long-range attractive interaction and to the parameter , can be achieved by scaling the interaction range itself with the number of particles in the cluster through , where is fixed to one, while is the scaling parameter that takes values in [0, 1]; we will refer as the
Additionally, this model allows one to estimate , in order to grow aggregates with any prescribed fractal dimension in , once the underlying entropic model, DLA or BA, is selected. As such, we are no longer restricted to the purely entropic models of fractal growth with a constant , as the energetic contribution of the long-range attractive interactions is maintained through the varying , enabling one to explore in a continuous manner the full range of clusters with fractal dimensions in . Nonetheless, the purely entropic contribution of the underlying models (DLA or BA) has two important contributions to the clusters’ structure: first, they establish an upper limit to the fractal dimensionality (), and second, they define a characteristic morphology to the clusters (that of DLA or BA). This kind of control over the clusters’ fractal dimension and the range it spans, as well as over the morphology of the clusters, has not been obtained before under any other related scheme of fractality tuning .
5.2. The -model: anisotropy-driven transition
In the second approach, a general stochastic aggregation process can be model under a Monte Carlo scheme involving three fundamental and simple
6. Universal description
It is necessary to remark that the DLA-MF and BA-MF transitions in the -model are characterized by inhomogeneous clusters, i.e., structures with nonconstant scaling as shown in Figure 6c and d, in contrast with the ones present in BA-DLA [15, 21] and the DBM [23, 25] characterized by monofractals. These multiscaling features reveal a crossover behavior that can be properly quantified by measuring a local or effective, , at different scales , as shown in Figure 7a (details for the values of the parameter used to produce Figure 7 are presented in Table 1). Analytically, all measurements can be described by Eqs. (8) and (9), using and as fitting parameters. Indeed, the data for as obtained through to are very well described by Eq. (8), whereas Eq. (9) better describes the results obtained through . In the case of the -model, the BA/DLA-MF transitions are governed by the branching parameter, , that is equivalent to the mixing parameter of the -model. Nonetheless, in the -model, the clusters exhibit a monofractal behavior all along the transition as measured by . Thus, the data obtained are then described by Eq. (8) as a fitting function. This analysis is presented in Figure 7d.
|DLA-MF||103 to 105||6.10||1.52||1.70||0.21|
|BA-MF||103 to 105||6.35||1.43||1.95||0.19|
By observing the description of the transitions based on the function in Figure 7, one can clearly appreciate their continuous nature for both the - and -models, as well as the fact that defines a change in aggregation dynamics from purely entropy to highly energetic. In particular, the similarity between the transitions of BA-MF and those of the DBM is quite interesting, with results such as for and for in the BA-MF transitions, while for and for in the case of the DBM, even though the processes are different (see Table 2). Even more, by plotting all data as a function of itself, i.e., , the DLA-MF, BA-MF, and DBM transitions approach the highly anisotropic regime in an almost identical manner, departing from Eqs. (10) and (12). See, for example, Figure 7c and the bottom pane in Figure 7d.
A final important implication of the previous findings is that the DBM and BA-MF transitions (for both - and -models), even though completely different in origin, could be treated as belonging to the same universality class. To understand this, we must recall that the DBM () and viscous fingering phenomena are said to belong to the same universality class as DLA, because they are all characterized by [10, 29]. Therefore, by extending this idea to the description with the function of Eq. (10), the
where the effective parameter is, respectively, given by Eqs. (11) and (13) for each of the previous two equations. Notice that, since the morphological transitions presented in this work are independent of their symmetry-breaking processes, initial-configuration, and that is given by the HTM equation, we arrive to the important conclusion that, under the formalism based on Eqs. (14) and (15), fractal to nonfractal morphological transitions will follow the same curves independently of the Euclidean dimension of the embedding space, as shown in Figure 8. This finding makes it clear that it is possible to define
It has been stated above that the entropic and energetic elements are the two aspects of the complex aggregation dynamics which in nature are strongly correlated. Nonetheless, this reductionist approach that essentially encapsulates the information of all the finer details of the dynamics into an effective interaction (in the -model, for example) or through a Monte Carlo approach to aggregation (as in the -model) has proven to be quite rewarding, as one can appreciate the wide assortment of fractal morphologies that can be generated and the fine and easy control one can achieve by means of a single parameter. Here, we shall recall that in two-dimensional systems (), by changing the fractal dimension of the particle trajectories, , from (random) to 1 (ballistic), it is possible to generate a complete set of clusters with fractal dimension between (DLA) to 2 (BA), corresponding to the stochastic (entropic) regime. However, by scaling the interaction range with , or by gradually introducing an energetic element trough MF dynamics, we are no longer restricted to this range in as we were. We can now explore the full set of fractals with in , where ranges from that value corresponding to DLA to that corresponding to BA, not necessarily bound to , since these approaches can be easily extended to higher dimensions .
Additionally, the descriptive framework for the scaling of fractal to nonfractal morphological transitions in stochastic growth processes, which includes the concept of an effective screening/anisotropy force and reduced codimensionality transformations, has revealed that the DLA-MF, BA-MF, and DBM transitions exhibit a well-defined universal scaling , which is independent of the initial fractal configuration of the system, the dimensionality of the embedding space, crossover effects, and the anisotropy force acting upon them.
The results and models discussed in this chapter represent an important unifying step toward a complete scaling theory of fractal growth and far-from-equilibrium pattern formation. Additionally, the possibility of applying the dimensionality function to discuss complex structures in other research areas, ranging from biology [4, 1], intelligent materials engineering [31, 32] to medicine , seems to be in some cases straightforward.
The authors gratefully acknowledge the computing time granted on the supercomputers MIZTLI (DGTIC-UNAM) and THUBAT-KAAL (CNS-IPICyT) and on XIUHCOATL (CINVESTAV) through M.A. Rodriguez (ININ, Mexico). We acknowledge the partial financial support by CONACyT and VIEP-BUAP through the grants: 257352, DIRV-EXC16-I, and CAEJ-EXC16-G.