title: Guiding fields for phase separation: controlling Liesegang patterns.
abstract: Liesegang patterns emerge from precipitation processes and may be used to build bulk structures at submicrometer length scales. Thus they have significant potential for technological applications provided adequate methods of control can be devised. Here we describe a simple, physically realizable pattern control based on the notion of driven precipitation, meaning that the phase separation is governed by a guiding field such as, for example, a temperature or pH field. The phase separation is modeled through a nonautonomous Cahn-Hilliard equation whose spinodal is determined by the evolving guiding field. Control over the dynamics of the spinodal gives control over the velocity of the instability front that separates the stable and unstable regions of the system. Since the wavelength of the pattern is largely determined by this velocity, the distance between successive precipitation bands becomes controllable. We demonstrate the above ideas by numerical studies of a one-dimensional system with a diffusive guiding field. We find that the results can be accurately described by employing a linear stability analysis (pulled-front theory) for determining the velocity-local-wavelength relationship. From the perspective of the Liesegang theory, our results indicate that the so-called revert patterns may be naturally generated by diffusive guiding fields.
authors.affiliation: Program for Evolutionary Dynamics, Harvard University, Cambridge, Massachusetts 02138, USA.
authors.forename: T
authors.initials: T
authors.lastname: Antal
authors.forename: I
authors.initials: I
authors.lastname: Bena
authors.forename: M
authors.initials: M
authors.lastname: Droz
authors.forename: K
authors.initials: K
authors.lastname: Martens
authors.forename: Z
authors.initials: Z
authors.lastname: Rácz
mesh_terms: D001703:Biophysics / Q000379:methods*; D055598:Chemical Phenomena; D002627:Chemistry, Physical; D004058:Diffusion; D006863:Hydrogen-Ion Concentration; D015233:Models, Statistical; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D013696:Temperature; D013997:Time Factors