Field theory for size- and charge-asymmetric primitive model of ionic systems: Mean-field stability analysis and pretransitional effects

Abstract

The primitive model of ionic systems is investigated within a field-theoretic description for the whole range of size-, λ, and charge, Z, ratios of the two ionic species. Two order parameters (OP) are identified, and their relations to physically relevant quantities are described for various values of λ and Z. Instabilities of the disordered phase associated with the two OP's are determined in the mean-field approximation (MF). In MF a gas-liquid separation occurs for any Z and λ = 1. In addition, an instability with respect to various types of periodic ordering of the two kinds of ions is found. Depending on λ and Z, one or the other transition is metastable in different thermdynamic states. The instabilities found in MF represent weak ordering of the majority of the instantaneous states, and are identified with structural loci associated with pretransitional effects.

Simple theory for oscillatory charge profile in ionic liquids near a charged wall

“…The theory is based on the mesoscopic theory for ionic systems developed for the bulk in Refs. [15][16][17][18][19][20].…”

“…In Ref. [17] the expansion in (20) was truncated at n = 2. From (20) and (11)- (15) we obtain the following approximation for the excess grand potential…”

Phase equilibria of size- and charge-asymmetric primitive models of ionic fluids: the method of collective variables

“…It reflects the fact that the first moment Stillinger-Lovett rule is satisfied. It is worth noting that equationε 2 (k = 0) = 0 (see (10)) leads us to the same expression for gas-liquid spinodal as that obtained in [30] but for another type of regularization of the Coulomb potential inside the hard core.…”

Mean-Field Theory of Meta-learning

“…Changes between phases of the system are induced by some external factors that can be modeled as a bias added to the local fields. Mean field theory was successfully used in a plenty of physical problems, such as a super-fluid effects [54], weakly interacting Bose gas in external field [55][56][57][58][59], quantum solitons in optical fibers [60,61] and many others [62][63][64][65][66][67][68] [69]. This approach allows for unsupervised protein-protein interactions prediction, taking different route, namely unsupervised meta-learning based on cellular automata and phase transitions than previous methods [70][71][72][73][74].…”