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The electrostatic correlations between ions profoundly influence the structure and forces within electrical double layers. Here, we apply the modified Gaussian Renormalized Fluctuation theory to investigate the counter-intuitive phenomenon of repulsion between two oppositely charged surfaces and discuss its relationship with overcharging. By accurately accounting for the effect of spatially varying ion-ion correlations, we capture these repulsive forces for divalent, trivalent as well as tetravalent ions, in quantitative agreement with reported simulation results. We show that the opposite-charge repulsion is long-ranged with an effective length scale of a few nanometers. The strength of opposite-charge repulsion increases monotonically with the multivalent salt concentration, in stark contrast with the non-monotonic salt concentration dependence of other ion correlation-driven phenomena, such as overcharging and like-charge attraction. We also elucidate that the origin of the opposite-charge repulsion is the large amount of ions attracted to the double layer as a result of ion-ion correlations, leading to higher osmotic pressure and stronger screening of the electrostatic attraction, which results in an overall repulsive force between two oppositely charged surfaces. Furthermore, we demonstrate that there is no causal relationship between opposite-charge repulsion and the overcharging of the surface. Opposite-charge repulsion is accompanied by overcharging at large separation distances, but can also occur in normal double layers without overcharging at intermediate separation distances.

Modeling ion correlations in inhomogeneous polymers and soft matters with spatially varying ionic strength or dielectric permittivity remains a great challenge. Here, we develop a new theory which systematically incorporates electrostatic fluctuations into the self-consistent field theory for polymers. Applied to polyelectrolyte brushes, the theory predicts that ion correlations induce non-monotonic change of the brush height: collapse followed by reexpansion. The scaling analysis elucidates the competition between the repulsive osmotic pressure due to translational entropy and the attraction induced by ion correlations. We also clarify the absence of causal relationship between the brush collapse-reexpansion and the inversion of the surface electrostatic potential. Furthermore, strong ion correlations can trigger microphase separation, either in the lateral direction as pinned micelles or in the normal direction as oscillatory layers. Our theoretical predictions are in good agreement with the experimental results reported in the literature.

The electrostatic double layer force is key to determining the stability and self-assembly of charged colloids and many other soft matter systems. Fully understanding the attractive force between two like-charged surfaces remains a great challenge. Here we apply the modified Gaussian renormalized fluctuation theory to study ion correlation-driven like-charge attraction in multivalent salt solutions. The effects of spatially varying ion correlations on the structure of overlapping double layers and their free energy are self-consistently accounted for. In the presence of multivalent salts, increasing surface charge or counterion valency leads to a short-range attraction. We demonstrate that although both overcharging and like-charge attraction are outcomes of ion correlation, there is no causal relationship between them. Our theory also captures the non-monotonic dependence of like-charge attraction on multivalent salt concentration. The reduction of attraction at high salt concentrations could be a contributing factor towards the reentrant stability of charged colloidal suspensions. Our theoretical predictions are consistent with the observations reported in experiments and simulations.

Understanding overcharging and charge inversion is one of the long-standing challenges in soft matter and biophysics. To study these phenomena, we employ the modified Gaussian renormalized fluctuation theory, which allows for the self-consistent accounting of spatially varying ionic strength, as well as the spatial variations in dielectric permittivity and excluded volume effects. The underlying dependence of overcharging on the electrostatic coupling is elucidated by varying surface charge, counterion valency, and dielectric contrast. Consistent with simulations, three characteristic regimes corresponding to weak, moderate, and strong coupling are identified. Important features like the inversion of zeta potential, crowding and ionic layering at the surface are successfully captured. For weak coupling, there is no overcharging. In the moderate coupling regime, overcharging increases with surface charge. Finally, in the strong coupling regime, ionic crowding and saturation in overcharging are observed. Our theory predicts non-monotonic dependence of charge inversion on multivalent salt concentration as well as the addition of monovalent salt, in quantitative agreement with experiments.

Inhomogeneity of ion correlation widely exists in many physicochemical, soft matter, and biological systems. Here, we apply the modified Gaussian renormalized fluctuation theory to study the classic example of the vapor-liquid interface of ionic fluids. The ion correlation is decomposed into a short-range contribution associated with the local electrostatic environment and a long-range contribution accounting for the spatially varying ionic strength and dielectric permittivity. For symmetric salt, both the coexistence curve and the interfacial tension predicted by our theory are in quantitative agreement with simulation data reported in the literature. Furthermore, we provide the first theoretical prediction of interfacial structure for asymmetric salt, highlighting the importance of capturing local charge separation.

The study of the electrical double layer lies at the heart of colloidal and interfacial science. The standard mean-field Poisson-Boltzmann (PB) theory is incapable of modeling many phenomena originated from ion correlation. An important example is charge inversion or overcharging of electrical double layers in multivalent electrolyte solutions. Existing theories aiming to include correlations cannot capture the nonmonotonic dependence of charge inversion on salt concentration because they have not systematically accounted for the inhomogeneous nature of correlations from surface to the bulk and the excluded volume effect of ions and solvent molecules. In this work, we modify the Gaussian renormalized fluctuation theory by including the excluded volume effect to study ion condensation and charge inversion. A boundary layer approach is developed to accurately model the giant difference in ion correlations between the condensed layer near the surface and the diffuse layer outside. The theory is used to study charge inversion in multivalent electrolytes and their mixtures. We predict a surface charge induced formation of a three-dimensional condensed layer, which is necessary but not sufficient for charge inversion. The value of the effective surface potential is found to depend non-monotonically on the bulk salt concentration. Our results also show a non-monotonic reduction in charge inversion in monovalent and multivalent electrolyte mixtures. Our work is the first to qualitatively reproduce experimental and simulation observations and explains the underlying physics.

Over the past decade, Deep Learning has emerged as a useful and efficient tool to solve a wide variety of complex learning problems ranging from image classification to human pose estimation, which is challenging to solve using statistical machine learning algorithms. However, despite their superior performance, deep neural networks are susceptible to adversarial perturbations, which can cause the network's prediction to change without making perceptible changes to the input image, thus creating severe security issues at the time of deployment of such systems. Recent works have shown the existence of Universal Adversarial Perturbations, which, when added to any image in a dataset, misclassifies it when passed through a target model. Such perturbations are more practical to deploy since there is minimal computation done during the actual attack. Several techniques have also been proposed to defend the neural networks against these perturbations. In this paper, we attempt to provide a detailed discussion on the various data-driven and data-independent methods for generating universal perturbations, along with measures to defend against such perturbations. We also cover the applications of such universal perturbations in various deep learning tasks., Comment: 20 pages, 17 figures