Asymmetric electrolytes near structured dielectric interfaces.

The ion distribution of electrolytes near interfaces with dielectric contrast has important consequences for electrochemical processes and many other applications. To date, most studies of such systems have focused on geometrically simple interfaces, for which dielectric effects are analytically solvable or computationally tractable. However, all real surfaces display nontrivial structure at the nanoscale and have, in particular, a nonuniform local curvature. Using a recently developed, highly efficient computational method, we investigate the effect of surface geometry on ion distribution and interface polarization. We consider an asymmetric 2:1 electrolyte bounded by a sinusoidally deformed solid surface. We demonstrate that even when the surface is neutral, the electrolyte acquires a nonuniform ion density profile near the surface. This profile is asymmetric and leads to an effective charging of the surface. We furthermore show that the induced charge is modulated by the local curvature. The effective charge is opposite in sign to the multivalent ions and is larger in concave regions of the surface.

Accurate and efficient numerical simulation of dielectrically anisotropic particles.

A variety of electrostatic phenomena, including the structure of electric double layers and the aggregation of charged colloids and proteins, are affected by nonuniform electric permittivity. These effects are frequently ignored in analytical and computational studies and particularly difficult to handle in situations where multiple dielectric contrasts are present, such as in colloids that are heterogeneous in permittivity. We present an extension to the Iterative Dielectric Solver developed by Barros and Luijten [Phys. Rev. Lett. 113, 017801 (2014)] that makes it possible to accurately compute the polarization of anisotropic particles with multiple dielectric contrasts. This efficient boundary-element-method-based approach is applicable to geometries that are not amenable to other solvers, opening the possibility of studying collective phenomena of dielectrically anisotropic particles. We provide insight into the underlying physical reasons for this efficiency.

Dielectric effects on the ion distribution near a Janus colloid.

Spherical Janus colloids, particles with different surface properties on their two hemispheres, are generally heterogeneous in permittivity. This dielectric heterogeneity may affect their behavior in electrolytes and external electric fields, but is typically not taken into account in computational studies. We apply the iterative dielectric solver developed by Barros and Luijten [Phys. Rev. Lett., 2014, 113, 017801] in combination with preconditioning techniques that can accurately and efficiently compute the polarization of dielectrically anisotropic particles. Employing this approach, we systematically study the ion distribution around neutral and charged Janus particles with various permittivities, immersed in symmetric and asymmetric electrolytes. We demonstrate that neutral Janus colloids may carry a nonzero dipole moment in asymmetric salts. For charged Janus colloids, dielectric effects can substantially influence the electric double layer. These findings also have implications for other dielectrically anisotropic entities, such as proteins.

The Real-Valued Sparse Direction of Arrival (DOA) Estimation Based on the Khatri-Rao Product.

There is a problem that complex operation which leads to a heavy calculation burden is required when the direction of arrival (DOA) of a sparse signal is estimated by using the array covariance matrix. The solution of the multiple measurement vectors (MMV) model is difficult. In this paper, a real-valued sparse DOA estimation algorithm based on the Khatri-Rao (KR) product called the L1-RVSKR is proposed. The proposed algorithm is based on the sparse representation of the array covariance matrix. The array covariance matrix is transformed to a real-valued matrix via a unitary transformation so that a real-valued sparse model is achieved. The real-valued sparse model is vectorized for transforming to a single measurement vector (SMV) model, and a new virtual overcomplete dictionary is constructed according to the KR product's property. Finally, the sparse DOA estimation is solved by utilizing the idea of a sparse representation of array covariance vectors (SRACV). The simulation results demonstrate the superior performance and the low computational complexity of the proposed algorithm.

Size-Selective Nanoparticle Assembly on Substrates by DNA Density Patterning.

The vision of nanoscale self-assembly research is the programmable synthesis of macroscale structures with controlled long and short-range order that exhibit a desired set of properties and functionality. However, strategies to reliably isolate and manipulate the nanoscale building blocks based on their size, shape, or chemistry are still in their infancy. Among the promising candidates, DNA-mediated self-assembly has enabled the programmable assembly of nanoparticles into complex architectures. In particular, two-dimensional assembly on substrates has potential for the development of integrated functional devices and analytical systems. Here, we combine the high-resolution patterning capabilities afforded by electron-beam lithography with the DNA-mediated assembly process to enable direct-write grayscale DNA density patterning. This method allows modulation of the functionally active DNA surface density to control the thermodynamics of interactions between nanoparticles and the substrate. We demonstrate that size-selective directed assembly of nanoparticle films from solutions containing a bimodal distribution of particles can be realized by exploiting the cooperativity of DNA binding in this system. To support this result, we study the temperature-dependence of nanoparticle assembly, analyze the DNA damage by X-ray photoelectron spectroscopy and fluorescence microscopy, and employ molecular dynamics simulations to explore the size-selection behavior.

A Modified Rife Algorithm for Off-Grid DOA Estimation Based on Sparse Representations.

In this paper we address the problem of off-grid direction of arrival (DOA) estimation based on sparse representations in the situation of multiple measurement vectors (MMV). A novel sparse DOA estimation method which changes MMV problem to SMV is proposed. This method uses sparse representations based on weighted eigenvectors (SRBWEV) to deal with the MMV problem. MMV problem can be changed to single measurement vector (SMV) problem by using the linear combination of eigenvectors of array covariance matrix in signal subspace as a new SMV for sparse solution calculation. So the complexity of this proposed algorithm is smaller than other DOA estimation algorithms of MMV. Meanwhile, it can overcome the limitation of the conventional sparsity-based DOA estimation approaches that the unknown directions belong to a predefined discrete angular grid, so it can further improve the DOA estimation accuracy. The modified Rife algorithm for DOA estimation (MRife-DOA) is simulated based on SRBWEV algorithm. In this proposed algorithm, the largest and sub-largest inner products between the measurement vector or its residual and the atoms in the dictionary are utilized to further modify DOA estimation according to the principle of Rife algorithm and the basic idea of coarse-to-fine estimation. Finally, simulation experiments show that the proposed algorithm is effective and can reduce the DOA estimation error caused by grid effect with lower complexity.