Single-electron turnstile pumping with high frequencies

In this Letter, we present a theoretical analysis to single-electron pumping operation in a large range of driving frequencies through the time-dependent tunneling barriers controlled by external gate voltages. We show that the single-electron turnstile works at the frequency lower than the characteristic frequency which is determined by the mean average electron tunneling rate. When the driving frequency is greater than the characteristic frequency of electron tunnelings, fractional electron pumping occurs as an effect of quantum coherence tunneling.Comment: 4 pages, 5 figure

Dichlorido[(1R,2R)-N-(pyridin-2-yl­methyl)cyclo­hexane-1,2-diamine-κ3 N,N′,N′′]mercury(II)

In the title compound, [HgCl2(C12H19N3)], the HgII ion is coordinated by three N atoms of the (1R,2R)-N-(pyridin-2-ylmeth­yl)cyclo­hexane-1,2-diamine ligand and by a Cl atom in the basal plane, and by a second Cl atom in the apical position, within a distorted square-pyramidal geometry. The coordination of the enanti­opure ligand to the metal atom renders the central N atom chiral with an S configuration, so the complex is enanti­omerically pure and corresponds to the S,R,R diastereoisomer. Mol­ecules are linked via weak N—H⋯Cl hydrogen bonds into a one-dimensional hydrogen-bonding supramolecular chain along the crystallographic b axis

Highly Efficient and Exact Method for Parallelization of Grid-Based Algorithms and its Implementation in DelPhi

The Gauss–Seidel (GS) method is a standard iterative numerical method widely used to solve a system of equations and, in general, is more efficient comparing to other iterative methods, such as the Jacobi method. However, standard implementation of the GS method restricts its utilization in parallel computing due to its requirement of using updated neighboring values (i.e., in current iteration) as soon as they are available. Here, we report an efficient and exact (not requiring assumptions) method to parallelize iterations and to reduce the computational time as a linear/nearly linear function of the number of processes or computing units. In contrast to other existing solutions, our method does not require any assumptions and is equally applicable for solving linear and nonlinear equations. This approach is implemented in the DelPhi program, which is a finite difference Poisson–Boltzmann equation solver to model electrostatics in molecular biology. This development makes the iterative procedure on obtaining the electrostatic potential distribution in the parallelized DelPhi several folds faster than that in the serial code. Further, we demonstrate the advantages of the new parallelized DelPhi by computing the electrostatic potential and the corresponding energies of large supramolecular structures