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Nonequilibrium pulling simulations have been a useful approach for investigating a variety of physical and biological problems. The major target in the simulations is to reconstruct reliable potentials of mean force (PMFs) or unperturbed free-energy profiles for quantitatively addressing both equilibrium mechanistic properties and contributions from nonequilibrium processes. While several current nonequilibrium methods were shown to be accurate in computing free-energy profiles in systems with relatively simple dynamics, they have proved to be unsuitable in more complicated systems. To extend the applicability of nonequilibrium sampling, we demonstrate a novel method that combines Minh-Adib’s bidirectional estimator with nonlinear WHAM equations to reconstruct and assess PMFs from relatively fast pulling trajectories. We test the method in a one-dimensional model system and in a system of an antibiotic gramicidin-A (gA) channel, which is considered a significant challenge for nonequilibrium sampling. We identify key parameters for efficiently performing pulling simulations to improve and ensure the convergence and accuracy of estimated PMFs. We show that a few pulling trajectories of a relatively fast pulling speed v= 10 Å/ns can return a fair estimate of the PMF of a single potassium ion in gA.

Since the discovery of graphene, growth of two-dimensional (2D) nanomaterials has greatly attracted attention. However, spontaneous growth of atomic two-dimensional (2D) materials is limitedly permitted for several layered-structure crystals, such as graphene, MoS2, and h-BN, and otherwise it is notoriously difficult. Here we report the gas-phase 2D growth of silicon (Si), that is cubic in symmetry, viadendritic growth and an interdendritic filling mechanism and to form Si nanosheets (SiNSs) of 1 to 13 nm in thickness. Thin SiNSs show strong thickness-dependent photoluminescence in visible range including red, green, and blue (RGB) emissions with the associated band gap energies ranging from 1.6 to 3.2 eV; these emission energies were greater than those from Si quantum dots (SiQDs) of the similar sizes. We also demonstrated that electrically driven white, as well as blue, emission in a conventional organic light-emitting diode (OLED) geometry with the SiNS assembly as the active emitting layers. Tunable light emissions in visible range in our observations suggest practical implications for novel 2D Si nanophotonics.

We synthesized free-standing Si nanosheets (NSs) with a thickness of about <2 nm using a chemical vapor deposition process and studied their optical properties. The Si NSs were formed by the formation of frameworks first along six different ⟨110⟩ directions normal to [111], its zone axis, and then by filling the spaces between the frameworks along the ⟨112⟩ directions under high flow rate of processing gas. The Si NSs showed blue emission at 435 nm, and absorbance and photoluminescence (PL) excitation measurements indicate that enhanced direct band transition attributes to the emission. Time-resolved PL measurement, which showed PL emission at 435 nm and a radiative lifetime of 1.346 ns, also indicates the enhanced direct band gap transition in these Si NSs. These outcomes indicate that dimensionality of Si nanostructures may affect the band gap transition and, in turn, the optical properties.

We present a new method for multiple sequence alignment (MSA), which we call MSACSA. The method is based on the direct application of a global optimization method called the conformational space annealing (CSA) to a consistency-based score function constructed from pairwise sequence alignments between constituting sequences. We applied MSACSA to two MSA databases, the 82 families from the BAliBASE reference set 1 and the 366 families from the HOMSTRAD set. In all 450 cases, we obtained well optimized alignments satisfying more pairwise constraints producing, in consequence, more accurate alignments on average compared with a recent alignment method SPEM. One of the advantages of MSACSA is that it provides not just the global minimum alignment but also many distinct low-lying suboptimal alignments for a given objective function. This is due to the fact that conformational space annealing can maintain conformational diversity while searching for the conformations with low energies. This characteristics can help us to alleviate the problem arising from using an inaccurate score function. The method was the key factor for our success in the recent blind protein structure prediction experiment.