Trajectory-Extending Kinetic Monte Carlo Simulations to Evaluate Pure and Gas Mixture Diffusivities through a Dense Polymeric Membrane.

With renewed interest in CO2 separations, carbon molecular sieving (CMS) membrane performance evaluation requires diffusion coefficients as inputs to have a reliable estimate of the permeability. An optimal material is desired to have both high selectivity and permeability. Gases diffusing through dense CMS and polymeric membranes experience extended subdiffusive regimes, which hinders reliable extraction of diffusion coefficients from mean squared displacement data. We improve the sampling of the diffusive landscape by implementing the trajectory-extending kinetic Monte Carlo (TEKMC) technique to efficiently extend molecular dynamics (MD) trajectories from ns to µs time scales. The obtained self-diffusion coefficient of pure CO2 in CMS membranes derived from a 6FDA/BPDA-DAM precursor polymer melt is found to linearly increase from 0.8-1.3 × 10-6 cm2 s-1 in the pressure range of 1-20 bar, which supports previous experimental findings. We also extended the TEKMC algorithm to evaluate the mixture diffusivities in binary mixtures to determine the permselectivity of CO2 in CH4 and N2 mixtures. The mixture diffusion coefficient of CO2 ranges from 1.3-7 × 10-6 cm2 s-1 in the binary mixture CO2/CH4, which is significantly higher than the pure gas diffusion coefficient. Robeson plot comparisons show that the permselectivity obtained from pure gas diffusion data is significantly lower than that predicted using mixture diffusivity data. Specifically, in the case of the CO2/N2 mixture, we find that using mixture diffusivities led to permselectivities lying above the Robeson limit highlighting the importance of using mixture diffusivity data for an accurate evaluation of the membrane performance. Combined with gas solubilities obtained from grand canonical Monte Carlo (GCMC) simulations, our work shows that simulations with the TEKMC method can be used to reliably evaluate the performance of materials for gas separations.

Efficient Removal of Uranyl Ions Using PAMAM Dendrimer: Simulation and Experiment.

In this work, using atomistic molecular dynamics (MD) simulations and polymer-assisted ultrafiltration experiments, we explore the adsorption and removal of uranyl ions from aqueous solutions using poly(amidoamine) (PAMAM) dendrimers. The effects of uranyl ion concentration and the pH of the solution were examined for PAMAM dendrimers of generations 3, 4, and 5. Our simulation results show that PAMAM has a high adsorption capacity for the uranyl ions. The adsorption capacity increases with increasing concentration of uranyl ions for all 3 generations of PAMAM in agreement with experimental findings. We find that the number of uranyl ions bound to PAMAM is significantly higher in acidic solutions (pH < 3) as compared to neutral solutions (pH ∼ 7) for all uranyl ion concentrations. Additionally, we find an increase in the number of adsorbed uranyl ions to PAMAM with the increase in the dendrimer generation. This increase is due to the greater number of binding sites present for higher-generation PAMAM dendrimers. Our simulation study shows that nitrate ions form a solvation shell around uranyl ions, which allows them to bind to PAMAM binding sites, including the amide, amine, and carbonyl groups. In polymer-assisted ultrafiltration (PAUF) experiments, the removal percentage of uranyl ions by G3 PAMAM dendrimer increased from 36.3% to 42.6% as the metal ion concentration increased from 2.1 × 10-5 M to 10.5 × 10-5 M at a pH of 2. Our combined experiment and simulation study suggests that PAMAM is an effective adsorbent for removing uranyl ions from aqueous solutions.

Does twist angle affect the properties of water confined inside twisted bilayer graphene?

Graphene nanoslit pores are used for nanofluidic devices, such as, in water desalination, ion-selective channels, ionic transistors, sensing, molecular sieving, blue energy harvesting, and protein sequencing. It is a strenuous task to prepare nanofluidic devices, because a small misalignment leads to a significant alteration in various properties of the devices. Here, we focus on the rotational misalignment between two parallel graphene sheets. Using molecular dynamics simulation, we probe the structure and dynamics of monolayer water confined inside graphene nanochannels for a range of commensurate twist angles. With SPC/E and TIP4P/2005 water models, our simulations reveal the independence of the equilibrium number density- n ∼ 13 nm-2 for SPC/E and n ∼ 11.5 nm-2 for TIP4P/2005- across twists. Based on the respective densities of the water models, the structure and dielectric constant are invariant of twist angles. The confined water structure at this density shows square ice ordering for SPC/E water only. TIP4P/2005 shows ordering at the vicinity of a critical density (n ∼ 12.5 nm-2). The average perpendicular dielectric constant of the confined water remains anomalously low (∼2 for SPC/E and ∼6 for TIP4P/2005) for the studied twist angles. We find that the friction coefficient of confined water molecules varies for small twist angles, while becoming independent for twists greater than 5.1°. Our results indicate that a small, angular misalignment will not impair the dielectric properties of monolayer water within a graphene slit-pore, but can significantly influence its dynamics.

Intercorrelation of Major DNA/RNA Sequence Descriptors - A Preliminary Study.

A large number of alignment-free techniques of graphical representation and numerical characterization (GRANCH) of bio-molecular sequences have been proposed in the recent past years, but the relative efficacy of these methods in determining the degree of similarities and dissimilarities of such sequences have not been ascertained. OBJECTIVE: Our objective is to make an assessment of the relative efficacy of these methods in determining the degree of similarities and dissimilarities of bio-molecular sequences. METHOD: We have chosen 7 published/communicated methods that represent various classes of GRANCH techniques and computed the descriptors that are expected to characterize similarities and dissimilarities in several sets of gene sequences. We critically appraise the different methods and determine which of these yield non-redundant structural information that could be used to compute different properties of the sequences, and which are correlated enough to one another so that using the simplest representative of the group would suffice. We also do a principal component analysis (PCA) to determine how the variances in the calculated sequence descriptors are explained by the computed principal components (PCs). RESULTS: We found that some of the descriptors are strongly correlated implying a commonality of structural information encoded by them while others are distinctly separate. The PCA results show that the first three PC's explain >97% of the variances. CONCLUSION: We found that some mathematical DNA descriptors calculated by a few of these techniques correlate strongly with one another implying a redundancy in the structural information quantified by those descriptors; others are not strongly correlated with one another suggesting that they encode non-redundant sequence information. From this and our PCA results, our recommendation would be to use minimally correlated set of descriptors or orthogonal descriptors like PCs derived from the descriptor set for the characterization of nucleic acid structure and function.