We report calculations of the Raman and Raman optical activity (ROA) spectra of methyl-β-D-glucose utilizing density functional theory combined with molecular dynamics (MD) simulations to provide an explicit hydration environment. This is the first report of such combination of MD simulations with ROA ab initio calculations. We achieve a significant improvement in accuracy over the more commonly used gas phase and polarizable continuum model (PCM) approaches, resulting in an excellent level of agreement with the experimental spectrum. Modeling the ROA spectra of carbohydrates has until now proven a notoriously difficult challenge due to their sensitivity to the effects of hydration on the molecular vibrations involving each of the chiral centers. The details of the ROA spectrum of methyl-β-D-glucose are found to be highly sensitive to solvation effects, and these are correctly predicted for the first time including those originating from the highly sensitive low frequency vibrational modes. This work shows that a thorough consideration of the role of water is pivotal for understanding the vibrational structure of carbohydrates and presents a new and powerful tool for characterizing carbohydrate structure and conformational dynamics in solution.

We demonstrate that nanotubular networks formed by enzyme-triggered self-assembly of Fmoc-L3 (9-fluorenylmethoxycarbonyl-tri-leucine) show significant charge transport. FT-IR, fluorescence spectroscopy and wide angle X-ray scattering (WAXS) data confirm formation of beta-sheets that are locked together viapi-stacking interactions. Molecular dynamics simulations confirmed the pi-pi stacking distance between fluorenyl groups to be 3.6-3.8 A. Impedance spectroscopy demonstrated that the nanotubular xerogel networks possess minimum sheet resistances of 0.1 MOmega/sq in air and 500 MOmega/sq in vacuum (pressure: 1.03 mbar) at room temperature, with the conductivity scaling linearly with the mass of peptide in the network. These materials may provide a platform to interface biological components with electronics.

Imidazole is a small but important molecule occurring as a structure fragment in systems from amino acids, over ionic liquids, to synthetic polymers. Here we focus on the structure and dynamics of imidazole in water at ambient conditions, using both radial and spatial distribution functions. Molecular dynamics simulations were carried out for various imidazole concentrations, using a traditional point-charge potential and a high-rank multipolar potential. The difference in the description of the electrostatics leads to sizable quantitative differences (e.g., the diffusion coefficient) but also qualitative differences in the local structure. In contrast to a point-charge potential, the multipolar potential favors hydrogen-bonded chainlike imidazole dimers over stacked dimers.

Rigid body molecular dynamics simulations were carried out on pure liquid imidazole at four different temperatures and at 1 atm. Imidazole, which is important both in life science and materials science, is one of the simplest molecules to possess both a lone pair and a π system. These two features are known to benefit from multipolar electrostatics. Here the electrostatic interaction is governed by atomic multipole moments obtained from topologically partitioned ab initio electron densities. The non-electrostatic terms are modeled with Lennard-Jones parameters adjusted to fit the experimental liquid density. All σ values are incrementally increased by one single scaling factor. We report on how the presence of multipolar electrostatics influences the local structure, dynamics and thermodynamics of the liquid compared to electrostatics by atomic point charges. The point charge force field exaggerates the number of π-stacked dimers in the liquid, and underestimates the number of hydrogen-bonded dimers. The effect of the temperature on the local structure of liquid imidazole was analysed using radial and spatial distribution functions.

Digital rock technology and pore-scale physics have become increasingly relevant topics in a wide range of porous media with important applications in subsurface engineering. This technology relies heavily on images of pore space and pore-level fluid distribution determined by X-ray microcomputed tomography (micro-CT). Digital images of pore space (or pore-scale fluid distribution) are typically obtained as gray-level images that first need to be processed and segmented to obtain the binary images that uniquely represent rock and pore (including fluid phases). This processing step is not trivial. Rock complexity, image quality, noise, and other artifacts prohibit the use of a standard processing workflow. Instead, an array of strategies of increasing sophistication has been developed. Typical processing pipelines consist of filtering, segmentation, and postprocessing steps. For each step, various choices and different options exist. This makes selection and validation of an optimum processing pipeline difficult. Using Darcy-scale quantities as a benchmark is not a good option because of rock heterogeneity and different scales of observation. Here, we present a conceptual workflow where noisy images are derived from a ground truth by systematically including typical image artifacts and noise. Artifacts and noise are not simply added to the images. Instead, tomographic forward projection and reconstruction steps are used to incorporate the artifacts in a physically correct way. A proof of concept of this workflow is demonstrated by comparing seven different image-segmentation pipelines ranging from absolute thresholding to a machine-learning approach (Trainable Weka Segmentation). The Trainable Weka Segmentation showed the best performance of the tested methods.