Richard B. Ross

Hildebrand and Hansen solubility parameters from Molecular Dynamics with applications to electronic nose polymer sensors

We introduce the Cohesive Energy Density (CED) method, a multiple sampling Molecular Dynamics computer simulation procedure that may offer higher consistency in the estimation of Hildebrand and Hansen solubility parameters. The use of a multiple sampling technique, combined with a simple but consistent molecular force field and quantum mechanically determined atomic charges, allows for the precise determination of solubility parameters in a systematic way (σ = 0.4 hildebrands). The CED method yields first‐principles Hildebrand parameter predictions in good agreement with experiment [root‐mean‐square (rms) = 1.1 hildebrands]. We apply the CED method to model the Caltech electronic nose, an array of 20 polymer sensors. Sensors are built with conducting leads connected through thin‐film polymers loaded with carbon black. Odorant detection relies on a change in electric resistivity of the polymer film as function of the amount of swelling caused by the odorant compound. The amount of swelling depends upon the chemical composition of the polymer and the odorant molecule. The pattern is unique, and unambiguously identifies the compound. Experimentally determined changes in relative resistivity of seven polymer sensors upon exposure to 24 solvent vapors were modeled with the CED estimated Hansen solubility components. Predictions of polymer sensor responses result in Pearson R2 coefficients between 0.82 and 0.99.

TraPPE-UA force field for acrylates and Monte Carlo simulations for their mixtures with Alkanes and alcohols

An extension of the transferable potentials for phase equilibria-united atom (TraPPE-UA) force field to acrylate and methacrylate monomers is presented. New parameters were fit to the liquid density, normal boiling point, saturated vapor pressure, and (where experimentally available) critical constants of 1,3-butadiene, isoprene, methyl acrylate, and methyl methacrylate using Gibbs ensemble Monte Carlo simulations. Excellent agreement with experiment was obtained for the parametrization compounds and seven additional acrylate and methacrylate compounds, with average errors in liquid density and normal boiling point of approximately 1%. The TraPPE-UA force field also predicts accurate heats of vaporization at 298 K. In addition, Gibbs ensemble Monte Carlo simulations of binary vapor-liquid equilibria for the mixtures methyl acrylate/1-butanol and methyl acrylate/n-decane show that the TraPPE-UA acrylate force field performs well in mixtures with both polar and nonpolar molecules. These simulations also indicate structural microheterogeneity in the liquid phase of these mixtures.

Multiscale simulation methods for nanomaterials

1. Overview of Multi-Scale Simulation Methods for Materials (Sanat S. Mohanty and Richard B. Ross). 2. Influence of Water and Fatty Acid Molecules on Quantum Photoinduced Electron Tunneling in Self-Assembled Photosynthetic Centers of Minimal Protocells (A. Tamulis, V. Tamulis, H. Ziock, and S. Rasmussen). 3. Optimizing the Electronic Properties of Carbon Nanotubes using Amphoteric Doping (Bob G. Sumpter and Vincent Meunier). 4. Using Order and Nanoconfinement to Tailor Semiconducting Polymers - A Combined Experimental and Multiscale Computational Study (Michael L. Drummond, Bob G. Sumpter, Michael D. Barnes, William A. Shelton, Jr., and Robert J. Harrison). 5. Coarse Grain to Atomistic Mapping Algorithm: A Tool for Multiscale Simulations (Steven O. Nielsen, Bernd Ensing, Preston B. Moore, and Michael L. Klein). 6. Microscopic Insights into the Dynamics of Protein-Solvent Mixtures (Taner E. Dirama and Gustavo A. Carri). 7. Mesoscale Simulations of Surface Modified Nanospheres in Solvents (Sanat Mohanty). 8. Fixing Interatomic Potentials Using Multiscale Modeling: ad hoc Schemes for Coupling Atomic and Continuum Simulations (Clifford W. Padgett, J. David Schall, J. Wesley Crill, and Donald W. Brenner). 9. Fully Analytic Implementation of Density Functional Theory for Efficient Calculations on Large Molecules (Rajendra R. Zope and Brett I. Dunlap). 10. Al Nanoparticles: Accurate Potential Energy Functions and Physical Properties (Nathan E. Schultz, Ahren W. Jasper, Divesh Bhatt, J. Ilja Siepmann, and Donald G. Truhlar). 11. Large-scale Monte Carlo Simulations for Aggregation, Self-Assembly and Phase Equilibria (Jake L. Rafferty, Ling Zhang, Nikolaj D. Zhuravlev, Kelly E. Anderson, Becky L. Eggimann, Matthew J. McGrath, and J. Ilja Siepmann). 12. New QM/MM Models for Multi-scale Simulation of Phosphoryl Transfer Reactions in Solution (Kwangho Nam, Jiali Gao, and Darrin M. York). 13. Modeling the Thermal Decomposition of Large Molecules and Nanostructures (Marc R. Nyden, Stanislav I. Stoliarov, and Vadim D. Knyazev). 14. Predicting Dynamic Mesoscale Structure of Commercially Relevant Surfactant Solutions (Fiona Case).

Adsorption, X-ray Diffraction, Photoelectron, and Atomic Emission Spectroscopy Benchmark Studies for the Eighth Industrial Fluid Properties Simulation Challenge

The primary goal of the eighth industrial fluid properties simulation challenge was to test the ability of molecular simulation methods to predict the adsorption of organic adsorbates in activated carbon materials. The challenge focused on the adsorption of perfluorohexane in the activated carbon standard BAM-P109. Entrants were challenged to predict the adsorption of perfluorohexane in the activated carbon at a temperature of 273 K and at relative pressures of 0.1, 0.3, and 0.6. The relative pressure (P/Po) is defined as that relative to the bulk saturation pressure predicted by the fluid model at a given temperature (273 K in this case). The predictions were judged by comparison to a set of experimentally determined values, which are published here for the first time and were not disclosed to the entrants prior to the challenge. Benchmark experimental studies, described herein, were also carried out and provided to entrants in order to aid in the development of new force fields and simulation methods to be employed in the challenge. These studies included argon, carbon dioxide, and water adsorption in the BAM-P109 activated carbon as well as X-ray diffraction, X-ray microtomography, photoelectron spectroscopy, and atomic emission spectroscopy studies of BAM-P109. Several concurrent studies were carried out for the BAM-P108 activated carbon. These are included in the current manuscript for comparison.

The Eighth Industrial Fluids Properties Simulation Challenge

The goal of the eighth industrial fluid properties simulation challenge was to test the ability of molecular simulation methods to predict the adsorption of organic adsorbates in activated carbon materials. In particular, the eighth challenge focused on the adsorption of perfluorohexane in the activated carbon BAM-P109. Entrants were challenged to predict the adsorption in the carbon at 273 K and relative pressures of 0.1, 0.3, and 0.6. The predictions were judged by comparison with a benchmark set of experimentally determined values. Overall, good agreement and consistency were found between the predictions of most entrants.