New QSPRs for Liquid Heat Capacity.

Quantitative Structure-Property Relationships (QSPRs) have found applications in many areas of chemistry and engineering as effective prediction methods. QSPRs use molecular descriptors to simplify complex molecular properties to a single value and have been used extensively for constant value properties. Liquid heat capacity ( cpl ) is another property where QSPRs can be helpful prediction tools. Researchers have shown strong correlation between the cpl and various molecular descriptors, but these predictions are limited to a single temperature, usually 298.15 K. Additionally, other QSPRs have had problems with oxygen-containing functional groups. In this work, QSPRs for cpl at various temperatures were developed using data selected from the DIPPR database using a novel search method. This method improves on existing QSPRs for cpl by using unique descriptors but does not overcome the issue of oxygen-containing species.

Development of bioreactors for comparative study of natural attenuation, biostimulation, and bioaugmentation of petroleum-hydrocarbon contaminated soil.

Bioremediation of soil and groundwater sites contaminated by petroleum hydrocarbons is known as a technically viable, cost-effective, and environmentally sustainable technology. The purpose of this study is to investigate laboratory-scale bioremediation of petroleum-hydrocarbon contaminated soil through development of eight bioreactors, two bioreactors for each bioremediation mode. The modes were: (1) natural attenuation (NA); (2) biostimulation (BS) with oxygen and nutrients; (3) bioaugmentation (BA) with hydrocarbon degrading isolates; (4) a combination of biostimulation and bioaugmentation (BS-BA). Total petroleum hydrocarbons (TPH) mass balance over the bioreactors showed about 2% of initial 20,000mgkg-soil-1 TPH was removed by advection due to synthetic groundwater which was flowing through the soil, and the rest of decrease in TPH was caused by biodegradation. The BS-BA mode showed the highest TPH biodegradation percentage (89.7±0.3%) compared to the NA (51.4±0.6%), BS (81.9±0.3%) and BA (62.9±0.5%) modes. Furthermore, an increase in microbial population was another evidence of TPH biodegradation by microorganism. Reaction rate data from each bioremediation mode were fitted with a first-order reaction rate model. The Monod kinetic constants including maximum specific growth rate of microorganisms (µmax) and substrate concentration at half-velocity constant (Ks) were estimated for each bioremediation modes.

Uncertainty quantification and propagation of errors of the Lennard-Jones 12-6 parameters for n-alkanes.

Molecular simulation has the ability to predict various physical properties that are difficult to obtain experimentally. For example, we implement molecular simulation to predict the critical constants (i.e., critical temperature, critical density, critical pressure, and critical compressibility factor) for large n-alkanes that thermally decompose experimentally (as large as C48). Historically, molecular simulation has been viewed as a tool that is limited to providing qualitative insight. One key reason for this perceived weakness in molecular simulation is the difficulty to quantify the uncertainty in the results. This is because molecular simulations have many sources of uncertainty that propagate and are difficult to quantify. We investigate one of the most important sources of uncertainty, namely, the intermolecular force field parameters. Specifically, we quantify the uncertainty in the Lennard-Jones (LJ) 12-6 parameters for the CH4, CH3, and CH2 united-atom interaction sites. We then demonstrate how the uncertainties in the parameters lead to uncertainties in the saturated liquid density and critical constant values obtained from Gibbs Ensemble Monte Carlo simulation. Our results suggest that the uncertainties attributed to the LJ 12-6 parameters are small enough that quantitatively useful estimates of the saturated liquid density and the critical constants can be obtained from molecular simulation.

An improved statistical analysis for predicting the critical temperature and critical density with Gibbs ensemble Monte Carlo simulation.

A rigorous statistical analysis is presented for Gibbs ensemble Monte Carlo simulations. This analysis reduces the uncertainty in the critical point estimate when compared with traditional methods found in the literature. Two different improvements are recommended due to the following results. First, the traditional propagation of error approach for estimating the standard deviations used in regression improperly weighs the terms in the objective function due to the inherent interdependence of the vapor and liquid densities. For this reason, an error model is developed to predict the standard deviations. Second, and most importantly, a rigorous algorithm for nonlinear regression is compared to the traditional approach of linearizing the equations and propagating the error in the slope and the intercept. The traditional regression approach can yield nonphysical confidence intervals for the critical constants. By contrast, the rigorous algorithm restricts the confidence regions to values that are physically sensible. To demonstrate the effect of these conclusions, a case study is performed to enhance the reliability of molecular simulations to resolve the n-alkane family trend for the critical temperature and critical density.

Critical and phase-equilibrium properties of an ab initio based potential model of methanol and 1-propanol using two-phase molecular dynamics simulations.

Two-phase molecular dynamics simulations employing a Monte Carlo volume sampling method were performed using an ab initio based force field model parameterized to reproduce quantum-mechanical dimer energies for methanol and 1-propanol at temperatures approaching the critical temperature. The intermolecular potential models were used to obtain the binodal vapor-liquid phase dome at temperatures to within about 10 K of the critical temperature. The efficacy of two all-atom, site-site pair potential models, developed solely from the energy landscape obtained from high-level ab initio pair interactions, was tested for the first time. The first model was regressed from the ab initio landscape without point charges using a modified Morse potential to model the complete interactions; the second model included point charges to separate Coulombic and dispersion interactions. Both models produced equivalent phase domes and critical loci. The model results for the critical temperature, density, and pressure, in addition to the sub-critical equilibrium vapor and liquid densities and vapor pressures, are compared to experimental data. The model's critical temperature for methanol is 77 K too high while that for 1-propanol is 80 K too low, but the critical densities are in good agreement. These differences are likely attributable to the lack of multi-body interactions in the true pair potential models used here.

The use of two-phase molecular dynamics simulations to determine the phase behavior and critical point of propane molecular models.

Molecular dynamics simulations were performed to determine two-phase configurations of model propane molecules below the critical point and in the near-critical, two-phase region. A postprocessor that uses a Monte Carlo method for determination of volumes attributable to each molecule was used to obtain density histograms of the particles from which the bulk coexisting equilibrium vapor and liquid densities were determined. This method of analyzing coexisting densities in a two-phase simulation is straightforward and can be easily implemented for complex, multisite models. Various degrees of internal flexibility in the propane models have little effect on the coexisting densities at temperatures 40 K or more below the critical point, but internal flexibility (angle bending and bond vibrations) does affect the saturated liquid densities in the near-critical region, changing the critical temperature by approximately 20 K. Shorter cutoffs were also found to affect the phase dome and the location of the critical point.