Nanoporous, gas-selective membranes have shown encouraging results for the removal of CO2 from flue gas, yet the optimal design for such membranes is often unknown. Therefore, we used molecular dynamics simulations to elucidate the behavior of CO2 within aqueous and ionic liquid (IL) systems ([EMIM][TFSI] and [OMIM][TFSI]), both confined individually and as an interfacial aqueous/IL system. We found that within aqueous systems the mobility of CO2 is reduced due to interactions between the CO2 oxygens and hydroxyl groups on the pore surface. Within the IL systems, we found that confinement has a greater effect on the [EMIM][TFSI] system as opposed to the [OMIM][TFSI] system. Paradoxically, the larger and more asymmetrical [OMIM]+ molecule undergoes less efficient packing, resulting in fewer confinement effects. Free energy surfaces of the nanoconfined aqueous/IL interface demonstrate that CO2 will transfer spontaneously from the aqueous to the IL phase.
A major hurdle in utilizing carbon dioxide (CO2) lies in separating it from industrial flue gas mixtures and finding suitable storage methods that enable its application in various industries. To address this issue, we utilized a combination of molecular dynamics simulations and experiments to investigate the behavior of CO2 in common room-temperature ionic liquids (RTIL) when in contact with aqueous interfaces. Our investigation of RTILs, [EMIM][TFSI] and [OMIM][TFSI], and their interaction with a pure water layer mimics the environment of a previously developed ultrathin enzymatic liquid membrane for CO2 separation. We analyzed diffusion constants and viscosity, which reveals that CO2 molecules exhibit faster mobility within the selected ILs compared to what would be predicted solely based on the viscosity of the liquids using the standard Einstein-Stokes relation. Moreover, we calculated the free energy of translocation for various species across the aqueous-IL interface, including CO2 and HCO3-. Free energy profiles demonstrate that CO2 exhibits a more favorable partitioning behavior in the RTILs compared to that in pure water, while a significant barrier hinders the movement of HCO3- from the aqueous layer. Experimental measurement of the CO2 transport in the RTILs corroborates the model. These findings strongly suggest that hydrophobic RTILs could serve as a promising option for selectively transporting CO2 from aqueous media and concentrating it as a preliminary step toward storage.
Diffusion properties of bulk fluids have been predicted using empirical expressions and machine learning (ML) models, suggesting that predictions of diffusion also should be possible for fluids in confined environments. The ability to quickly and accurately predict diffusion in porous materials would enable new discoveries and spur development in relevant technologies such as separations, catalysis, batteries, and subsurface applications. Here in this work, we apply artificial neural network (ANN) models to predict the simulated self-diffusion coefficients of real liquids in both bulk and pore environments. The training data sets were generated from molecular dynamics (MD) simulations of Lennard-Jones particles representing a diverse set of 14 molecules ranging from ammonia to dodecane over a range of liquid pressures and temperatures. Planar, cylindrical, and hexagonal pore models consisted of walls composed of carbon atoms. Our simple model for these liquids was primarily used to generate ANN training data, but the simulated self-diffusion coefficients of bulk liquids show excellent agreement with experimental diffusion coefficients. ANN models based on simple descriptors accurately reproduced the MD diffusion data for both bulk and confined liquids, including the trend of increased mobility in large pores relative to the corresponding bulk liquid.
Symbolic regression (SR) with a multi-gene genetic program has been used to elucidate new empirical equations describing diffusion in Lennard-Jones (LJ) fluids. Examples include equations to predict self-diffusion in pure LJ fluids and equations describing the finite-size correction for self-diffusion in binary LJ fluids. The performance of the SR-obtained equations was compared to that of both the existing empirical equations in the literature and to the results from artificial neural net (ANN) models recently reported. It is found that the SR equations have improved predictive performance in comparison to the existing empirical equations, even though employing a smaller number of adjustable parameters, but show an overall reduced performance in comparison to more extensive ANNs.
Predicting the diffusion coefficient of fluids under nanoconfinement is important for many applications including the extraction of shale gas from kerogen and product turnover in porous catalysts. Due to the large number of important variables, including pore shape and size, fluid temperature and density, and the fluid-wall interaction strength, simulating diffusion coefficients using molecular dynamics (MD) in a systematic study could prove to be prohibitively expensive. Here, we use machine learning models trained on a subset of MD data to predict the self-diffusion coefficients of Lennard-Jones fluids in pores. Our MD data set contains 2280 simulations of ideal slit pore, cylindrical pore, and hexagonal pore geometries. We use the forward feature selection method to determine the most useful features (i.e., descriptors) for developing an artificial neutral network (ANN) model with an emphasis on easily acquired features. Our model shows good predictive ability with a coefficient of determination (i.e., R2) of ∼0.99 and a mean squared error of ∼2.9 × 10-5. Finally, we propose an alteration to our feature set that will allow the ANN model to be applied to nonideal pore geometries.
The objective of this project was to eliminate and/or render bulk agent unusable by a threat entity via neutralization and/or polymerization of the bulk agent using minimal quantities of additives. We proposed the in situ neutralization and polymerization of bulk chemical agents (CAs) by performing reactions in the existing CA storage container via wet chemical approaches using minimal quantities of chemical based materials. This approach does not require sophisticated equipment, fuel to power generators, electricity to power equipment, or large quantities of decontaminating materials. By utilizing the CA storage container as the batch reactor, the amount of logistical resources can be significantly reduced. Fewer personnel are required since no sophisticated equipment needs to be set up, configured, or operated. Employing the CA storage container as the batch reactor enables the capability to add materials to multiple containers in a short period of time as opposed to processing one container at a time for typical batch reactor approaches. In scenarios where a quick response is required, the material can be added to all the CA containers and left to react on its own without intervention. Any attempt to filter the CA plus material solution will increase the rate of reaction due to increased agitation of the solution.
Recently, lithium nitride (Li3N) has been proposed as a chemical warfare agent (CWA) neutralization reagent for its ability to produce nucleophilic ammonia molecules and hydroxide ions in aqueous solution. Quantum chemical calculations can provide insight into the Li3N neutralization process that has been studied experimentally. Here, we calculate reaction-free energies associated with the Li3N-based neutralization of the CWA VX using quantum chemical density functional theory and ab initio methods. We find that alkaline hydrolysis is more favorable to either ammonolysis or neutral hydrolysis for initial P-S and P-O bond cleavages. Reaction-free energies of subsequent reactions are calculated to determine the full reaction pathway. Notably, products predicted from favorable reactions have been identified in previous experiments.
Molecular diffusion coefficients calculated using molecular dynamics (MD) simulations suffer from finite-size (i.e., finite box size and finite particle number) effects. Results from finite-sized MD simulations can be upscaled to infinite simulation size by applying a correction factor. For self-diffusion of single-component fluids, this correction has been well-studied by many researchers including Yeh and Hummer (YH); for binary fluid mixtures, a modified YH correction was recently proposed for correcting MD-predicted Maxwell-Stephan (MS) diffusion rates. Here we use both empirical and machine learning methods to identify improvements to the finite-size correction factors for both self-diffusion and MS diffusion of binary Lennard-Jones (LJ) fluid mixtures. Using artificial neural networks (ANNs), the error in the corrected LJ fluid diffusion is reduced by an order of magnitude versus existing YH corrections, and the ANN models perform well for mixtures with large dissimilarities in size and interaction energies where the YH correction proves insufficient.