Nanoconfinement of Carbon Dioxide within Interfacial Aqueous/Ionic Liquid Systems.

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.

Transport and Energetics of Carbon Dioxide in Ionic Liquids at Aqueous Interfaces.

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.

Machine Learning Predictions of Simulated Self-Diffusion Coefficients for Bulk and Confined Pure Liquids.

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. 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 development of empirical equations for diffusion in Lennard-Jones fluids.

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.

Quantum Calculations of VX Ammonolysis and Hydrolysis Pathways via Hydrated Lithium Nitride.

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.

Machine Learning-Based Upscaling of Finite-Size Molecular Dynamics Diffusion Simulations for Binary Fluids.

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.

Manipulating the Amount and Structure of the Organic Matrix Affects the Water Compartments of Human Cortical Bone.

Being predictors of the mechanical properties of human cortical bone, bound and pore water measurements by magnetic resonance (MR) imaging are being developed for the clinical assessment of fracture risk. While pore water is a surrogate of cortical bone porosity, the determinants of bound water are unknown. Manipulation of organic matrix properties by oxidative deproteinization, thermal denaturation, or nonenzymatic glycation lowers bone toughness. Because bound water contributes to bone toughness, we hypothesized that each of these matrix manipulations affect bound water fraction (Vbw/Vbone). Immersing cadaveric bone samples in sodium hypochlorite (NaClO) for 96 hours did not affect tissue mineral density or cortical porosity, but rather decreased Vbw/Vbone and increased short-T2 pore water signals as determined by 1H nuclear MR relaxometry (1H NMR). Moreover, the post treatment Vbw/Vbone linearly correlated with the remaining weight fraction of the organic matrix. Heating bone samples at 110°C, 120°C, 130°C, and then 140°C (∼24 hours per temperature and rehydration for ∼24 hours before 1H NMR analysis) did not affect Vbw/Vbone. After subsequently heating them at 200°C, Vbw/Vbone increased. Boiling bone samples followed by heating at 110°C, 120°C, and then 130°C in water under pressure (8 hours per temperature) had a similar effect on Vbw/Vbone. Raman spectroscopy analysis confirmed that the increase in Vbw/Vbone coincided with an increase in an Amide I subpeak ratio that is sensitive to changes in the helical structure of collagen I. Glycation of bone by ribose for 4 weeks, but not in glucose for 16 weeks, decreased Vbw/Vbone, although the effect was less pronounced than that of oxidative deproteinization or thermal denaturation. We propose that MR measurements of bound water reflect the amount of bone organic matrix and can be modulated by collagen I helicity and by sugar-derived post translational modifications of the matrix. © 2019 The Authors. JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.

Self-assembled nanoparticle antireflection coatings on geometrically complex optical surfaces.

Here we report a simple and scalable electrostatics-assisted colloidal self-assembly technology for fabricating monolayer nanoparticle antireflection coatings on geometrically complex optical surfaces. By using a surface-modified glass volumetric flask with a long neck as a proof-of-concept demonstration, negatively charged silica nanoparticles with 110 nm diameter are electrostatically adsorbed on both the interior and exterior surfaces of the flask possessing positive surface charges. The self-assembled monolayer nanoparticle antireflection coatings can significantly improve light transmission through different regions of the flask with varied curvatures, as revealed by optical transmission measurements and numerical simulations using a simplified thin-film multilayer model.

Assessing glycation-mediated changes in human cortical bone with Raman spectroscopy.

Establishing a non-destructive method for spatially assessing advanced glycation end-products (AGEs) is a potentially useful step toward investigating the mechanistic role of AGEs in bone quality. To test the hypothesis that the shape of the amide I in the Raman spectroscopy (RS) analysis of bone matrix changes upon AGE accumulation, we incubated paired cadaveric cortical bone in ribose or glucose solutions and in control solutions for 4 and 16 weeks, respectively, at 37°C. Acquiring 10 spectra per bone with a 20X objective and a 830 nm laser, RS was sensitive to AGE accumulation (confirmed by biochemical measurements of pentosidine and fluorescent AGEs). Hyp/Pro ratio increased upon glycation using either 0.1 M ribose, 0.5 M ribose or 0.5 M glucose. Glycation also decreased the amide I sub-peak ratios (cm-1 ) 1668/1638 and 1668/1610 when directly calculated using either second derivative spectrum or local maxima of difference spectrum, though the processing method (eg, averaged spectrum vs individual spectra) to minimize noise influenced detection of differences for the ribose-incubated bones. Glycation however did not affect these sub-peak ratios including the matrix maturity ratio (1668/1690) when calculated using indirect sub-band fitting. The amide I sub-peak ratios likely reflected changes in the collagen I structure.