Structural and Dynamic Heterogeneity of Deep Eutectic Solvents Composed of Choline Chloride and Ortho-Phenol Derivatives.

Structural, thermal, and dynamic properties of four deep eutectic solvents comprising choline chloride paired with phenolic derivative hydrogen-bond donors were probed using experiments and molecular simulations. The hydrogen-bond donors include phenol, catechol, -chlorophenol, and o-cresol, in a 3:1 mixture with the hydrogen-bond acceptor choline chloride. Density, viscosity, and pulsed-field gradient NMR diffusivity measurements were conducted over a range of temperatures.

Assessing Melting and Solid-Solid Transition Properties of Choline Chloride via Molecular Dynamics Simulations.

Choline chloride (ChCl) is used extensively as a hydrogen bond donor in deep eutectic solvents (DESs). However, determining its melting properties experimentally is challenging due to decomposition upon melting, leading to widely varying literature values. Accurate melting properties are crucial for understanding the solid-liquid phase behavior of ChCl-containing DESs.

Molecular dynamics simulations of uranyl and plutonyl cations in a task-specific ionic liquid.

Ionic liquids (ILs) are a unique class of solvents with potential applications in advanced separation technologies relevant to the nuclear industry. ILs are salts with low melting points and a wide range of tunable physical properties, such as viscosity, hydrophobiciy, conductivity, and liquidus range. ILs have negligible vapor pressure, are often non-flammable, and can have high thermal stability and a wide electrochemical window, making them attractive for use in separations processes relevant to the nuclear industry.

Combining High-Throughput Experiments and Active Learning to Characterize Deep Eutectic Solvents.

The high tunability of deep eutectic solvents (DESs) stems from the ease of changing their precursors and relative compositions. However, measuring the physicochemical properties across large composition and temperature ranges, necessary to properly design target-specific DESs, is tedious and error-prone and represents a bottleneck in the advancement and scalability of DES-based applications. As such, active learning (AL) methodologies based on Gaussian processes (GPs) were developed in this work to minimize the experimental effort necessary to characterize DESs.

Adsorption of difluoromethane (HFC-32) and pentafluoroethane (HFC-125) and their mixtures in silicalite-1: An experimental and Monte Carlo simulation study.

Hydrofluorocarbons are a class of fluorinated molecules used extensively in residential and industrial refrigeration systems. This study examines the potential of using adsorption processes with the silicalite-1 zeolite to separate a mixture of difluoromethane (CH2F2, HFC-32) and pentafluoroethane (CF3CF2H, HFC-125) at various concentrations. Pure adsorption data were measured using a XEMIS gravimetric microbalance, whereas binary data were determined using the Integral Mass Balance method.