Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents.

Carbon dioxide (CO) is a detrimental greenhouse gas and is the main contributor to global warming. In addressing this environmental challenge, a promising approach emerges through the utilization of deep eutectic solvents (DESs) as an ecofriendly and sustainable medium for effective CO capture. Chemically reactive DESs, which form chemical bonds with the CO, are superior to nonreactive, physically based DESs for CO absorption.

Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents.

Deep eutectic solvents (DESs) are emerging as environmentally friendly designer solvents for mass transport and heat transfer processes in industrial applications; however, the lack of accurate tools to predict and thus control their viscosities under both a range of environmental factors and formulations hinders their general application. While DESs may serve as designer solvents, with nearly unlimited combinations, this unfortunately makes it experimentally infeasible to comprehensively measure the viscosities of all DESs of potential industrial interest. To assist in the design of DESs, we have developed several new machine learning (ML) models that accurately and rapidly predict the viscosities of a diverse group of DESs at different temperatures and molar ratios using, to date, one of the most comprehensive data sets containing the properties of over 670 DESs over a wide range of temperatures (278.

Characterization of the acoustic cavitation in ionic liquids in a horn-type ultrasound reactor.

Most ultrasound-based processes root in empirical approaches. Because nearly all advances have been conducted in aqueous systems, there exists a paucity of information on sonoprocessing in other solvents, particularly ionic liquids (ILs). In this work, we modelled an ultrasonic horn-type sonoreactor and investigated the effects of ultrasound power, sonotrode immersion depth, and solvent's thermodynamic properties on acoustic cavitation in nine imidazolium-based and three pyrrolidinium-based ILs.

Predictive understanding of the surface tension and velocity of sound in ionic liquids using machine learning.

Knowledge of the physical properties of ionic liquids (ILs), such as the surface tension and speed of sound, is important for both industrial and research applications. Unfortunately, technical challenges and costs limit exhaustive experimental screening efforts of ILs for these critical properties. Previous work has demonstrated that the use of quantum-mechanics-based thermochemical property prediction tools, such as the conductor-like screening model for real solvents, when combined with machine learning (ML) approaches, may provide an alternative pathway to guide the rapid screening and design of ILs for desired physiochemical properties.

Description of Hydration Water in Protein (Green Fluorescent Protein) Solution.

The structurally and dynamically perturbed hydration shells that surround proteins and biomolecules have a substantial influence upon their function and stability. This makes the extent and degree of water perturbation of practical interest for general biological study and industrial formulation. We present an experimental description of the dynamical perturbation of hydration water around green fluorescent protein in solution.

Water's Contribution to the Energetic Roughness from Peptide Dynamics.

Water plays a very important role in the dynamics and function of proteins. Apart from protein-protein and protein-water interactions, protein motions are accompanied by the formation and breakage of hydrogen-bonding network of the surrounding water molecules. This ordering and reordering of water also adds to the underlying roughness of the energy landscape of proteins and thereby alters their dynamics.

Water structure around dipeptides in aqueous solutions.

The bulk water structure around small peptide fragments--glycyl-L-alanine, glycyl-L-proline and L:-alanyl-L-proline-has been determined by a combination of neutron diffraction with isotopic substitution and empirical potential structural refinement techniques. The addition of each of the dipeptides to water gives rise to decreased water-water coordination in the surrounding water solvent. Additionally, both the Ow-Ow radial distribution functions and the water-water spatial density functions in all of the solutions indicate an electrostrictive effect in the second water coordination shell of the bulk water network.

Investigations on the structure of dimethyl sulfoxide and acetone in aqueous solution.

Aqueous solutions of dimethyl sulfoxide (DMSO) and acetone have been investigated using neutron diffraction augmented with isotopic substitution and empirical potential structure refinement computer simulations. Each solute has been measured at two concentrations-1:20 and 1:2 solute:water mole ratios. At both concentrations for each solute, the tetrahedral hydrogen bonding network of water is largely unperturbed, though the total water molecule coordination number is reduced in the higher 1:2 concentrations.

Molecular Dynamics Simulations of Proteins: Can the Explicit Water Model Be Varied?

In molecular mechanics simulations of biological systems, the solvation water is typically represented by a default water model which is an integral part of the force field. Indeed, protein nonbonding parameters are chosen in order to obtain a balance between water-water and protein-water interactions and hence a reliable description of protein solvation. However, less attention has been paid to the question of whether the water model provides a reliable description of the water properties under the chosen simulation conditions, for which more accurate water models often exist.