Molecular Dynamics Simulations of Heterogeneous Hydrogen Bond Environment in Hydrophobic Deep Eutectic Solvents

Abbas, Usman; Nguyen, Manh Tien; Qiao, Qi; Shi, Jian; Shao, Qing

doi:10.22541/au.161360058.82373663/v1

Cited by 3 publications

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“…They are also highly hygroscopic, and the water content needs to be quantified as it significantly affects their physical properties. − On the other hand, the so-called hydrophobic DESs have only recently been reported. ,, They typically consist of long-chain fatty acids and quaternary ammonium salts with long alkyl chains or small organic molecules (menthol, thymol, ibuprofen, organic acids) and have been shown to be promising solvents for the extraction of natural compounds and metals from aqueous solutions. − They have higher viscosity and lower conductivity than hydrophilic DESs but we have recently demonstrated their use as solvents in electrochemistry . Their properties are less studied but their formation, vapor pressure, density, and viscosity have been characterized and discussed using theoretical fluid models and molecular dynamics simulations. ,,,− …”

Section: Introductionmentioning

confidence: 99%

Effect of Water on a Hydrophobic Deep Eutectic Solvent

et al. 2022

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Deep eutectic solvents (DESs) formed by hydrogen bond donors and acceptors are a promising new class of solvents. Both hydrophilic and hydrophobic binary DESs readily absorb water, making them ternary mixtures, and a small water content is always inevitable under ambient conditions. We present a thorough study of a typical hydrophobic DES formed by a 1:2 mole ratio of tetrabutyl ammonium chloride and decanoic acid, focusing on the effects of a low water content caused by absorbed water vapor, using multinuclear NMR techniques, molecular modeling, and several other physicochemical techniques. Already very low water contents cause dynamic nanoscale phase segregation, reduce solvent viscosity and fragility, increase self-diffusion coefficients and conductivity, and enhance local dynamics. Water interferes with the hydrogen-bonding network between the chloride ions and carboxylic acid groups by solvating them, which enhances carboxylic acid self-correlation and ion pair formation between tetrabutyl ammonium and chloride. Simulations show that the component molar ratio can be varied, with an effect on the internal structure. The water-induced changes in the physical properties are beneficial for most prospective applications but water creates an acidic aqueous nanophase with a high halide ion concentration, which may have chemically adverse effects.

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Section: Introductionmentioning

confidence: 99%

Effect of Water on a Hydrophobic Deep Eutectic Solvent

et al. 2022

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“…However, it is not trivial to determine which HB properties could be used. Our previous work [52] found that the non-ionic DESs can be broadly classified into three groups based on the ratio of intra-and inter-component HB numbers. Such observations inspire us to explore the possibility of developing machine learning models using HB-based descriptors.…”

Section: Introductionmentioning

confidence: 99%

Deciphering Hydrogen Bond Features to Develop Machine Learning Models for Predicting Non-ionic Deep Eutectic Solvents

Abbas

Zhang

Tapia

et al. 2023

Preprint

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Non-ionic deep eutectic solvents (DESs) have emerged as designer solvents for applications such as catalysis, extraction, and carbon capture and conversion. A major challenge is the lack of an efficient tool to discover DES candidates. Currently, the search relies heavily on the researchers intuition or a trial-and-error process, which leads to a low success rate or bypassing of promising candidates. Recognizing the central role that hydrogen bonds play in the DES formation, this work aims to decipher the hydrogen bond features for DESs and develop machine learning models to predict the potential of a system to be DES based on the hydrogen bond-based descriptors. We first analyze the hydrogen bond properties of 38 known DES and 111 known non-DES systems using their molecular dynamics simulation trajectories. The analysis reveals two features for DES compared to non-DES: (a) the imbalance between the numbers of the two intra-component hydrogen bonds, and (b) more and stronger inter-component hydrogen bonds. Based on the analysis results, we developed 30 machine learning models using ten algorithms and three types of hydrogen bond-based descriptors. The model performance is first benchmarked using their average and minimal ROC-AUC values. We also analyze the importance of individual features in the models and the results are consistent with the simulation-based statistical analysis. Finally, we validate the prediction ability of the models using the experimental results of 34 systems. The Extra Trees outperforms the others in the validation with an ROC-AUC of 0.88. Our work iterates the importance of hydrogen bond in DES formation and shows the potential of machine learning in discovering new DESs.

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“…Lidocaine 8 has also been reported as a promising candidate in the pharmaceutical industry. We have investigated the bulk phase properties of these eight DES systems in our previous work 47 . The work here is a continuous effort to advance our understanding of the interfacial behavior of these eight hydrophobic DESs.…”

Section: Introductionmentioning

confidence: 99%

Structure and hydrogen bonds of hydrophobic deep eutectic solvent‐aqueous liquid–liquid interfaces

et al. 2021

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Hydrophobic deep eutectic solvents (DESs) emerge as candidates to extract organic substrates from aqueous solutions. The DES-aqueous liquid-liquid interface plays a vital role in the extraction ability of DESs because the nonbulk structure of interfacial molecules could cause thermodynamic and kinetic barriers. One question is how the DES compositions affect the structural features of the interface. We investigate the density profile, dipole moment, and hydrogen bonds of eight hydrophobic DESaqueous interfaces using molecular dynamics simulations. The eight DESs are composed of four organic compounds: decanoic acid, menthol, thymol, and lidocaine. The results show the variations of dipole moment and hydrogen bond structure and dynamics at the interfaces. These variations could influence the extraction ability of DES through adjusting the partition and kinetics of organic substrates in the DESaqueous biphasic systems. We also analyze the relationship between the variation of these interfacial features and the size and hydrophobicity of DES components.

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Molecular Dynamics Simulations of Heterogeneous Hydrogen Bond Environment in Hydrophobic Deep Eutectic Solvents

Cited by 3 publications

References 0 publications

Effect of Water on a Hydrophobic Deep Eutectic Solvent

Effect of Water on a Hydrophobic Deep Eutectic Solvent

Deciphering Hydrogen Bond Features to Develop Machine Learning Models for Predicting Non-ionic Deep Eutectic Solvents

Structure and hydrogen bonds of hydrophobic deep eutectic solvent‐aqueous liquid–liquid interfaces

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