1-Butanol as a Solvent for Efficient Extraction of Polar Compounds from Aqueous Medium: Theoretical and Practical Aspects

Abstract

The extraction of polar molecules from aqueous solution is a challenging task in organic synthesis. 1-Butanol has been used sporadically as an eluent for polar molecules, but it is unclear which molecular features drive its efficiency. Here, we employ free energy simulations to study the partitioning of 15 solutes between water and 1-butanol. The simulations demonstrate that the high affinity of polar molecules to the wet 1-butanol phase is associated with its nanostructure. Small inverse micelles of water are able to accommodate polar solutes and locally mimic an aqueous environment. We verify the simulations based on partition coefficients between water and 1-octanol, and include a blind prediction of the water/1-butanol partition coefficient of cyclohexane-1,2-diol. The calculations are in excellent agreement with experiment, reaching root-mean-square deviations below 0.7 kcal/mol. Actual extractions of cyclohexane-1,2-diol from buffer solutions that mimic cell lysates and suspensions in biocatalytic reactions further exemplify our findings. The yields highlight that extractions with 1-butanol can be significantly more efficient than the conventional protocol based on ethyl acetate.

A Comparison of QM/MM Simulations with and without the Drude Oscillator Model Based on Hydration Free Energies of Simple Solutes

“…In addition, this set includes amino acid side-chain analogs, ring compounds and hydrophobic molecules, thus providing a minimalistic test set without additional challenges, such as protonation, tautomerism or extensive conformational flexibility. We have previously used this test set to study polarization energies [ 141 ], the convergence of free energy simulations [ 133 ] and the use of 1-butanol for the extraction of polar solutes [ 142 ].…”

n‐Butanol: An Ecologically and Economically Viable Extraction Solvent for Isolating Polar Products from Aqueous Solutions

“…Our computational study showed how molecules of different sizes and polarities interact with the wet n-butanol phase. [14] While there is a small entropic penalty for transferring very polar molecules from the water phase to the restricted volume of the inverted micelles (with diameters of about 6 to 9.5 Å), [14] the interactions with the aliphatic tail of n-butanol can oftentimes overcome those costs if the solute contains apolar groups. Out of the partition coefficients that were evaluated in the study, only water itself, methanol and the amino acid serine exhibited a higher affinity for water, while the rest of the molecules favored the wet n-butanol phase.…”

“…Out of the partition coefficients that were evaluated in the study, only water itself, methanol and the amino acid serine exhibited a higher affinity for water, while the rest of the molecules favored the wet n-butanol phase. [14] Examples of polar molecules that favor the wet n-butanol phase over aqueous solution are acetic acid, diethylamide, adenosine triphosphate, acetamide, cyclohexane-1,2-diol, as well as large organic compounds like progesterone derivatives. [14,20] Figure 2 shows a snapshot from a molecular dynamics (MD) simulation of cyclohexane-1,2-diol in the wet n-butanol phase.…”

“…Following our very promising experience with n-butanol and discussions with Gerhard König, at that time member of the Theory Department of the Max-Planck Institute in Mülheim, a theoretical project was initiated which led to a recent publication. [14] In this study we pointed out that one of the main challenges in finding a suitable green substitute for any solvent is to predict which alternative is best for a given compound. It was concluded that solvent selection guides should be supplemented by solubility data for a diverse set of molecules in the recommended solvents, [14] in order to easily find the best solvent.…”

“…[14] In this study we pointed out that one of the main challenges in finding a suitable green substitute for any solvent is to predict which alternative is best for a given compound. It was concluded that solvent selection guides should be supplemented by solubility data for a diverse set of molecules in the recommended solvents, [14] in order to easily find the best solvent. When experimental solubility or distribution coefficient data are not available, modern computational chemistry methods can fill this gap with relatively high accuracy, particularly if quantum-mechanical (QM) calculations are used to further improve the accuracy of the predictions.…”