Controlling the outcome of SN2 reactions in ionic liquids: from rational data set design to predictive linear regression models

Ion-combination specific effects driving the enzymatic activity of halophilic alcohol dehydrogenase 2 from Haloferax volcanii in aqueous ionic liquid solvent mixtures

Biocatalysis in ionic liquids enables novel routes for bioprocessing. Enzymes derived from extremophiles promise greater stability and activity under ionic liquid (IL) influence. Here, we probe the enzyme alcohol dehydrogenase 2 from the halophilic archaeon Haloferax volcanii in thirteen different ion combinations for relative activity and analyse the results against molecular dynamics (MD) simulations of the same IL systems. We probe the ionic liquid property space based on ion polarizability and molecular electrostatic potential. Using the radial distribution functions, survival probabilities and spatial distribution functions of ions, we show that cooperative ion-ion interactions determine ion-protein interactions, and specifically, strong ion-ion interactions equate to higher enzymatic activity if neither of the ions interact strongly with the protein surface. We further demonstrate a tendency for cations interacting with the protein surface to be least detrimental to enzymatic activity if they show a low polarizability when combined with small hydrophilic anions. We also find that the IL ion influence is not mitigated by the surplus of negatively charged residues of the halophilic enzyme. This is shown by free energy landscape analysis in root mean square deviation and distance variation plots of active site gating residues (Trp43 and His273) demonstrating no protection of specific structural elements relevant to preserving enzymatic activity. On the other hand, we observe a general effect across all IL systems that a tight binding of water at acidic residues is preferentially interrupted at these residues through the increased presence of potassium ions. Overall, this study demonstrates a co-ion interaction dependent influence on allosteric surface residues controlling the active/inactive conformation of halophilic alcohol dehydrogenase 2 and the necessity to engineer ionic liquid systems for enzymes that rely on the integrity of functional surface residues regardless of their halophilicity or thermophilicity for use in bioprocessing.

Effects of Ionic Liquids on the Nucleofugality of Bromide

The nucleofugality of bromide was measured in solvent mixtures containing ionic liquids. The solvolysis rate constants of the bromides of well-defined electrofuges were determined in mixtures containing different proportions of 1-butyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide in ethanol. Temperature-dependent kinetic studies allowed an explanation of the observed solvent effects in different mixtures in terms of interactions in solution. Using the solvolysis data, the nucleofugality of bromide in these systems was determined. Likewise, nucleofugality data for bromide were determined in mixtures containing high proportions of seven further ionic liquids. These data allowed quantification of the effects of both varying the amount of ionic liquid and the nature of ionic liquid components on the nucleofugality of bromide. Importantly, ionic liquid mixtures were shown to affect the nucleofugality in a manner similar to chloride, providing a method for predicting the effects of ionic liquids on other electrofuges. Further, the ionic liquids were shown to move the transition state earlier along the reaction coordinate, meaning that there is less charge development in the transition state.

Modeling the alkylation of amines with alkyl bromides: explaining the low selectivity due to multiple alkylation

CONTEXT

Nucleophilic substitution reactions of aliphatic amines with alkyl halides represent a simple and direct mechanism for obtaining higher-order aliphatic amines. However, it is well known that these reactions suffer from low selectivity due to multiple alkylations, which is attributed to the higher reactivity of the newly formed amine. In order to provide a detailed explanation for this kind of system, we have investigated the reactivity of primary and secondary amines with 1-bromopropane and 2-bromopropane. The free energy profile in acetonitrile solution was obtained and a detailed microkinetic analysis was needed to analyze this complex reaction system. We have found that the product of the first alkylation is an ion pair corresponding to the protonated secondary amine and the bromide ion, which can transfer the proton to the reactant primary amine. Then, the newly formed secondary amine can also react, leading to a second alkylation to produce a tertiary protonated amine. Our modeling points out that both the proton transfer equilibria and the similar reactivity of the primary and secondary amines produce reduced selectivity. The proton transfer equilibria also contribute to slowing down the kinetics of the first alkylation.

METHODS

The exploration of the mechanism was done by geometry optimization using the CPCM/X3LYP/ma-def2-SVP method, followed by harmonic frequency calculation at this same level of theory. A composite approach was used to obtain the free energy profile, using the more accurate ωB97X-D3/ma-def2-TZVPP level of theory for electronic energy and the SMD model for the solvation free energy. These calculations were performed with the ORCA 4 program. The detailed microkinetic analysis was done using the Kintecus program.

The effects of ionic liquids on the ethanolysis of a chloroacenaphthene. Evaluation of the effectiveness of nucleofugality data to predict reaction outcome

The reaction of a chlorobenzene in mixtures containing ethanol and eight different ionic liquids was investigated in order to understand the effects of varying proportions and constituent ions of an ionic liquid on the rate constant of the process. The results were found to be generally consistent with previously studied reactions of the same type, with small proportions of an ionic liquid resulting in a rate constant increase compared to ethanol and large proportions causing a rate constant decrease. Temperature dependent kinetic studies were used to interpret the changes in reaction outcome, particularly noting an entropic cost on moving to high proportions of ionic liquid, consistent with organisation of solvent around the transition state. While attempts to use empirical solvent parameters to correlate outcome with the ionic liquid used were unsuccessful, use of recently acquired nucleofugality data for chloride and estimations for the electrofuge allowed for excellent prediction of the effects of ionic liquids, with rate constants quantitatively predicted in systems containing both different proportions of ionic liquid (mean absolute error (MAE) log(k₁) = 0.11) and different ionic liquids (MAE log(k₁) = 0.33). Importantly, this demonstrates the ready application of these quantitative reactivity parameters.

The amphiphilic nanostructure of ionic liquids affects the dehydration of alcohols

The effect of the amphiphilic nanostructure of ionic liquids on the dehydration of secondary alcohols to alkenes has been investigated. The influence of these nanostructures was inverted when an acid catalyst was added to the reaction. This phenomenon was ascribed to a balance between ion-solute interactions and the formation of solute-catalyst hydrogen bonds, highlighting the complex interplay between interactions and reaction outcomes in these nanostructured solvent systems.

Effects of Ionic Liquids on the Nucleofugality of Chloride

The nucleofugality of chloride has been measured in solvent mixtures containing ionic liquids for the first time, allowing reactivity in these solvents to be put in context with molecular solvents. Using well-described electrofuges, solvolysis rate constants were determined in mixtures containing different proportions of ethanol and the ionic liquid 1-butyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide; the different solvent effects observed as the mixture changed could be explained using interactions of the ionic liquid with species along the reaction coordinate, determined using temperature dependent kinetic studies. The solvolysis data allowed determination of the nucleofugality of chloride in these mixtures, which varied with the proportion of salt in the reaction mixture, demonstrating quantitatively the importance of the amount of ionic liquid in the reaction mixture in determining reaction outcome. Nucleofugality data for chloride were determined in seven further ionic liquids, with the reactivity shown to vary over more than an order of magnitude. This outcome illustrates that the components of the ionic liquid are critical in determining reaction outcome. Overall, this work quantitatively extends the understanding of solvent effects in ionic liquids and demonstrates the potential for such information to be used to rationally select an ionic liquid to control reaction outcome.

A review on machine learning algorithms for the ionic liquid chemical space

There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.

Rapid relaxation NMR measurements to predict rate coefficients in ionic liquid mixtures. An examination of reaction outcome changes in a homologous series of ionic liquids

A series of ionic liquids based on the 1-alkyl-3-methylimidazolium cations were examined as components of the solvent mixture for a bimolecular substitution process. The effects on both the rate coefficient of the process and the NMR spin-spin relaxation of the solvent components of changing either the alkyl chain length or the amount of ionic liquid in the reaction mixture were determined. At a constant mole fraction, a shorter alkyl chain length resulted in a greater rate coefficient enhancement and a longer relaxation time, with the opposite effects for a longer alkyl chain length. For a given ionic liquid, increasing the proportion of salt in the reaction mixture resulted in a greater rate coefficient and a shorter relaxation time. The microscopic origins of the rate coefficient enhancement were determined and a step change found in the activation parameters on increasing the alkyl chain length from hexyl to octyl, suggesting notable structuring in solution. Across a range of ionic liquids and solvent compositions, the relaxation time from NMR measurements was shown to relate to the reaction rate coefficient. The approach of using fast and simple NMR relaxation measurements to predict reaction outcomes was exemplified using a morpholinium-based ionic liquid.

Machine learning approaches to understand and predict rate constants for organic processes in mixtures containing ionic liquids

The ability to tailor the constituent ions in ionic liquids (ILs) is highly advantageous as it provides access to solvents with a range of physicochemical properties. However, this benefit also leads to large compositional spaces that need to be explored to optimise systems, often involving time consuming experimental work. The use of machine learning methods is an effective way to gain insight based on existing data, to develop structure-property relationships and to allow the prediction of ionic liquid properties. Here we have applied machine learning models to experimentally determined rate constants of a representative organic process (the reaction of pyridine with benzyl bromide) in IL-acetonitrile mixtures. Multiple linear regression (MLREM) and artificial neural networks (BRANNLP) were both able to model the data well. The MLREM model was able to identify the structural features on the cations and anions that had the greatest effect on the rate constant. Secondly, predictive MLREM and BRANNLP models were developed from the full initial set of rate constant data. From these models, a large number of predictions (>9000) of rate constant were made for mixtures of different ionic liquids, at different proportions of ionic liquid and molecular solvent, at different temperatures. A selection of these predictions were tested experimentally, including through the preparation of novel ionic liquids, with overall good agreement between the predicted and experimental data. This study highlights the benefits of using machine learning methods on kinetic data in ionic liquid mixtures to enable the development of rigorous structure-property relationships across multiple variables simultaneously, and to predict properties of new ILs and experimental conditions.