467113 An Assessment of Solvent Effects on the Selectivity of an Alkylation Reaction: A Comparison Between Experiments and Computations

Tuesday, November 15, 2016: 4:33 PM
Franciscan C (Hilton San Francisco Union Square)
Aikaterini Diamanti1, Eliana Grant1, Zara Ganase1, Anita Rea2, Amparo Galindo3 and Claire Adjiman3, (1)Centre for Process Systems Engineering, Dept of Chemical Engineering, Imperial College London, London, United Kingdom, (2)Syngenta, Bracknell, United Kingdom, (3)Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London, United Kingdom

An assessment of solvent effects on the selectivity of an alkylation reaction: A comparison between experiments and computations

A. Diamanti, E. Grant, Z. Ganase, A. M. Rea, A. Galindo, C. S. Adjiman

Centre for Process Systems Engineering, Department of Chemical Engineering Imperial College London, London, SW7 2AZ, UK

Process Studies Group, Technology & Engineering, Syngenta, Jealott’s Hill International Research Centre, Bracknell, Berkshire RG42 6EY, United Kingdom

The solvent in which a given reaction is conducted can influence the selectivity, rate, stability, catalyst activity and overall process performance.1 In the production of pharmaceuticals and fine chemicals, solvents characteristically constitute between 80 and 90 % by mass of the non-aqueous materials used.2 As a result, choosing the best overall solvent for a given process is of high importance. However, making an informed choice is challenging given that there is no universal model for predicting the solvent effects on reactions. Experimentalists can spend significant amounts of time performing reactions in various solvents to optimise a given reaction outcome in the laboratory. As a result, the suitability of computational methods to predict the solvent effects on a reaction is of great interest. Furthermore, if such methods are to enable the identification of an optimal solvent for specific reaction metrics, one must balance computational cost and accuracy: the combination of quantum mechanical calculations with continuum solvation models is particularly appealing in this context. Indeed, the SMD continuum solvation model of Marenich et al.3 has been found to provide a useful basis for the development of a quantum mechanical-computer aided molecular design (QM-CAMD) technique to find optimal reaction solvents. Its application to a Menschutkin reaction enabled the generation of a solvent that enhanced the rate of the reaction by 40% compared with the next best solvent that had been used previously.4 Given that the solvent can also heavily affect the regioselectivity of a reaction, our current work aims to assess the feasibility of predicting solvent effects on selectivity so that this aspect can be incorporated in the QM-CAMD. In order to do so, we undertake a systematic study combining predictions and kinetic experiments, based on an interesting example of the solvent effects on selectivity reported in the literature for the Williamson ether synthesis of sodium beta-naphthoxide and benzyl bromide.5 The choice of solvent significantly impacts the selectivity of reaction for alkylation at either oxygen or carbon sites leading to O- or C-alkylated products, and determines the final product ratio. Density functional theory (DFT) electronic structure calculations are used to predict the rate constants for the Williamson reaction via the two possible pathways as well as to compute the final product ratio, with the solvent represented using the SMD solvation model.3 Twenty combinations of levels of theory and basis sets are investigated. A diverse set of solvents of varying polarity is used and detailed kinetic experiments involving in situ 1HNMR data acquisition are carried out to assess the predictive results. The performance of the various models is discussed and the challenges in conducting reliable experiments are highlighted.



1. Reichardt, C.; Welton, T. Solvent and Solvent Effects in Organic Chemistry, 4th Ed.; Wiley-VCH: Weinheim, 2010.

2. Constable, D. J. C.; Jimenez-Gonzalez, C.; Henderson, R. K. Org. Proc. Res. Dev. 2007, 11, 133–137.

3. Marenich, A. V; Cramer, C. J.; Truhlar, D. G. J. Phys. Chem. B 2009, 113, 6378–6396.

4. Struebing, H.; Ganase, Z.; Karamertzanis, P. G.; Siougkrou, E.; Haycock, P.; Piccione, P. M.; Armstrong, A.; Galindo, A.; Adjiman, C. S. Nat. Chem. 2013, 5, 952–957.

5. Kornblum, N.; Seltzer, R.; Haberfield, P. J. Am. Chem. Soc. 1963, 85 (8), 1148–1154.

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