(376b) A Rational Approach to Solvent Choice in the Development of Homogeneous Catalytic Reaction Systems

The choice of solvent or solvent mixture in which to carry out a reaction is usually made in the early stages of process development. This decision can have a huge effect on process performance, by affecting reaction rates and selectivity, by placing demands on downstream purification units and by contributing to the environmental impact of the process. In this work, we present a systematic approach to solvent selection, which greatly widens the options considered during process design, and can lead to significant improvements in reaction performance. The approach was first developed for model single-step reaction [1] and is extended here to more complex reaction schemes. It involves (i) a targeted set of experiments to quantify reaction kinetics and solvent influences on rate (ii) a computer-aided solvent design step in which potentially better solvents are identified, and (iii) synthetic verification of the results.

The approach is presented through application to the ring-closing metathesis (RCM) of a diene catalyzed by the 1,3-dimesityl-4,5-dihydroimidazol-2-ylidene ruthenium complex (Grubbs II catalyst, [2]). The Grubbs II catalyst is in many ways promising for RCM in an industrial context, but the amount of catalyst that is typically used to achieve good productivity makes its adoption expensive. It is difficult to postulate what solvent properties best promote the reaction, as the conversion is the result of complex balance between the rates of catalyst activation, the metathesis reaction itself, and catalyst deactivation. We demonstrate how the proposed methodology has led to the discovery of better solvents for this reaction, in which complete conversion is achieved quickly with 10 times less catalyst than is required for the usual solvent, dichloromethane [3]. Through this approach, we have also been able to gain a better understanding of the kinetics of the key reactions, providing essential information for process design and scale-up.

References

[1] M. Folic, C.S. Adjiman, E.N. Pistikopoulos, AIChE Journal, 53(5):1240, 2007; M. Folic, C.S. Adjiman, E.N. Pistikopoulos, ?Computer-aided solvent design: Maximizing product formation?, in press, Industrial & Engineering Chemistry Research (2008).

[2] M. Scholl, S. Ding, C.W. Lee, R.H. Grubbs, Org. Lett,, 1: 953 , 1999.

[3] C.S. Adjiman, A.J. Clarke, G. Cooper, P.C. Taylor, Chem Commun., DOI:10.1039:b802921k, 2008.