Bio-Based Solvents for Organic Synthesis James Richard Sherwood Submitted for the degree of Doctor of Philosophy University of York Department of Chemistry July 2013 2 Abstract Scrutiny over solvent selection in the chemical industry has risen in recent decades, popularising research into neoteric solvent systems such as ionic liquids and supercritical fluids. More recently bio-based solvent products have been considered as replacements for conventional petroleum derived solvents. Because they bear a close resemblance to existing solvent products, bio-based solvents can be readily absorbed into the fine chemical industries. This work develops a methodology for identifying reactions of concern with respect to current solvent selection practice, and then implementing a high performance bio-based solvent substitute. In this thesis, kinetic studies of heteroatom alkylation, amidation, and esterification are documented, and the solvent effect dictating the rate of each reaction ascertained. With the ideal properties for the solvent known, bio-based solvent candidates were screened for suitability in each case study. This process, which employs computational tools, was also applied to model the productivity of the Biginelli reaction as a representative multi-component heterocycle synthesis. A strong case is made for limonene and p-cymene as bio-based solvents for all but heteroatom alkylation from the case studies listed above. Alkylations with nitrogen nucleophiles are instead suited to high polarity solvents, and to this end some bio-based amides were investigated. 3 4 Contents List of figures 9 List of schemes 17 List of tables 21 Acknowledgements 27 Declaration 29 1 Introduction: A critical analysis of green and renewable solvents 33 1.1 Modern solvent use 34 1.2 Properties of solvents 37 1.3 Solvent selection 51 1.4 Analysis of bio-based solvents 63 2 Nucleophilic substitution 77 2.1 Solvents and nucleophilic substitution 77 2.2 Nucleophilic substitution results and discussion 81 2.3 Heteroatom alkylation summary 111 3 Amidation 117 3.1 Solvents and amidation 117 3.2 Amidation results and discussion 127 3.3 Amidation summary 153 4 Uncatalysed esterification 157 4.1 Solvents and esterification 157 4.2 Uncatalysed esterification results and discussion 161 4.3 Esterification summary 175 5 5 Catalysed carbonyl addition 179 5.1 Bio-based acid catalysts for organic chemistry 179 5.2 Combined solvent and catalytic effects in carbonyl additions 191 5.3 Catalysed carbonyl addition summary 204 6 Heterocycle synthesis: The Biginelli reaction 209 6.1 Introduction to Pietro Biginelli and his reaction 209 6.2 Standard Biginelli reaction solvent effects 214 6.3 Modified Biginelli reaction solvent effects 237 6.4 Biginelli reaction summary 240 7 Conclusion 245 7.1 Case study recapitulation 245 7.2 The future of bio-based solvents 248 8 Appendices 255 8.1 Experimental procedures 255 8.2 Supplementary data 261 Abbreviations 285 References 295 6 7 8 List of figures Figure 1.1 Materials (by mass) required for the manufacture of a typical pharmaceutical product. Figure 1.2 A comparison between reaction class frequency in process chemistry and manufacturing plant chemistry within the pharmaceutical industry. Figure 1.3 An energy profile of 4-nitroaniline dissolving in acetic acid. Figure 1.4 A comparison between relative permittivity and the Hildebrand solubility parameter. Figure 1.5 Three dimensional Hansen plot based on the solubility of urea. Figure 1.6 The solvent effect influencing the light absorbance of Reichardt’s betaine dye. Figure 1.7 The solvent polarity scale derived from Reichardt’s dye. Figure 1.8 The solvatochromism of N,N-diethyl-4-nitroaniline. Figure 1.9 The UV-vis. spectrum of N,N-diethyl-4-nitroaniline in cyclohexane, 2-MeTHF, and DMSO. Figure 1.10 A comparison between aniline dye absorbance maxima in different solvents. Figure 1.11 Aprotic solvent polarity map. Figure 1.12 Protic solvent polarity map. Figure 1.13 A visual representation of the algorithmic solvent selection process. Figure 1.14 Rule D of the solvent selection algorithm. Figure 1.15 Rule C (R2a) of the revised solvent selection algorithm when the solvent is opted to be recycled. Figure 1.16 An arbitrary polarity map with rule G assignments from the revised solvent selection algorithm. Figure 1.17 A hypothetical comparison between methods for identifying the permissible toxicity limits of solvents when the user defined limit is log(LD50) = 3.5. 9 Figure 1.18 A linear free energy diagram showing the acidity of benzoic acids in different solvents. Figure 1.19 The rate of a Fischer esterification correlated to solvent polarity. Figure 1.20 Aprotic bio-based solvent polarity map. Figure 1.21 Protic bio-based solvent polarity map. Figure 2.1 The natural logarithms of rate constants (relative to methanol) for the Menschutkin reaction. Figure 2.2 The progression of the model Menschutkin reaction in DMSO as observed by 1H-NMR spectroscopy. Figure 2.3 The conversion to 1-decyl-2,3-dimethylimidazolium bromide with the Menschutkin reaction in DMSO and ethanol as determined by 1H-NMR spectroscopy. Figure 2.4 Integrated rate equations of Menschutkin reactions in DMSO and ethanol. Figure 2.5 The polarity of the model Menschutkin reaction solvent set. Figure 2.6 The SUS-HAS-ECO classifications of the model Menschutkin reaction solvent set. Figure 2.7 The LSER describing the rate constant of the model Menschutkin reaction as a function of solvent dipolarity only. Figure 2.8 A comparison between experimental and predicted SN2 ln(k) values based on a LSER incorporating both π* and α Figure 2.9 Predicted ln(k) values from the LSER featuring the ε modification to α compared to experimental Menschutkin reaction data. Figure 2.10 A systematic error check of experimental rate constants for the model Menschutkin reaction. Figure 2.11 A demonstration of the absence of a correlation between Reichardt’s parameter and ln(k) of the model Menschutkin reaction. Figure 2.12 The relationship between relative permittivity and experimental values of ln(k) of the model Menschutkin reaction. Figure 2.13 An arbitrary free energy diagram indicating Menschutkin reaction solvent effects in DMSO relative to chloroform and ethanol. 10 Figure 2.14 Menschutkin reaction solvent selection algorithm screenshot, step 1: Reaction components. Figure 2.15 Menschutkin reaction solvent selection algorithm screenshot, step 2: Solvent class inclusion. Figure 2.16 Menschutkin reaction solvent selection algorithm screenshot, step 3: Parameter input. Figure 2.17 Menschutkin reaction solvent selection algorithm screenshot: Polarity matching diagram for estimating the solubility of 1,2-dimethylimidizole. Figure 2.18 Menschutkin reaction solvent selection algorithm screenshot, step 4: Scoring system. Figure 2.19 Menschutkin reaction solvent selection algorithm screenshot, step 5: Solvent effects. Figure 2.20 Menschutkin reaction solvent selection algorithm screenshot, step 6: Solvent selection guide. Figure 2.21 Menschutkin reaction solvent selection algorithm screenshot, step 7: Results preview. Figure 2.22 Bio-based amide solvent yields. Figure 2.23 The polarity of highly dipolar aprotic solvents including bio-based amides. Figure 2.24 The performance of bio-based amide solvents and sulpholane in the Menschutkin reaction. Figure 3.1 A reproduction and simplification of the Pfizer reagent selection Venn diagram for amide coupling protocols. Figure 3.2 An example of a 1H-NMR spectrum showing the partially complete model amidation in toluene. Figure 3.3 Amidation reaction order determination in toluene. Figure 3.4 A polarity map of solvents included in the initial screening of the model amidation reaction. Figure 3.5 The environmental, health and safety of amidation solvents. Figure 3.6 The LSER correlating the rate of the model amidation reaction with the hydrogen bond accepting ability of the solvent. 11 Figure 3.7 A multi-solvent Eyring relationship for amidation including a predicted crossover point of reaction rates at an iso-kinetic temperature. Figure 3.8 The correlation between the enthalpy of activation and solvent hydrogen bond accepting ability (β). Figure 3.9 The correlation between the entropy of activation and solvent hydrogen bond accepting ability (β). Figure 3.10 The relationship between the activation parameters dictating the kinetics of the model amidation. Figure 3.11 The generalised variable enthalpy of amidation illustrated in toluene and DMSO. Figure 3.12 The toluene-DMSO binary solvent effect on the rate of amidation. Figure 3.13 Amidation reaction solvent selection algorithm screenshot, step 3: Parameter input. Figure 3.14 Amidation reaction solvent selection algorithm screenshot, step 6: Solvent selection guide. Figure 3.15 A LSER indicating the performance of limonene and p-cymene in amidation reactions. Figure 3.16 Estimated versus predicted ln(k) values of amidation. Figure 3.17 Systematic error check in the amidation rate constants. Figure 3.18 Isolated yields of N-benzyl-4-phenylbutanamide from different solvents. Figure 3.19 Associated metrics of amidation reactions between 4-phenylbutanoic acid and benzylamine unless otherwise stated. Figure 3.20 The comparison of PMI in different amidations. Figure 4.1 The manipulation of a fluorous multiphasic system to enhance esterification yields. Figure 4.2 A 1H-NMR spectrum of the uncatalysed model esterification occurring in chloroform. Figure 4.3 Polarity map of solvents used in the initial uncatalysed model esterification solvent set. Figure