Lignocellulosic biomass (organic material from plants) is a promising renewable resource that can be converted into transportation fuels or commodity chemicals. Biomass conversion is facilitated by acid-catalyzed reactions performed in aqueous solution that are hindered by low reactivity and poor selectivity. One possible solution to improve reaction yields is to modify the solvent composition by including organic, polar aprotic cosolvents (e.g. dioxane, tetrahydrofuran, etc.), which has been shown to improve reaction performance by 100-fold compared to the same reaction in pure water. However, identifying optimal solvent compositions by experimental trial-and-error is cost prohibitive and lends little physical insight into how these mixtures can affect reaction rates for new biomass conversion processes.<br/><br/>We developed classical molecular dynamics (MD) simulation methods to understand the effects of mixing water with polar aprotic cosolvents on seven acid-catalyzed reactions involving biomass-derived reactants. We found that inclusion of polar aprotic cosolvents leads to the formation of water-enriched local domains around hydrophilic reactants, which draws the acid catalyst to these regions due to preferred catalyst-water interactions and results in improved reaction rates. By quantifying the extent of water-enrichment around the reactant, we found that MD measurables can accurately predict experimental reaction rates for dioxane-water mixtures, showing that classical simulation techniques can inform reaction rates without modeling the reaction mechanism or the catalyst. We improved the predictive model by analyzing MD trajectories using three-dimensional convolutional neural networks (CNNs), which can capture complex spatial solvent features that are related to reaction rates but difficult to quantify by human experts. MD simulations in conjunction with CNNs were used to screen solvent compositions across four mass fractions and three cosolvents for seven reactions (total of 84 systems), a large design space that is difficult to study using quantum mechanical methods. Finally, we used MD simulations to understand the influence of solvent composition on experimentally determined product selectivities for representative acid-catalyzed reactions. These computational models are important for enabling the rational design of new liquid-phase biomass conversion processes by guiding solvent selection.

Solvent screening for biomass conversion reactions using molecular dynamics simulations

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

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