The landscape of potential ionic liquids (IL) and deep eutectic solvents (DES) available for biomass pretreatment is vast and, when combined with potential process configurations (e.g., one-pot, lignin-first, lignin-last), experimentally investigating the space of possibilities is inaccessible. The ability to predict the efficacy of a proposed new integrated IL or DES – based deconstruction process will dramatically expand the landscape of ILs that can be investigated for biomass pretreatment by conducting the bulk of the initial IL screening computationally. Predictive models also open the possibility to better engineer conversion technologies where models of monosaccharide and lignin fragment yields as a function of solvent and enzyme features and pretreatment conditions are used to optimize pretreatment process conditions to maximize product yields. The predictive deconstruction group works very closely with the Biological Lignin Depolymerization, Catalytic Lignin Depolymerization, and Pretreatment Optimization and Process Integration groups and facilitated by the JBEI Feedstocks-to-Fuels Pipeline.
Research
Research in predictive deconstruction is aimed at developing fundamental understanding of the interactions among solvent systems (ILs, DESs, water) the different components of biomass to promote dissolution of cellulose, hemicellulose, and lignin. This information is being expanded and used to predict how new proposed ILs or DESs will perform under different pretreatment process scenarios. An additional focus is understanding how ILs and DESs interact with enzymes and other biological entities where it is important to understand and mitigate how the solvent system might negatively impact enzyme activity or microbial growth and productivity. Ultimately, we would like to use the framework to design and optimize biomass and pretreatment process specific ILs and DESs.
Atomistic simulations of biomass-solvent systems
We use molecular dynamics simulations and quantum mechanics calculations such as COnductor like Screening MOdel for Real Solvents (COSMO-RS) to both explain how ILs and DES solubilize biomass and to the calculate properties of ionic liquids correlated with efficient biomass deconstruction such as solubility parameters. Insights and features extracted from these simulations are being used to generate machine learning models of the pretreatment efficacy and biocompatibility of IL and DES solvents.
Machine learning models
Machine learning models of biomass deconstruction using IL and DES solvent systems are being constructed using features of the solvent system extracted from atomistic simulations, features extracted from biomass characterization (e.g., composition, FT-IR, NMR), features extracted from the chemical structures of cations and anions, and metadata on experimental conditions. Hypotheses such as whether a particular solvent system is agnostic to the feedstock phenotype will be tested by comparing models generated using different subsets of features (e.g., models including feedstock phenotype differences vs models excluding biomass phenotype differences). Predictive models are validated using JBEI’s feedstock to fuels pipeline, which will also be used to generate data designed to improve existing models.
Featured Publications
- Multiscale molecular simulations for the solvation of lignin in ionic liquids. Sci. Rep. (2023).
- Prediction of Solubility Parameters of Lignin and Ionic Liquids Using Multi-resolution Simulation Approaches. Green Chem. (2022)
- Effect of cosolvent on the solubility of glucose in ionic liquids: experimental and molecular dynamics simulations. Fluid Phase Equilib. (2022)
- Multiscale molecular simulation strategies for understanding the delignification mechanism of biomass in cyrene. ACS Sustain. Chem. Eng. (2022)
- Towards understanding of delignification of grassy and woody biomass in cholinium-based ionic liquids. Green Chem. (2021)
- A predictive toolset for the identification of effective lignocellulosic pretreatment solvents: a case study of solvents tailored for lignin extraction. Green Chem. (2021).
- Theoretical study on the microscopic mechanism of lignin solubilization in Keggin-type polyoxometalate ionic liquids. Phys. Chem. Chem. (2020)
- Theoretical Insights into the Role of Water in the Dissolution of Cellulose Using IL/Water Mixed Solvent Systems. J. Phys. Chem. (2015)
- Understanding pretreatment efficacy of four cholinium and imidazolium ionic liquids by chemistry and computation. Green Chem. (2014)
- Simulations reveal conformational changes of methylhydroxyl groups during dissolution of cellulose Iβ in ionic liquid 1-ethyl-3-methylimidazolium acetate. J. Phys. Chem. (2012)
- Molecular dynamics study of polysaccharides in binary solvent mixtures of an ionic liquid and water. J. Phys. Chem. (2011)
- Understanding the interactions of cellulose with ionic liquids: a molecular dynamics study. J. Phys. Chem. (2010)
Ionic Liquid Databases
- Public Literature Review: JBEI Pretreatment Lignin Ionic Liquids
- ILThermo
- The Ionic Liquid Property Explorer
- Reference https://www.mdpi.com/2306-5729/4/2/88
- Enterprise Ionic Liquids Database (ILUAM)