A predictive toolset for the identification of effective lignocellulosic pretreatment solvents: a case study of solvents tailored for lignin extraction

Ezinne C. Achinivu; Mood Mohan; Hemant Choudhary; Lalitendu Das; Kaixuan Huang; Harsha D. Magurudeniya; Venkataramana R. Pidatala; Anthe George; Blake A. Simmons; John M. Gladden

doi:10.1039/d1gc01186c

A predictive toolset for the identification of effective lignocellulosic pretreatment solvents: a case study of solvents tailored for lignin extraction

Ezinne C. Achinivu, Mood Mohan, Hemant Choudhary, Lalitendu Das, Kaixuan Huang, Harsha D. Magurudeniya, Venkataramana R. Pidatala, Anthe George, Blake A. Simmons, John M. Gladden

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25 Scopus citations

Abstract

Pretreatment of lignocellulosic biomass is essential for efficient conversion into biofuels and bioproducts. The present study develops a predictive toolset to computationally identify solvents that can efficiently dissolve lignin and therefore can be used to extract it from lignocellulose during pretreatment, a process known to reduce recalcitrance to enzymatic deconstruction and increase conversion efficiency. Two approaches were taken to examine the potential of eleven organic solvents to solubilize lignin, Hansen solubility parameters (HSP) and activity coefficients and excess enthalpies of solvent/lignin mixtures predicted by COSMO-RS (COnductor like Screening MOdel for Real Solvents). The screening revealed that diethylenetriamine was the most effective solvent, promoting the highest lignin removal (79.2%) and fermentable sugar yields (>72%). Therefore, a COSMO-RS-based predictive model for the lignin removal as a function of number and type of amines was developed. Among the fitted models, the non-linear regression model predicts the lignin solubility more accurately than the linear model. Experimental results demonstrated a >65% lignin removal and >70% of sugar yield from several amine-based solvents tested, which aligned very well with the model's prediction. Finally, to help understand the dissolution mechanism of lignin by these solvents, quantum theory of atoms in molecules (QTAIM) and quantum chemical calculations (interaction energies and natural bond orbital (NBO) analysis) was performed and suggest that amines exhibit strong electrostatic interactions and hydrogen bonding strengths with lignin leading to higher lignin removal. Together, these computational tools provide an effective approach for rapidly identifying solvents that are tailored for effective biomass pretreatment.

Original language	English
Pages (from-to)	7269-7289
Number of pages	21
Journal	Green Chemistry
Volume	23
Issue number	18
DOIs	https://doi.org/10.1039/d1gc01186c
State	Published - Sep 21 2021
Externally published	Yes

Funding

This work was part of the DOE Joint BioEnergy Institute supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research, through contract DE-AC02-05CH11231 between Lawrence Berkeley National Laboratory and the US Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. XRD characterization was conducted with the help of Dr Tevye Kuykendall at the Molecular Foundry in Lawrence Berkeley National Laboratory. This work was part of the DOE Joint BioEnergy Institute (http://www.jbei.org) supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research, through contract DE-AC02-05CH11231 between Lawrence Berkeley National Laboratory and the US Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. XRD characterization was conducted with the help of Dr Tevye Kuykendall at the Molecular Foundry in Lawrence Berkeley National Laboratory.

Funders	Funder number
United States Government
U.S. Department of Energy
Office of Science
Biological and Environmental Research	DE-AC02-05CH11231
Lawrence Berkeley National Laboratory

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1039/d1gc01186c

Cite this

Achinivu, E. C., Mohan, M., Choudhary, H., Das, L., Huang, K., Magurudeniya, H. D., Pidatala, V. R., George, A., Simmons, B. A., & Gladden, J. M. (2021). A predictive toolset for the identification of effective lignocellulosic pretreatment solvents: a case study of solvents tailored for lignin extraction. Green Chemistry, 23(18), 7269-7289. https://doi.org/10.1039/d1gc01186c

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title = "A predictive toolset for the identification of effective lignocellulosic pretreatment solvents: a case study of solvents tailored for lignin extraction",

abstract = "Pretreatment of lignocellulosic biomass is essential for efficient conversion into biofuels and bioproducts. The present study develops a predictive toolset to computationally identify solvents that can efficiently dissolve lignin and therefore can be used to extract it from lignocellulose during pretreatment, a process known to reduce recalcitrance to enzymatic deconstruction and increase conversion efficiency. Two approaches were taken to examine the potential of eleven organic solvents to solubilize lignin, Hansen solubility parameters (HSP) and activity coefficients and excess enthalpies of solvent/lignin mixtures predicted by COSMO-RS (COnductor like Screening MOdel for Real Solvents). The screening revealed that diethylenetriamine was the most effective solvent, promoting the highest lignin removal (79.2%) and fermentable sugar yields (>72%). Therefore, a COSMO-RS-based predictive model for the lignin removal as a function of number and type of amines was developed. Among the fitted models, the non-linear regression model predicts the lignin solubility more accurately than the linear model. Experimental results demonstrated a >65% lignin removal and >70% of sugar yield from several amine-based solvents tested, which aligned very well with the model's prediction. Finally, to help understand the dissolution mechanism of lignin by these solvents, quantum theory of atoms in molecules (QTAIM) and quantum chemical calculations (interaction energies and natural bond orbital (NBO) analysis) was performed and suggest that amines exhibit strong electrostatic interactions and hydrogen bonding strengths with lignin leading to higher lignin removal. Together, these computational tools provide an effective approach for rapidly identifying solvents that are tailored for effective biomass pretreatment.",

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doi = "10.1039/d1gc01186c",

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T2 - a case study of solvents tailored for lignin extraction

AU - Achinivu, Ezinne C.

AU - Mohan, Mood

AU - Choudhary, Hemant

AU - Das, Lalitendu

AU - Huang, Kaixuan

AU - Magurudeniya, Harsha D.

AU - Pidatala, Venkataramana R.

AU - George, Anthe

AU - Simmons, Blake A.

AU - Gladden, John M.

PY - 2021/9/21

Y1 - 2021/9/21

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AB - Pretreatment of lignocellulosic biomass is essential for efficient conversion into biofuels and bioproducts. The present study develops a predictive toolset to computationally identify solvents that can efficiently dissolve lignin and therefore can be used to extract it from lignocellulose during pretreatment, a process known to reduce recalcitrance to enzymatic deconstruction and increase conversion efficiency. Two approaches were taken to examine the potential of eleven organic solvents to solubilize lignin, Hansen solubility parameters (HSP) and activity coefficients and excess enthalpies of solvent/lignin mixtures predicted by COSMO-RS (COnductor like Screening MOdel for Real Solvents). The screening revealed that diethylenetriamine was the most effective solvent, promoting the highest lignin removal (79.2%) and fermentable sugar yields (>72%). Therefore, a COSMO-RS-based predictive model for the lignin removal as a function of number and type of amines was developed. Among the fitted models, the non-linear regression model predicts the lignin solubility more accurately than the linear model. Experimental results demonstrated a >65% lignin removal and >70% of sugar yield from several amine-based solvents tested, which aligned very well with the model's prediction. Finally, to help understand the dissolution mechanism of lignin by these solvents, quantum theory of atoms in molecules (QTAIM) and quantum chemical calculations (interaction energies and natural bond orbital (NBO) analysis) was performed and suggest that amines exhibit strong electrostatic interactions and hydrogen bonding strengths with lignin leading to higher lignin removal. Together, these computational tools provide an effective approach for rapidly identifying solvents that are tailored for effective biomass pretreatment.

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JO - Green Chemistry

JF - Green Chemistry

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ER -

A predictive toolset for the identification of effective lignocellulosic pretreatment solvents: a case study of solvents tailored for lignin extraction

Abstract

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