Molecular Simulation and Artificial Intelligence for the Circular Economy of Bioenergy and Bioproducts.

The concept of the circular bioeconomy is a carbon neutral, sustainable system with zero waste. One vision for such an economy is based upon lignocellulosic biomass. This lignocellulosic circular bioeconomy requires CO absorption from biomass growth and the efficient deconstruction of recalcitrant biomass into solubilized and fractionated biopolymers which are then used as precursors for the sustainable production of high-quality liquid fuels, chemical bioproducts and bio-based materials.

Coarse-Grained Molecular Dynamics Simulation of Solvent-Dependent Cellulose Nanofiber Interactions.

Associations between cellulose are important both in biofuel production and in the use of cellulose for biomaterials. Cellulose nanofibers (CNFs) are sustainable, strong, light-weight alternatives to traditional materials in manufacturing, but are challenging to obtain due to irreversible aggregation in solution during preparative fibrillation. Therefore, it is imperative to understand the underlying factors driving aggregation with a view to designing solvents that can effectively compete with inter-fiber interactions, hence reducing aggregation.

Deep Learning Approaches for Predicting the Surface Tension of Ionic Liquids.

Ionic liquids (ILs) are a novel class of solvents that have attracted significant attention due to their unique and tunable properties. Among their physiochemical characteristics, surface tension plays a critical role in various industrial applications including electrolytes, heat transfer fluids, and separation processes. However, because of the exploratory nature of IL design and the vast combinatorial space of possible anion-cation pairs, the experimental determination of these properties is often impractical, being both time-consuming and costly.

Research progress on organic acid pretreatment of lignocellulose.

Lignocellulosic biomass is a naturally occurring, renewable resource that is utilized to produce a variety of high-value-added products, such as fuels, acids, and building block chemicals. The pretreatment of lignocellulosic biomass is a crucial step in the deconstruction and fractionation of its components. Organic acids, such as formic, acetic, lactic, and maleic acids, have been widely studied for their effectiveness in lignocellulose pretreatment.

Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents.

Carbon dioxide (CO) is a detrimental greenhouse gas and is the main contributor to global warming. In addressing this environmental challenge, a promising approach emerges through the utilization of deep eutectic solvents (DESs) as an ecofriendly and sustainable medium for effective CO capture. Chemically reactive DESs, which form chemical bonds with the CO, are superior to nonreactive, physically based DESs for CO absorption.

Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents.

Deep eutectic solvents (DESs) are emerging as environmentally friendly designer solvents for mass transport and heat transfer processes in industrial applications; however, the lack of accurate tools to predict and thus control their viscosities under both a range of environmental factors and formulations hinders their general application. While DESs may serve as designer solvents, with nearly unlimited combinations, this unfortunately makes it experimentally infeasible to comprehensively measure the viscosities of all DESs of potential industrial interest. To assist in the design of DESs, we have developed several new machine learning (ML) models that accurately and rapidly predict the viscosities of a diverse group of DESs at different temperatures and molar ratios using, to date, one of the most comprehensive data sets containing the properties of over 670 DESs over a wide range of temperatures (278.

Characterization of the acoustic cavitation in ionic liquids in a horn-type ultrasound reactor.

Most ultrasound-based processes root in empirical approaches. Because nearly all advances have been conducted in aqueous systems, there exists a paucity of information on sonoprocessing in other solvents, particularly ionic liquids (ILs). In this work, we modelled an ultrasonic horn-type sonoreactor and investigated the effects of ultrasound power, sonotrode immersion depth, and solvent's thermodynamic properties on acoustic cavitation in nine imidazolium-based and three pyrrolidinium-based ILs.

Predictive understanding of the surface tension and velocity of sound in ionic liquids using machine learning.

Knowledge of the physical properties of ionic liquids (ILs), such as the surface tension and speed of sound, is important for both industrial and research applications. Unfortunately, technical challenges and costs limit exhaustive experimental screening efforts of ILs for these critical properties. Previous work has demonstrated that the use of quantum-mechanics-based thermochemical property prediction tools, such as the conductor-like screening model for real solvents, when combined with machine learning (ML) approaches, may provide an alternative pathway to guide the rapid screening and design of ILs for desired physiochemical properties.

Multiscale molecular simulations for the solvation of lignin in ionic liquids.

Lignin, the second most abundant biopolymer found in nature, has emerged as a potential source of sustainable fuels, chemicals, and materials. Finding suitable solvents, as well as technologies for efficient and affordable lignin dissolution and depolymerization, are major obstacles in the conversion of lignin to value-added products. Certain ionic liquids (ILs) are capable of dissolving and depolymerizing lignin but designing and developing an effective IL for lignin dissolution remains quite challenging.

Renewable Schiff-Base Ionic Liquids for Lignocellulosic Biomass Pretreatment.

Growing interest in sustainable sources of chemicals and energy from renewable and reliable sources has stimulated the design and synthesis of renewable Schiff-base (iminium) ionic liquids (ILs) to replace fossil-derived ILs. In this study, we report on the synthesis of three unique iminium-acetate ILs from lignin-derived aldehyde for a sustainable “future” lignocellulosic biorefinery. The synthesized ILs contained only imines or imines along with amines in their structure; the ILs with only imines group exhibited better pretreatment efficacy, achieving >89% sugar release.

COSMO-RS-Based Screening of Antisolvents for the Separation of Sugars from Ionic Liquids: Experimental and Molecular Dynamic Simulations.

The use of ionic liquids (ILs) in the biorefinery process has been increasing for the past few decades. In biorefinery, the separation process with respect to sugars needs to be evaluated for an efficient process design. Therefore, the present work aims to investigate the separation of sugars and ILs by means of a precipitation process using an antisolvent method.

Molecular Dynamic Simulations for the Extraction of Quinoline from Heptane in the Presence of a Low-Cost Phosphonium-Based Deep Eutectic Solvent.

The present study aims at the extraction of a polyaromatic hydrocarbon from fuel oils using a novel low-cost deep eutectic solvent (DES). The DES is prepared by mixing the hydrogen bond acceptor (HBA; methyltriphenylphosphonium bromide, MTPB) and hydrogen bond donor (HBD; ethylene glycol) at a molar ratio of 1:4. The liquid-liquid equilibrium is then measured at ambient condition.

Dilute acid pretreatment of sorghum biomass to maximize the hemicellulose hydrolysis with minimized levels of fermentative inhibitors for bioethanol production.

Conversion of lignocellulosic biomass into monomeric carbohydrates is economically beneficial and suitable for sustainable production of biofuels. Hydrolysis of lignocellulosic biomass using high acid concentration results in decomposition of sugars into fermentative inhibitors. Thus, the main aim of this work was to investigate the optimum hydrolysis conditions for sorghum brown midrib IS11861 biomass to maximize the pentose sugars yield with minimized levels of fermentative inhibitors at low acid concentrations.

Hydrolysis of bamboo biomass by subcritical water treatment.

The aim of present study was to obtain total reducing sugars (TRS) from bamboo under subcritical water (SCW) treatment in a batch reactor at the temperature ranging from 170 °C to 220 °C and 40 min hydrolysis time. Experiments were performed to investigate the effects of temperature and time on TRS yield. The maximum TRS yield (42.