Multiscale modelling strategies and experimental insights for the solvation of cellulose and hemicellulose in ionic liquids

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 (278.15-385.25 K). Three ML models, including support vector regression (SVR), feed forward neural networks (FFNNs), and categorical boosting (CatBoost), were developed to predict DES viscosity as a function of temperature and molar ratio and contrasted with multilinear and two-factor polynomial regression baselines. Quantum chemistry-based, COSMO-RS-derived sigma profile (σ-profile) features were used as inputs for the ML models. The CatBoost model is excellent at externally predicting DES viscosity, as indicated by high R2 (0.99) and low root-mean-square-error (RMSE) and average absolute relative deviations (AARD) (5.22%) values for the testing data sets, and 98% of the data points lie within the 15% of AARD deviations. Furthermore, SHapley additive explanation (SHAP) analysis was employed to interpret the ML results and rationalize the viscosity predictions. The result is an ML approach that accurately predicts viscosity and will aid in accelerating the design of appropriate DESs for industrial applications.

Theoretical study of lactic acid-based deep eutectic solvents dissociation of hemicellulose with different hydrogen bonding acceptors

This paper explored the mechanism of dissociation of hemicellulose using lactic acid (LA)-based deep eutectic solvents (DESs) synthesized with different hydrogen bond acceptors (HBAs) via simulations. Density functional theory (DFT) calculations and molecular dynamics (MD) simulations revealed that DESs synthesized with guanidine hydrochloride (GuHCl) as hydrogen bond acceptor (HBA) demonstrated better hemicellulose solubilization compared to the conventional DESs synthesized using choline chloride (ChCl) as HBA. The best interaction with hemicellulose was achieved at GuHCl:LA = 1:1. The results showed that CL^- played a dominant role in the dissolution of hemicellulose by DESs. Unlike ChCl, the guanidine group in GuHCl had the delocalized π bond, which made CL^- have stronger coordination ability and promoted dissolution of hemicellulose by DESs. Moreover, multivariable analysis was employed to establish the correlation between the effects of different DESs on hemicellulose and the molecular simulation results. Additionally, the influence of different HBAs functional groups and carbon chain length on the solubilization of hemicellulose by DESs were analyzed.

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. To address this issue, the COnductor-like Screening MOdel for Real Solvents (COSMO-RS) model was used to screen 5670 ILs by computing logarithmic activity coefficients (ln(γ)) and excess enthalpies (H^E) of lignin, respectively. Based on the COSMO-RS computed thermodynamic properties (ln(γ) and H^E) of lignin, anions such as acetate, methyl carbonate, octanoate, glycinate, alaninate, and lysinate in combination with cations like tetraalkylammonium, tetraalkylphosphonium, and pyridinium are predicted to be suitable solvents for lignin dissolution. The dissolution properties such as interaction energy between anion and cation, viscosity, Hansen solubility parameters, dissociation constants, and Kamlet-Taft parameters of selected ILs were evaluated to assess their propensity for lignin dissolution. Furthermore, molecular dynamics (MD) simulations were performed to understand the structural and dynamic properties of tetrabutylammonium [TBA]⁺-based ILs and lignin mixtures and to shed light on the mechanisms involved in lignin dissolution. MD simulation results suggested [TBA]⁺-based ILs have the potential to dissolve lignin because of their higher contact probability and interaction energies with lignin when compared to cholinium lysinate.

Screening ionic liquids for dissolving hemicellulose by COSMO-RS based on the selective model

The utilization of biomass resources has attracted more and more attention due to the consumption of non-renewable resources. Compared with cellulose and lignin, hemicellulose has been less studied. Some ionic liquids (ILs) have been proved to be excellent solvents for lignocellulosic pretreatment. However, screening of more efficient ILs is difficult due to numerous possible ILs. Computational chemistry has been proved effective in solvent screening, but a precise model is indispensable. In this work, we focused on building several appropriate models and selected the most suitable one. According to the structure of hemicellulose, six hemicellulose models were constructed and the mid-dimer of the xylan chain hemicellulose (MDXC) model was proved to be the best compared with the reported experimental results. Based on the MDXC model, 1368 ILs were screened to evaluate their ability to dissolve hemicellulose by Conductor-like Screening Model for Real Solvents (COSMO-RS). The activity coefficient (γ), excess enthalpy (H^E), and σ-profile indicated that the hydrogen-bond (H-bond) played a vital role in the dissolution of hemicellulose. Anions played a more critical role than cations, where small anions with H-bond acceptor groups could enhance the molecular interactions with hemicellulose. This work provided a thermodynamic understanding of hemicellulose and IL solvent systems. It highlights the importance of building appropriate solute models, which may be necessary to predict of the other thermodynamic properties in the future.

Catalytic liquefaction of sewage sludge to small molecular weight chemicals

The catalytic hydrotreatment of sewage sludge, the wet solid byproducts from wastewater treatment plants, using supported Ir, Pt, Pd, Ru catalysts had been investigated with different solvent conditions. Reactions were carried out in a batch set-up at elevated temperatures (400 °C) using a hydrogen donor (formic acid (FA) in isopropanol (IPA) or hydrogen gas), with sewage sludge obtained from different sampling places. Sewage sludge conversions of up to 83.72% were achieved using Pt/C, whereas the performance for the others catalysts is different and solvent had a strong effect on the conversion rate and product constitution. The sewage sludge oils were characterised using a range of analytical techniques (GC, GC-MS, GCxGC, GPC) and were shown to consist of monomers, mainly alkanes and higher oligomers.