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. However, the question of which machine-learning approaches are most appropriate remains. In the present study, we examine how different ML architectures, ranging from tree-based approaches to feed-forward artificial neural networks, perform in generating nonlinear multivariate quantitative structure-property relationship models for the prediction of the temperature- and pressure-dependent surface tension of and speed of sound in ILs over a wide range of surface tensions (16.9-76.2 mN/m) and speeds of sound (1009.7-1992 m/s). The ML models are further interrogated using the powerful interpretation method, shapley additive explanations. We find that several different ML models provide high accuracy, according to traditional statistical metrics. The decision tree-based approaches appear to be the most accurate and precise, with extreme gradient-boosting trees and gradient-boosting trees being the best performers. However, our results also indicate that the promise of using machine-learning to gain deep insights into the underlying physics driving structure-property relationships in ILs may still be somewhat premature.

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. Various analytical and computational tools were employed to understand the pretreatment efficacy of these ILs. This is the first study to demonstrate the ease of synthesis of these renewable ILs, and therefore, opens the door for a new class of "Schiff-base ILs" for further investigation that could also be designed to be task specific.

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

Carbon dioxide (CO2) 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 CO2 capture. Chemically reactive DESs, which form chemical bonds with the CO2, are superior to nonreactive, physically based DESs for CO2 absorption. However, there are no accurate computational models that provide accurate predictions of the CO2 solubility in chemically reactive DESs. Here, we develop machine learning (ML) models to predict the solubility of CO2 in chemically reactive DESs. As training data, we collected 214 data points for the CO2 solubility in 149 different chemically reactive DESs at different temperatures, pressures, and DES molar ratios from published work. The physics-driven input features for the ML models include σ-profile descriptors that quantify the relative probability of a molecular surface segment having a certain screening charge density and were calculated with the first-principle quantum chemical method COSMO-RS. We show here that, although COSMO-RS does not explicitly calculate chemical reaction profiles, the COSMO-RS-derived σ-profile features can be used to predict bond formation. Of the models trained, an artificial neural network (ANN) provides the most accurate CO2 solubility prediction with an average absolute relative deviation of 2.94% on the testing sets. Overall, this work provides ML models that can predict CO2 solubility precisely and thus accelerate the design and application of chemically reactive DESs.

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.

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. The model accounts for bubbles, acoustic impedance mismatch at interfaces, and treats the ILs as incompressible, Newtonian, and saturated with argon. Following a statistical analysis of the simulation results, we determined that viscosity and ultrasound input power are the most significant variables affecting the intensity of the acoustic pressure field (P), the volume of cavitation zones (V), and the magnitude of the maximum acoustic streaming surface velocity (u). V and u increase with the increase of ultrasound input power and the decrease in viscosity, whereas the magnitude of negative P decreases as ultrasound power and viscosity increase. Probe immersion depth positively correlates with V, but its impact on P and u is insignificant. 1-alkyl-3-methylimidazolium-based ILs yielded the largest V and the fastest acoustic jets - 0.77 cm3 and 24.4 m s-1 for 1-ethyl-3-methylimidazolium chloride at 60 W. 1-methyl-3-(3-sulfopropyl)-imidazolium-based ILs generated the smallest V and lowest u - 0.17 cm3 and 1.7 m s-1 for 1-methyl-3-(3-sulfopropyl)-imidazolium p-toluene sulfonate at 20 W. Sonochemiluminescence experiments validated the model.

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 (HE) of lignin, respectively. Based on the COSMO-RS computed thermodynamic properties (ln(γ) and HE) 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.

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. For this purpose, both theoretical and experimental studies were conducted. Initially, the conductor-like screening model for real solvents model was employed to screen the suitable antisolvents for the separation of sugars from the ILs. From the screening study, dichloromethane (DCM) and 1,2-dichloroethane were found to be the better antisolvents for the separation process. With the selected antisolvents, precipitation experiments were conducted for the mixtures involving four different sugars and three ILs at different experimental conditions. The process variables such as different antisolvents, sugars, ILs, antisolvent-IL molar ratios, and temperatures were examined in terms of their effect on sugar removal and IL recovery. DCM was found to be the most suitable antisolvent in this study with 90-99% of sugar removal and 80-98% of IL recovery. Further, molecular dynamics simulations were adopted to understand the structural properties of carbohydrates with ILs and antisolvents via interaction energies, hydrogen bonding, and coordination numbers. It was observed that the interaction energy between the sugars and IL plays a critical role in the removal of sugar. Higher the interaction energy between the sugars and IL, lower is the sugar removal.

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. The classical molecular dynamic (MD) simulation technique is then employed to investigate and compare the experimental phase behavior of a DES-quinoline-heptane ternary system. For performing the MD simulations, two experimental feed points are considered which gave high selectivity and distribution coefficient values. The interaction energies of different species and the structural properties such as radial distribution functions, average number of hydrogen bonds, and spatial distribution functions (SDFs) are then computed. It is found that the cation within the HBA, namely, MTP, possesses favorable interactions with quinoline when compared to HBD or anion (Br). MTP here acts as a HBA and contributes to the hydrogen bonding with quinoline, which results in higher experimental selectivity values. The calculations of SDFs further reveal the fact that the DES molecules are evenly distributed around the active sites of the quinoline molecule, whereas heptane molecules are found to be distributed around the nonactive sites of the aromatic ring.

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. Process parameters investigated include sulfuric acid concentration (0.2-1 M), reaction time (30-120 min) and temperature (80-121 °C). At the optimum condition (0.2 M sulfuric acid, 121 °C and 120 min), 97.6% of hemicellulose was converted into xylobiose (18.02 mg/g), xylose (225.2 mg/g), arabinose (20.2 mg/g) with low concentration of furfural (4.6 mg/g). Furthermore, the process parameters were statistically optimized using response surface methodology based on central composite design. Due to the presence of low concentration of fermentative inhibitors, 78.6 and 82.8% of theoretical ethanol yield were attained during the fermentation of non-detoxified and detoxified hydrolyzates, respectively, using Pichia stipitis 3498 wild strain, in a techno-economical way.

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 (42.21%) was obtained at lower temperature (180 °C), however longer reaction time (25 min). X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, and scanning electron microscopy (SEM) analysis were used to characterise treated and untreated bamboo samples. The XRD profile revealed that crystallinity of bamboo increased to 71.90% with increase in temperature up to 210 °C and decreased thereafter to 70.92%. The first-order reaction kinetic model was used to fit the experimental data to obtain rate constants. From the Arrhenius plot, activation energy and pre-exponential factor at 25 min time were found to be 17.97 kJ mol(-1) and 0.154 min(-1), respectively.