Design Of Deep Eutectic Solvents Research Articles

Harnessing the potential of deep eutectic solvents in biocatalysis: design strategies using CO2 to formate reduction as a case study.

Deep eutectic solvents (DESs) have emerged as green solvents with versatile applications, demonstrating significant potential in biocatalysis. They often increase the solubility of poorly water-soluble substrates, serve as smart co-substrates, modulate enzyme stereoselectivity, and potentially improve enzyme activity and stability. Despite these advantages, screening for an optimal DES and determining the appropriate water content for a given biocatalytic reaction remains a complex and time-consuming process, posing a significant challenge. This paper discusses the rational design of DES tailored to a given biocatalytic system through a combination of experimental screening and computational tools, guided by performance targets defined by solvent properties and process constraints. The efficacy of this approach is demonstrated by the reduction of CO2 to formate catalyzed by NADH-dependent formate dehydrogenase (FDH). By systematically analyzing FDH activity and stability, NADH stability (both long-term and short-term stability after solvent saturation with CO2), and CO2 solubility in initially selected glycerol-based DESs, we were able to skillfully guide the DES screening process. Considering trade-offs between experimentally determined performance metrics of DESs, 20% solution of choline chloride:glycerol in phosphate buffer (ChCl:Gly80%B) was identified as the most promising solvent system for a given reaction. Using ChCl:Gly as a co-solvent resulted in an almost 15-fold increase in FDH half-life compared to the reference buffer and stabilized the coenzyme after the addition of CO2. Moreover, the 20% addition of ChCl:Gly to the buffer improved the volumetric productivity of FDH-catalyzed CO2 reduction in a batch system compared to the reference buffer. The exceptional stability of the enzyme in this co-solvent system shows great potential for application in continuous operation, which can significantly improve process productivity. Additionally, based on easily measurable physicochemical solvent properties and molecular descriptors derived from COSMO-RS, QSAR models were developed, which successfully predicted enzyme activity and stability, as well as coenzyme stability in selected solvent systems with DESs.

The Reaction between K2CO3 and Ethylene Glycol in Deep Eutectic Solvents.

Understanding intermolecular interactions is important for the design of deep eutectic solvents. Herein, potassium carbonate (K2CO3) and ethylene glycol (EG) are used to form deep eutectic solvents. The interactions between K2CO3 and EG are studied using nuclear magnetic resonance (NMR) and Fourier transform infrared (FTIR) spectra. Interestingly, the interaction results indicate that the carbonate anion CO32- can react with EG to form EG-based organic carbonate, which can occur even at room temperature. The possible reaction steps between K2CO3 and EG are presented. As K2CO3 can be prepared from CO2 and KOH, the findings of this work may provide a promising strategy for CO2 capture and conversion.

Design of deep eutectic solvents for multiple perfluoroalkyl substances removal: Energy-based screening and mechanism elucidation

The current landscape of perfluoroalkyl substances (PFAS) extraction methodologies presents significant challenges, particularly for multiple PFAS with different carbon chain lengths. This study introduced an energy-driven strategic approach for screening deep eutectic solvents (DESs) to effectively remove a diverse range of PFAS, including perfluoroalkylcarboxylic acids (PFCAs), perfluoroalkanesulfonic acids (PFSAs), and perfluoroalkyl amides (FAAs), from contaminated environments (total 13 target compounds). Utilizing energy-based screening, we identified DES candidates with high affinity for a spectrum of PFAS compounds from 1234 potential starting materials of eutectic systems. Key findings revealed the superior removal efficiency of tributylphosphineoxide/2-methylpiperazine system, exceeding 99 % for various PFAS with different carbon chain lengths in real environmental water samples. Additionally, we elucidated the molecular interactions between DESs and PFAS through ab initio molecular dynamics (AIMD) simulations, providing valuable insights into the mechanisms governing the removal process. The mechanism of extraction involves hydrogen bond network topology and structural organization, with DESs capable of extracting PFAS while maintaining a weakly aggregated state of target molecules and minimizing the impact on the intrinsic structures of DES. The proposed system forms a dynamic, complementary, and flexible non-covalent interaction network structure with PFAS. The study advances the understanding of DES as a designable, effective, and sustainable alternative to conventional solvents for PFAS remediation, offering a significant contribution to environmental chemistry and green technology.

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.

Rational design of deep eutectic solvents for the stabilization of dehydrogenases: an artificial neural network prediction approach.

Stabilized enzymes are crucial for the industrial application of biocatalysis due to their enhanced operational stability, which leads to prolonged enzyme activity, cost-efficiency and consequently scalability of biocatalytic processes. Over the past decade, numerous studies have demonstrated that deep eutectic solvents (DES) are excellent enzyme stabilizers. However, the search for an optimal DES has primarily relied on trial-and-error methods, lacking systematic exploration of DES structure-activity relationships. Therefore, this study aims to rationally design DES to stabilize various dehydrogenases through extensive experimental screening, followed by the development of a straightforward and reliable mathematical model to predict the efficacy of DES in enzyme stabilization. A total of 28 DES were tested for their ability to stabilize three dehydrogenases at 30°C: (S)-alcohol dehydrogenase from Rhodococcus ruber (ADH-A), (R)-alcohol dehydrogenase from Lactobacillus kefir (Lk-ADH) and glucose dehydrogenase from Bacillus megaterium (GDH). The residual activity of these enzymes in the presence of DES was quantified using first-order kinetic models. The screening revealed that DES based on polyols serve as promising stabilizing environments for the three tested dehydrogenases, particularly for the enzymes Lk-ADH and GDH, which are intrinsically unstable in aqueous environments. In glycerol-based DES, increases in enzyme half-life of up to 175-fold for Lk-ADH and 60-fold for GDH were observed compared to reference buffers. Furthermore, to establish the relationship between the enzyme inactivation rate constants and DES descriptors generated by the Conductor-like Screening Model for Real Solvents, artificial neural network models were developed. The models for ADH-A and GDH showed high efficiency and reliability (R2 > 0.75) for in silico screening of the enzyme inactivation rate constants based on DES descriptors. In conclusion, these results highlight the significant potential of the integrated experimental and in silico approach for the rational design of DES tailored to stabilize enzymes.

Machine learning prediction of delignification and lignin structure regulation of deep eutectic solvents pretreatment processes

Prediction of the pretreatment efficiency of lignocellulosic biomass with ternary deep eutectic solvents (DES) containing Lewis acids by machine learning (ML). Principal component analysis, partial least square method, spearman correlation matrix, random forest, extreme gradient boosting and deep neural network were used to elucidate the correlation between 77 variables and the mechanism of lignin depolymerization. The effects of raw material composition, reaction conditions, physicochemical properties of DES and structural parameters in lignin on 9 target variables including β-O-4 bond, β-β bond, β-5 bond, weight average molecular weight, number average molecular weight, polydispersity index, ratio of syringyl units to guaiacyl units, content of phenolic hydroxyl groups and delignification were analyzed. Multivariate analysis showed that temperature, polarity related parameters of HBD and acidity of Lewis acids contributed significantly to the degree of lignin depolymerization. The types and fracture mechanisms of the bonds between different structural units of lignin can be determined by the analysis of structural parameters. XGBoost model has the best performance among all the ML models, and the R square of the test sets for the target variables is above 0.76. Feature importance analysis showed that structural parameters significantly affected the pretreatment effect. The physical and chemical parameters of HBD, such as dipole moment, Log P and surface tension should be paid attention to in the design of DES. The study of the weak intermolecular forces in the lignin and DES systems is beneficial to reveal the mechanism of the pretreatment process. This study provides novel insights into the structural regulation and high-value utilization of lignin in the process of DES pretreatment of lignocellulosic biomass.

Molecular design and experimental study of deep eutectic solvent extraction of keratin derived from feathers

Feather keratin has a complex structure, hard texture and must be treated to improve its bioavailability. In this paper, according to the designability of DES, some deep eutectic solvents (DESs) were prepared to degrade feathers and extract keratin. Calculations by quantum chemical methods showed that DESs were considered molecular scissors with the ability to break initial hydrogen bonds and form new bonds only when the Gibbs free energy change for the degradation process was ΔG < 0, i.e., hydrogen binding energy ΔE < −0.3038 kcal/mol. Then, the degradation mechanism was predicted to provide guidance for the molecular design of DES. Finally, experimental results showed that the same ratio of choline chloride-based DESs had higher catalytic performance, in which [ChCl][P][ZnCl2] 1:5:2 was used with a high yield of keratin of 85.46 %. DES had a high catalytic performance after multiple recycling cycles and this method has no H2S gas generation, which improves the atomic utilization.

Comparison of polyol-based deep eutectic solvents (DESs) on pretreatment of moso bamboo (Phyllostachys pubescens) for enzymatic hydrolysis

Four polyol-based deep eutectic solvents (DESs) were designed for bamboo pretreatment and enzymatic hydrolysis , and the pretreatment conditions including temperature, time and acid dosage in the optimized DES were comprehensively evaluated. The results showed that the components separation and saccharification of bamboo could be promoted by polyol-based DESs pretreatment, which are positively correlated with the hydroxyl contents of hydrogen bond donor in DESs, except for sorbitol-based DES. Excessive hydroxyl contents could lead to the increase of DES viscosity and lignin deposition, which had a negative effect on saccharification. Meanwhile, the optimum pretreatment conditions in xylitol-based DES (ChCl/XL) were determined to be 120 °C for 3 h with 1wt% acid, and 96.08% of glucose yield was recorded. The recycling experiment indicated that ChCl/XL retained acceptable pretreatment performance. This work gives novel insights into the design and selection of polyol-based DES for bamboo pretreatment and enzymatic hydrolysis. • A novel insights into polyol DES pretreatment was designed for enzymatic hydrolysis. • The optimum pretreatment solvent is xylitol-based DES (ChCl/XL). • ChCl/XL can be adjusted by adding acid to keep its capability after multiple cycles. • Glucose yield was 96.08% at the optimal condition (120 °C, 3 h, 1wt% acid).

Design of deep eutectic solvents with multiple-active-sites for HCl separation and storage

The exploiting of efficient, reversible and environmentally friendly hydrogen chloride (HCl) absorbent is of great significance for the recovery and high-value utilization of by-product HCl. Herein, we propose a new strategy to design deep eutectic solvents (DESs) with multiple-active-sites to effectively absorb HCl. Therefore, five new DESs were designed and prepared by pairing 1,3-dimethyl-2-imidazolidinone (DMI) with amide derivatives or LiCl. Among them, DMI + 1,3-dimethylurea (2:1) showed the unprecedented HCl capacity of 2.16 mol/mol at 303.2 K and 1 bar, surpassing that of water, DESs and ILs reported in literature. Spectroscopic characterization and quantum-chemical calculations showed that such high HCl capacity originates from multiple interaction sites between DESs and HCl. DMI + 1,3-dimethylurea (2:1) also exhibited the advantages of fast absorption of HCl, good recyclability, good selectivity and acceptable cost. A new reaction equilibrium thermodynamic model was developed to correlate HCl solubility data. The absorption rate of HCl in DESs was measured systematically for the first time. These DESs with multiple-active-sites offer improvements over commonly used absorbents, indicating the promise for industrial application in HCl separation and storage.

Liquefying Flavonoids with Terpenoids through Deep Eutectic Solvent Formation.

The formation of deep eutectic solvents (DES) is tied to negative deviations to ideality caused by the establishment of stronger interactions in the mixture than in the pure DES precursors. This work tested thymol and menthol as hydrogen bond donors when combined with different flavonoids. Negative deviations from ideality were observed upon mixing thymol with either flavone or flavanone, two parent flavonoids that only have hydrogen bond acceptor (HBA) groups, thus forming non-ionic DES (Type V). On the other hand, the menthol systems with the same compounds generally showed positive deviations from ideality. That was also the case with the mixtures containing the more complex hydroxylated flavonoid, hesperetin, which resulted in positive deviations when mixed with either thymol or menthol. COSMO-RS successfully predicted the behavior of the solid-liquid phase diagram of the studied systems, allowing for evaluation of the impact of the different contributions to the intermolecular interactions, and proving to be a good tool for the design of DES.

Why do ammonium salt/phenol-based deep eutectic solvents show low viscosity?

• Glycolic acid (GA) DESs exhibit higher activation energy compared with phenolics DESs. • A strong charge transfer complex was found between GA and ammonium salt. • Hydrogen bonds of GA DES are partially covalent and partially electrostatic. • Interaction network stability of GA DES is more robust owing to strong covalency. • This work paves the way for rational design of novel DES with low viscosity. The viscosity of deep eutectic solvents (DESs) plays an important role in determining how they are used industrially. In order to gain a deeper insight into the parameters which affect the viscosity of ionic DES, a series of systems composed of ammonium salts and two types of representative donors were prepared and characterized. They were investigated by quantum-chemistry calculations and molecular dynamics simulations. The viscosity of phenol/4-methylphenol-based system is much lower than that of glycolic acid-based system. Moreover, DESs containing glycolic acid exhibit higher activation energy values compared with DESs containing phenolics. It was found the existence of a strong charge transfer complex between glycolic acid and ammonium salt, thus suggesting its vital role in the fluidity difference of studied mixtures. The hydrogen bonds of glycolic acid-based system are partially covalent and partially electrostatic, manifested via atoms in molecules (AIM) analysis. Additionally, Cl − ⋯HO phenolic hydroxyl is expected to be less covalent than Cl − ⋯HO carboxyl , which is also identified by lower delocalization index in the AIM basin. The interaction network stability of glycolic acid-based DES is more robust than that of phenolics-based one due to the strong covalency of hydrogen bond. This is the main reason that ammonium salt/phenol-based DESs show low viscosity. This work gives new perspectives on more rational design of novel DES with low viscosity.

Targeted Delivery of Doxorubicin in HeLa Cells Using Self‐Assembled Polymeric Nanocarriers Guided by Deep Eutectic Solvents

AbstractGreen material/solvents have been extensively investigated for biomedical applications, including drug delivery applications.In this work, the material design of deep eutectic solvents (DESs) grafted chitosan (CS) with biotin nanocarrier was synthesized from green solvents or deep eutectic solvents and evaluated for sustained anticancer drug release of doxorubicin (DOX) drug. The chemical structure and physical properties of the DESs, DES‐g‐CS‐Biotin, and DOX loaded polymeric nanocarrier were investigated via NMR, FT‐IR, and density analysis. The surface morphology and size of the polymeric nanocarrier were determinedby Atomic Force Microscopy (AFM), Scanning Electron Microscopy (SEM), and Transmission Electron Microscopy (TEM), and Dynamic Light Scattering (DLS) techniques. Furthermore, the sustained release of DOX loaded polymeric nanocarrier was studied usingUV‐visible spectroscopy at the λmax value of 325 nm. The sustained release of drug discharge from the polymeric nanocarrier was evaluated under various physiological conditions.Moreover, the cell viability and cytotoxicity effects against polymeric nanocarrier and DOX loaded polymeric nanocarrier were evaluated using VERO and HeLa cell lines. In conclusion, the polymeric nanocarrier, with deep eutectic solvent enhances the bioavailability, and biocompatibility to biological system improves cancer chemotherapy treatment.

Using COSMO-RS in the Design of Deep Eutectic Solvents for the Extraction of Antioxidants from Rosemary

Trial and error remain the most common method to select the right solvent for the extraction of natural products, in particular when dealing with novel, poorly studied solvents such as deep eutecti...

Quantum Chemistry Insight into the Interactions Between Deep Eutectic Solvents and SO2.

A systematic research work on the rational design of task specific Deep Eutectic Solvents (DES) has been carried out via density functional theory (DFT) in order to increase knowledge on the key interaction parameters related to efficient SO2 capture by DES at a molecular level. A total of 11 different DES structures, for which high SO2 affinity and solubility is expected, have been selected in this work. SO2 interactions in selected DES were investigated in detail through DFT simulations and this work has generated a valuable set of information about required factors at the molecular level to provide high SO2 solubility in DES, which is crucial for enhancing the current efficiency of the SO2 capture process and replacing the current state of the art with environmentally friendly solvents and eventually implementing these materials in the chemical industry. Results that were obtained from DFT calculations were used to deduce the details of the type and the intensity of the interaction between DES and SO2 molecules at various interaction sites as well as to quantify short-range interactions by using various methods such as quantum theory of atoms in a molecule (QTAIM), electrostatic potentials (ESP) and reduced density gradients (RDG). Systematic research on the molecular interaction characterization between DES structures and SO2 molecule increases our knowledge on the rational design of task-specific DES.

A highly selective and efficient method for the production of 5-hydroxymethylfurfural from dehydration of fructose using SACS/DES catalytic system

Our findings could have an impact on the synthesis of HMF and the rational design of deep eutectic solvents (DES) for liquid‐phase separations of HMF in biomass processing. Brønsted acidic sites in the case of sulfonated amorphous carbon–silica (SACS) led to excellent selectivity in the dehydration of fructose in deep eutectic solvents. The deep eutectic solvents were found to play a crucial role to suppress the formation of undesired products and make the work-up separation of HMF easier. The combination of amorphous carbon with deep eutectic solvent allowed to conduct a transformation of fructose to HMF with 67% yield and 100% selectivity towards HMF at 110 °C in 4 h. The excellent selectivity, work-up simplification, and recyclable catalytic system provide a cost-effective strategy for the synthesis of HMF.

On the behaviour of aqueous solutions of deep eutectic solvents at lipid biomembranes

The cytotoxicity of deep eutectic solvents is studied at molecular level using molecular dynamics simulations with a 1-palmitoyl-2-oleoylphosphatidylcholine lipid bilayer as a model of cell membranes and its interaction with eleven choline chloride – based deep eutectic solvents considering different hydrogen bond donors that allows to cover a wide spectrum of molecular variations. The possible insertion of molecules forming the deep eutectic solvent in the lipid bilayer and the changes in bilayer properties upon interaction with DESs are analysed as a function of the molecular structure of the considered hydrogen bond donors. The results confirm the pivotal role of the used hydrogen bond donor for deep eutectic solvents development on the interaction and insertion in the lipid bilayer, and thus on the deep eutectic solvent cytotoxicity. Likewise, molecular dynamics studies on deep eutectic solvents – lipid bilayers are a suitable tool for in silico screening and design of deep eutectic solvents with suitable environmental and toxicity profiles.