Perspectives and guidelines on thermodynamic modelling of deep eutectic solvents

Application of eco-friendly natural deep eutectic solvents (NADES) in HPLC for separation of complex natural products: Current limitations and future directions

Natural deep eutectic solvents (NADES) have been used in chromatography as extraction media and HPLC mobile phase additives, but only once have they been used as HPLC major mobile phase component. This review illustrates current knowledge and major limitations on use of NADES in HPLC mobile phase as well as to propose possible NADES may be ready for use as HPLC mobile phases and the detectors they can be used with. High viscosity is one of the major roadblocks encountered when using NADES as a mobile phase component in HPLC regardless of detectors employed. A comprehensive review of published literature was conducted to identify articles that focused on using NADES as extraction solvents for natural products, particularly polyphenols or reported NADES viscosities to establish a database of NADES which could be used as HPLC mobile phases under various conditions. Other identified challenges that limit NADES application in HPLC mobile phase include low volatility, NADES wavelength cutoff (UV and Fluorescent detectors) and impurities. Methods for overcoming these limitations are discussed so that NADES may be more integrated into HPLC systems in the future.

Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach

Deep eutectic solvents (DESs) are gaining recognition as environmentally friendly solvent alternatives for diverse chemical processes. Yet, designing DESs tailored to specific applications is a resource-intensive task, which requires an accurate estimation of their physicochemical properties. Among them, viscosity is crucial, as it often dictates a DES's suitability as a solvent. In this study, an artificial neural network (ANN) is introduced to accurately describe the viscosity of DESs and their mixtures with cosolvents. The ANN utilizes molecular parameters derived from σ-profiles, computed using the conductor-like screening model for the real solvent segment activity coefficient (COSMO-SAC). The data set comprises 1891 experimental viscosity measurements for 48 DESs based on choline chloride, encompassing 279 different compositions, along with 1618 data points of DES mixtures with cosolvents as water, methanol, isopropanol, and dimethyl sulfoxide, covering a wide range of viscosity measurements from 0.3862 to 4722 mPa s. The optimal ANN structure for describing the logarithmic viscosity of DESs is configured as 9-19-16-1, achieving an overall average absolute relative deviation of 1.6031%. More importantly, the ANN shows a remarkable extrapolation capacity, as it is capable of predicting the viscosity of systems including solvents (ethanol) and hydrogen bond donors (2,3-butanediol) not considered in the training. The ANN model also demonstrates an extensive applicability domain, covering 94.17% of the entire database. These achievements represent a significant step forward in developing robust, open source, and highly accurate models for DESs using molecular descriptors.

Predicting the formation of NADES using a transformer-based model

The application of natural deep eutectic solvents (NADES) in the pharmaceutical, agricultural, and food industries represents one of the fastest growing fields of green chemistry, as these mixtures can potentially replace traditional organic solvents. These advances are, however, limited by the development of new NADES which is today, almost exclusively empirically driven and often derivative from known mixtures. To overcome this limitation, we propose the use of a transformer-based machine learning approach. Here, the transformer-based neural network model was first pre-trained to recognize chemical patterns from SMILES representations (unlabeled general chemical data) and then fine-tuned to recognize the patterns in strings that lead to the formation of either stable NADES or simple mixtures of compounds not leading to the formation of stable NADES (binary classification). Because this strategy was adapted from language learning, it allows the use of relatively small datasets and relatively low computational resources. The resulting algorithm is capable of predicting the formation of multiple new stable eutectic mixtures (n = 337) from a general database of natural compounds. More importantly, the system is also able to predict the components and molar ratios needed to render NADES with new molecules (not present in the training database), an aspect that was validated using previously reported NADES as well as by developing multiple novel solvents containing ibuprofen. We believe this strategy has the potential to transform the screening process for NADES as well as the pharmaceutical industry, streamlining the use of bioactive compounds as functional components of liquid formulations, rather than simple solutes.

Overview and thermodynamic modelling of deep eutectic solvents as co-solvents to enhance drug solubilities in water

The poor water solubility of active pharmaceutical ingredients (APIs) is a major challenge in the pharmaceutical industry. Co-solvents are sometimes added to enhance drug dissolution. A novel group of co-solvents, the Deep Eutectic Solvents (DES), have gained interest in the pharmaceutical field due to their good solvent power, biodegradability, sustainability, non-toxicity, and low cost. In this study, we first provide an overview of all the literature solubility studies involving a drug or API + water + DES, which can be a valuable list to some researchers. Then, we analyze these systems with focus on each individual drug/API and provide statistical information on each. A similar analysis is carried out with focus on the individual DESs. An investigation of the numeric values of the water-solubility enhancement by the different DESs for various drugs indicates that DESs are indeed effective co-solvents, with varying degrees of solubility enhancement, even up to 15-fold. This is strongly encouraging, indicating the need for further studies to find the most promising DESs for solubility enhancement. However, time-consuming and costly trial and error should be prevented by first screening, using theoretical-based or thermodynamic-based models. Based on this conclusion, the second part of the study is concerned with investigating and suggesting accurate thermodynamic approaches to tackle the phase equilibrium modeling of such systems. For this purpose, a large data bank was collected, consisting of 2009 solubility data of 25 different drugs/APIs mixed with water and 31 different DESs as co-solvents at various DES concentrations, over wide ranges of temperatures at atmospheric pressure. This data bank includes 107 DES + water + drug/API systems in total. The solubility data were then modeled according to the solid-liquid equilibrium framework, using the local composition activity coefficient models of NRTL, and UNIQUAC. The results showed acceptable behavior with respect to the experimental values and trends for all of the investigated systems, with AARD% values of 9.65 % and 14.08 % for the NRTL and UNIQUAC models, respectively. In general, the lower errors of NRTL, as well as its simpler calculation process and the requirement of fewer component parameters, suggest the priority of NRTL over UNIQUAC for use in this field.

Novel hybrid QSPR-GPR approach for modeling of carbon dioxide capture using deep eutectic solvents

In recent years, deep eutectic solvents (DESs) have garnered considerable attention for their potential in carbon capture and utilization processes. Predicting the carbon dioxide (CO2) solubility in DES is crucial for optimizing these solvent systems and advancing their application in sustainable technologies. In this study, we presented an evolving hybrid Quantitative Structure-Property Relationship and Gaussian Process Regression (QSPR-GPR) model that enables accurate predictions of CO2 solubility in various DESs. The QSPR-GPR model combined the strengths of both approaches, leveraging molecular descriptors and structural features of DES components to establish a robust and adaptable predictive framework. Through a systematic evolution process, we iteratively refined the model, enhancing its performance and generalization capacity. By incorporating experimental CO2 solubility data in varied DES compositions and temperatures, we trained the model to capture the intricate solubility behaviour precisely. The analytical capability of the evolving hybrid model was validated against an extensive dataset of experimental CO2 solubility values, demonstrating its superiority over individual QSPR and GPR models. The model achieves high accuracy, capturing the complex interactions between CO2 and DES components under varying thermodynamic conditions. The versatility of the evolving hybrid model was highlighted by its ability to accommodate new experimental data and adapt to different DES compositions and temperatures. The proposed QSPR-GPR model presented a powerful tool for predicting CO2 solubility in DES, providing valuable insights for designing and optimizing solvent systems in carbon capture technologies. The model's remarkable performance enhances our understanding of CO2 solubility mechanisms and contributes to sustainable solutions for mitigating greenhouse gas emissions. As research in DESs progresses, the evolving hybrid QSPR-GPR model offers a versatile and accurate means for predicting CO2 solubility, supporting advancements in carbon capture and utilization processes towards a greener and more sustainable future.

Untapped Potential of Deep Eutectic Solvents for the Synthesis of Bioinspired Inorganic-Organic Materials

Since the discovery of deep eutectic solvents (DESs) in 2003, significant progress has been made in the field, specifically advancing aspects of their preparation and physicochemical characterization. Their low-cost and unique tailored properties are reasons for their growing importance as a sustainable medium for the resource-efficient processing and synthesis of advanced materials. In this paper, the significance of these designer solvents and their beneficial features, in particular with respect to biomimetic materials chemistry, is discussed. Finally, this article explores the unrealized potential and advantageous aspects of DESs, focusing on the development of biomineralization-inspired hybrid materials. It is anticipated that this article can stimulate new concepts and advances providing a reference for breaking down the multidisciplinary borders in the field of bioinspired materials chemistry, especially at the nexus of computation and experiment, and to develop a rigorous materials-by-design paradigm.

Evaluating the status quo of deep eutectic solvent in food chemistry. Potentials and limitations

Conventional organic solvents (e.g., methanol, ethanol, ethyl acetate) are widely used for extraction, reaction, and separation of valuable compounds. Although these solvents are effective, they have disadvantages, including flammability, toxicity, and persistence in the environment. Deep eutectic solvents (DESs) are valued for their biodegradability/low impact on the environment, low cost, and ease of manufacture. The objective of this review was to provide an overview of applications of DES in food chemistry, specifically in regard of extraction of polyphenols (e.g., anthocyanin, rutin, kaempferol, quercetin, resveratrol), protein, carbohydrates (e.g., chitin, pectins), lipids and lipid-soluble compounds (e.g., free fatty acids, astaxanthin, β-carotene, terpenoids), biosensor development, and use in food safety (pyrethroids, Sudan I, bisphenol A, Pb²⁺, Cd²⁺, etc.) over the past five years. A comprehensive analysis and discussion of DES types, preparation, structures, and influencing factors is provided. Furthermore, the potential and disadvantages of using DESs to extract biomolecules were assessed. We concluded that DES is a viable alternative for extracting polyphenols, carbohydrates, and lipids as well as use in food safety monitoring and biosensor development. However, more work is needed to address shortcomings, and determine whether using compounds extracted with DES can be consumed safely.

Current understanding and insights towards protein stabilization and activation in deep eutectic solvents as sustainable solvent media

Deep eutectic solvents (DESs) have emerged as a new class of green, designer and biocompatible solvents, an alternative to conventional organic solvents and ionic liquids (ILs) which are comparatively toxic and non-biodegradable. DESs are eutectic mixtures that are formed when a hydrogen bond acceptor (HBA) is mixed with a hydrogen bond donor (HBD) at particular molar ratios by mechanical grinding or under mild heating conditions. Very recently, these solvents have been the center of attention for researchers in biotechnology, biomedicine and various scientific applications. These environmentally benign solvents have a close analogy with ILs; however, they offer certain unique merits over traditional ILs. DESs display remarkable properties such as easy preparation, tunable composition, biodegradability, recyclability, inherently low toxicity, sustainability and biocompatibility; these special features validate DESs as new potential solvents/co-solvents for biomolecules. Mechanistically, the biocompatibility and protein friendly nature of DESs depend on various factors, which include the composition of the DES, viscosity and hydration level. Therefore, it becomes an essential task to bring together all the studies related to protein behaviour in DESs to unlock their biomolecular proficiency. This review specifically highlights recent insights into the biomacromolecular functionality in DESs, including outlines of the solubilization and stabilization of proteins, long term protein packaging, different extraction methods and enzyme activation in the presence of DESs. A literature survey reveals that DESs act as green media in which the protein structure and activity are retained. In some cases, proteins refolded and enzymatic activity was enhanced several fold in the presence of DESs. Furthermore, we have reviewed the possible mechanistic behaviour behind protein stabilization, refolding and activation in DESs. Overall, the main objective of this review is to explicate the advantages of the introduction of DESs for biomolecules and to demonstrate the versatility of these eco-friendly solvents for future bio-based applications.

Searching for Sustainable Refrigerants by Bridging Molecular Modeling with Machine Learning

We present here a novel integrated approach employing machine learning algorithms for predicting thermophysical properties of fluids. The approach allows obtaining molecular parameters to be used in the polar soft-statistical associating fluid theory (SAFT) equation of state using molecular descriptors obtained from the conductor-like screening model for real solvents (COSMO-RS). The procedure is used for modeling 18 refrigerants including hydrofluorocarbons, hydrofluoroolefins, and hydrochlorofluoroolefins. The training dataset included six inputs obtained from COSMO-RS and five outputs from polar soft-SAFT parameters, with the accurate algorithm training ensured by its high statistical accuracy. The predicted molecular parameters were used in polar soft-SAFT for evaluating the thermophysical properties of the refrigerants such as density, vapor pressure, heat capacity, enthalpy of vaporization, and speed of sound. Predictions provided a good level of accuracy (AADs = 1.3–10.5%) compared to experimental data, and within a similar level of accuracy using parameters obtained from standard fitting procedures. Moreover, the predicted parameters provided a comparable level of predictive accuracy to parameters obtained from standard procedure when extended to modeling selected binary mixtures. The proposed approach enables bridging the gap in the data of thermodynamic properties of low global warming potential refrigerants, which hinders their technical evaluation and hence their final application.

Computer Simulations of Deep Eutectic Solvents: Challenges, Solutions, and Perspectives

Deep eutectic solvents (DESs) are one of the most rapidly evolving types of solvents, appearing in a broad range of applications, such as nanotechnology, electrochemistry, biomass transformation, pharmaceuticals, membrane technology, biocomposite development, modern 3D-printing, and many others. The range of their applicability continues to expand, which demands the development of new DESs with improved properties. To do so requires an understanding of the fundamental relationship between the structure and properties of DESs. Computer simulation and machine learning techniques provide a fruitful approach as they can predict and reveal physical mechanisms and readily be linked to experiments. This review is devoted to the computational research of DESs and describes technical features of DES simulations and the corresponding perspectives on various DES applications. The aim is to demonstrate the current frontiers of computational research of DESs and discuss future perspectives.

Deep Eutectic Solvents: Are They Safe?

Deep eutectic solvents (DESs) are a relatively new type of solvent that have attracted the attention of the scientific community due to their environmentally friendly properties and their versatility in many applications. Many possible DESs have been described and, thus, it is not easy to unequivocally characterize and generalize their properties. This is especially important in the case of the (eco)toxicity information that can be found for these mixtures. In this review, we collect data on the human and environmental toxicity of DESs, with the aim of gathering and exploring the behavioral patterns of DESs. The toxicity data found were analyzed attending to different factors: hydrogen bond donors or acceptors that form part of the eutectic mixture, pH, and the presence of organic acids in the DES molar ratio of the components, or interactions with natural compounds. In the case of ecotoxicity, results generally depend on the biomodel studied, along with other factors that have been also revised. Finally, we also carried out a revision of the biodegradation of DESs.

Current and future perspectives on catalytic-based integrated carbon capture and utilization

There exist several well-known methods with varying maturity for capturing carbon dioxide from emission sources of different concentrations, including absorption, adsorption, cryogenics and membrane separation, among others. The capture and separation steps can produce almost pure CO₂, but at substantial cost for being conditioned for transport and final utilization, with high economical risks to be considered. A possible way for the elimination of this conditioning and cost is direct CO₂ utilization, whether on-site in a further process but within the same plant, or in-situ, coupling both capture and conversion in the same unit. This approach is usually called integrated carbon capture and utilization (ICCU) or integrated carbon capture and conversion (ICCC), and has lately started receiving considerable attention in many circles. As CO₂ is already industrially employed in other sectors, such as food preservation, water treatment and conversion to high added-value chemicals and fuels such as methanol, methane, etc., among others, it is of great interest to explore the global ICCC approach. Catalytic-based processes play a key role in CO₂ conversion, and different technologies are gaining great attention from both academia and industry. However, the 'big picture of ICCU' and in which technology the efforts should focus on at large scale is still unclear. This review analyzes some promising concepts of ICCU specifically on CO₂ catalytic conversion, highlighting their current commercial relevance as well as challenges that have to be faced today and in the next future.

Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures

Deep eutectic solvents (DES) are often regarded as greener sustainable alternative solvents and are currently employed in many industrial applications on a large scale. Bearing in mind the industrial importance of DES—and because the vast majority of DES has yet to be synthesized—the development of cheminformatic models and tools efficiently profiling their density becomes essential. In this work, after rigorous validation, quantitative structure-property relationship (QSPR) models were proposed for use in estimating the density of a wide variety of DES. These models were based on a modelling dataset previously employed for constructing thermodynamic models for the same endpoint. The best QSPR models were robust and sound, performing well on an external validation set (set up with recently reported experimental density data of DES). Furthermore, the results revealed structural features that could play crucial roles in ruling DES density. Then, intelligent consensus prediction was employed to develop a consensus model with improved predictive accuracy. All models were derived using publicly available tools to facilitate easy reproducibility of the proposed methodology. Future work may involve setting up reliable, interpretable cheminformatic models for other thermodynamic properties of DES and guiding the design of these solvents for applications.

Partial Charges Optimized by Genetic Algorithms for Deep Eutectic Solvent Simulations

Deep eutectic solvents (DESs) are a class of solvents often composed of ammonium-based chloride salts and a neutral hydrogen bond donor (HBD) at specific ratios. These cost-effective and environmentally friendly solvents have seen significant growth in multiple fields, including organic synthesis, and in materials and extractions because of their desirable properties. In the present work, a new software called genetic algorithm machine learning (GAML) was developed that utilizes a genetic algorithm (GA) approach to facilitate the development of optimized potentials for liquid simulation (OPLS)-based force field (FF) parameters for eight unique DESs based on three ammonium-based salts and five HBDs at multiple salt:HBD ratios. As an initial test of GAML, partial charges were created for 86 conventional solvents based on neutral organic molecules that yielded excellent overall mean absolute deviations (MADs) of 0.021 g/cm³, 0.63 kcal/mol, and 0.20 kcal/mol compared to experimental densities, heats of vaporization (ΔH_vap), and free energies of hydration (ΔG_hyd), respectively. FFs for DESs constructed from ethylammonium, N,N-diethylethanolammonium, and N-ethyl-N,N-dimethylethanolammonium chloride salts were then parameterized using GAML with exceptional agreement achieved at multiple temperatures for experimental densities, surface tensions, and viscosities with MADs of 0.024 g/cm³, 4.2 mN/m, and 5.3 cP, respectively.

General Design Methodology for Organic Eutectic Electrolytes toward High-Energy-Density Redox Flow Batteries

By virtue of strong molecular interactions, eutectic electrolytes provide highly concentrated redox-active materials without other auxiliary solvents, hence achieving high volumetric capacities and energy density for redox flow batteries (RFBs). However, it is critical to unveil the underlying mechanism in this system, which will be undoubtedly beneficial for their future research on high-energy storage systems. Herein, a general formation mechanism of organic eutectic electrolytes (OEEs) is developed, and it is found that molecules with specific functional groups such as carbonyl (CO), nitroxyl radical (NO•), and methoxy (OCH₃ ) groups can coordinate with alkali metal fluorinated sulfonylimide salts (especially for bis(trifluoromethanesulfonyl)imide, TFSI), thereby forming OEEs. Molecular designs further demonstrate that the redox-inactive methoxy group functionalized ferrocene derivative maintains the liquid OEE at both reduced and oxidized states. Over threefold increase in solubility is obtained (2.8 m for ferrocene derivative OEE) and high actual discharge energy density of 188 Wh L^-1 (75% of the theoretical value) is achieved in the Li hybrid cell. The established mechanism presents new ways of designing desirable electrolytes through molecular interactions for the development of high-energy-density organic RFBs.

Unravelling the nature of citric acid:L-arginine:water mixtures: the bifunctional role of water

The use of water as a component of deep eutectic systems (DES) has raised some questions regarding its influence on the nature of the mixture. Does it form a DES or an aqueous solution and what is the role of water? In this work, the nature of citric acid:l-arginine:water mixtures was explored through phase equilibria studies and spectroscopic analysis. In a first step, PC-SAFT was validated as a predictive tool to model the water influence on the solid liquid equilibria (SLE) of the DES reline using the individual-component approach. Hence, activity coefficients in the ternary systems citric acid:l-arginine:water and respective binary combinations were studied and compared using ePC-SAFT. It was observed that the water-free mixtures citric acid:l-arginine showed positive deviation from Raoult's law, while upon addition of water strong negative deviation from Raoult's law was found, yielding melting depressions around 100 K. Besides these strong interactions, pH was found to become acidic (pH = 3.5) upon water addition, which yields the formation of charged species ([H2Cit]- and [l-arg]+). Thus, the increased interactions between the molecules upon water addition might be caused by several mechanisms such as hydrogen bonding or ionic forces, both being induced by water. For further investigation, the liquid mixtures citric acid:l-arginine:water were studied by FTIR and NMR spectroscopy. FTIR spectra disproved a possible solubility enhancement caused by salt formation between citric acid and l-arginine, while NMR spectra supported the formation of a hydrogen bonding network different from the binary systems citric acid:water and l-arginine:water. Either being a DES or other type of non-ideal solution, the liquefaction of the studied systems is certainly caused by a water-mediator effect based on the formation of charged species and cross interactions between the mixture constituents.

A Review of the Use of Eutectic Solvents, Terpenes and Terpenoids in Liquid–liquid Extraction Processes

Diverse and abundant applications of the eutectic solvents have appeared in the last years. Their promising tunable properties, eco-friendly character and the possibility of being prepared from numerous compounds have led to the publication of numerous papers addressing their use in different areas. Terpenes and terpenoids have been employed in the formulation of eutectic solvents, though they also have been applied as solvents in extraction processes. For their hydrophobic nature, renewable character, low environmental impact, cost and being non-hazardous, they have also been proposed as possible substitutes of conventional solvents in the separation of organic compounds from aqueous streams, similarly to hydrophobic eutectic solvents. The present work reviews the application of eutectic solvents in liquid–liquid extraction and terpenes and terpenoids in extraction processes. It has been made a research in the current state-of-the-art in these fields, describing the proposed applications of the solvents. It has been highlighted the scale-up feasibility, solvent regeneration and reuse procedures and the comparison of the performance of eutectic solvents, terpenes and terpenoids in extraction with conventional organic solvents or ionic liquids. Ultimately, it has been also discussed the employ of predictive methods in extraction, the reliability of thermodynamic models in correlation of liquid–liquid equilibria and simulation of liquid–liquid extraction processes.