Model-based method for the screening of solvents for chemical reactions

Design Strategy and Application of Deep Eutectic Solvents for Green Synthesis of Nanomaterials

The first report of deep eutectic solvents (DESs) was released in 2003 and was identified as a new member of ionic liquid (IL), involving innovative chemical and physical characteristics. Using green solvent technology concerning economical, practical, and environmental aspects, DESs open the window for sustainable development of nanomaterial fabrication. The DESs assist in different fabrication processes and design nanostructures with specific morphology and properties by tunable reaction conditions. Using DESs in synthesis reactions can reduce the required high temperature and pressure conditions for decreasing energy consumption and the risk of environmental contamination. This review paper provides the recent applications and advances in the design strategy of DESs for the green synthesis of nanomaterials. The strategy and application of DESs in wet-chemical processes, nanosize reticular material fabrication, electrodeposition/electrochemical synthesis of nanostructures, electroless deposition, DESs based nano-catalytic and nanofluidic systems are discussed and highlighted in this review.

Screening of Double Solvents Based on Multi-Index Evaluation Method for the Selective Separation m-Cresol from Model Oil

Ionic liquid (IL) as an extractant is an effective method for separating oil-phenol mixtures. However, relatively high neutral oil entrainment can lead to oil loss and a remarkable reduction in phenol purity. To reduce the entrainment of neutral oil in the extraction process, a double-solvent extraction system composed of IL 1-ethyl-3-methyl imidazolium acetate ([C₂mim][Ac]) and an organic solvent was investigated. The multi-index evaluation method was used to screen the conventional organic solvent. The double-solvent extraction experiments were employed to verify the accuracy of the COSMO-RS prediction. Eighteen organic solvents, the interaction energy with m-cresol and cumene (neutral oil), their extraction ability, and mutual solubility with [C₂mim][Ac], were calculated by COSMO-RS. Alkane and cycloalkane were screened due to their strong interaction and low distribution coefficient with cumene, as well as the low mutual solubility with [C₂mim][Ac]. The experimental results were in good agreement with the COSMO-RS prediction, and cyclopentane as an organic solvent had the lowest distribution coefficient and entrainment of cumene because of its strong nonpolarity and hydrogen-bond repulsive interaction, which was explained by the σ-profile and σ-potential analysis. When the cyclopentane-to-[C₂mim][Ac] mass ratio increased from 0.0 to 3.0, the entrainment of cumene was evidently declined from 39.5 to 5.8% and the selectivity to m-cresol was improved from 378.0 to 1058.5.

A Survey of Datasets, Preprocessing, Modeling Mechanisms, and Simulation Tools Based on AI for Material Analysis and Discovery

Research has become increasingly more interdisciplinary over the past few years. Artificial intelligence and its sub-fields have proven valuable for interdisciplinary research applications, especially physical sciences. Recently, machine learning-based mechanisms have been adapted for material science applications, meeting traditional experiments' challenges in a time and cost-efficient manner. The scientific community focuses on harnessing varying mechanisms to process big data sets extracted from material databases to derive hidden knowledge that can successfully be employed in technical frameworks of material screening, selection, and recommendation. However, a plethora of underlying aspects of the existing material discovery methods needs to be critically assessed to have a precise and collective analysis that can serve as a baseline for various forthcoming material discovery problems. This study presents a comprehensive survey of state-of-the-art benchmark data sets, detailed pre-processing and analysis, appropriate learning model mechanisms, and simulation techniques for material discovery. We believe that such an in-depth analysis of the mentioned aspects provides promising directions to the young interdisciplinary researchers from computing and material science fields. This study will help devise useful modeling in the materials discovery to positively contribute to the material industry, reducing the manual effort involved in the traditional material discovery. Moreover, we also present a detailed analysis of experimental and computation-based artificial intelligence mechanisms suggested by the existing literature.

Effect of Water on Phenol Separation from Model Oil with Ionic Liquids Based on COSMO-RS Calculation and Experimental Study

Ionic liquids (ILs) are widely used in the extraction of phenolic compounds from low-temperature coal tar (LTCT). However, both ILs and LTCT contain a certain amount of water. The existence of water may have a remarkable impact on the phenol separation performance of ILs with different structures. In this work, the capacity and selectivity for m-cresol, as well as the solubility of cumene and dodecane in different IL-H2O mixtures, were firstly calculated by the conductor-like screening model for real solvents (COSMO-RS) at infinite dilution. The calculation covers ILs with different anionic and anionic structures and different water contents. To explore the effect of water in IL on separation performance, 1-ethyl-3-methyl imidazolium acetate ([C2mim][Ac]) was selected as the representative IL, and then the molecular interactions between the [C2mim][Ac]-H2O mixture solvent and solute (including m-cresol, cumene, and dodecane) were analyzed by COSMO-RS. The results indicated that both water and m-cresol could form hydrogen bonds with [C2mim][Ac]. The competition between them leads to decreasing separation performance for m-cresol of the [C2mim][Ac]-H2O mixture with increasing water content. Moreover, through analyses of m-cresol extraction efficiency, distribution coefficient, selectivity, and entrainment of cumene and dodecane, the experimental results confirmed that the presence of water in [C2mim][Ac] had a negative effect on the separation of m-cresol. The viscosity and UV-vis spectra of the [C2mim][Ac]-H2O mixture were also measured. Water in ILs should be removed as much as possible to ensure a better dephenolization effect and avoid phenol containing wastewater.

Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis

Rational solvent selection remains a significant challenge in process development. Here we describe a hybrid mechanistic-machine learning approach, geared towards automated process development workflow. A library of 459 solvents was used, for which 12 conventional molecular descriptors, two reaction-specific descriptors, and additional descriptors based on screening charge density, were calculated. Gaussian process surrogate models were trained on experimental data from a Rh(CO)2(acac)/Josiphos catalysed asymmetric hydrogenation of a chiral α-β unsaturated γ-lactam. With two simultaneous objectives - high conversion and high diastereomeric excess - the multi-objective algorithm, trained on the initial dataset of 25 solvents, has identified solvents leading to better reaction outcomes. In addition to being a powerful design of experiments (DoE) methodology, the resulting Gaussian process surrogate model for conversion is, in statistical terms, predictive, with a cross-validation correlation coefficient of 0.84. After identifying promising solvents, the composition of solvent mixtures and optimal reaction temperature were found using a black-box Bayesian optimisation. We then demonstrated the application of a new genetic programming approach to select an appropriate machine learning model for a specific physical system, which should allow the transition of the overall process development workflow into the future robotic laboratories.

Improving Product Specificity of Whole-Cell Alkane Oxidation in Nonconventional Media: A Multivariate Analysis Approach

Two-liquid-phase reaction media have long been used in bioconversions to supply or remove hydrophobic organic reaction substrates and products to reduce inhibitory and toxic effects on biocatalysts. In case of the terminal oxyfunctionalization of linear alkanes by the AlkBGT monooxygenase the excess alkane substrate is often used as a second phase to extract the alcohol, aldehyde, and acid products. However, the selection of other carrier phases or surfactants is complex due to a large number of parameters that are involved, such as biocompatibility, substrate bioavailability, and product extraction selectivity. This study combines systematic high-throughput screening with chemometrics to correlate physicochemical parameters of a range of cosolvents to product specificity and yield using a multivariate regression model. Partial least-squares regression shows that the defining factor for product specificity is the solubility properties of the reaction substrate and product in the cosolvent, as measured by Hansen solubility parameters. Thus the polarity of cosolvents determines the accumulation of either alcohol or acid products. Whereas usually the acid product accumulates during the reaction, by choosing a more polar cosolvent the 1-alcohol product can be accumulated. Especially with Tergitol as a cosolvent, a 3.2-fold improvement in the 1-octanol yield to 18.3  mmol  L^-1 is achieved relative to the control reaction without cosolvents.

Screening of Suitable Ionic Liquids as Green Solvents for Extraction of Eicosapentaenoic Acid (EPA) from Microalgae Biomass Using COSMO-RS Model

Omega-3 poly unsaturated fatty acids (PUFA) particularly eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA), have many health benefits including reducing the risk of cancer and cardiovascular disease. Recently, the use of ionic liquids (ILs) in lipid extraction from microalgae provides the potential to overcome common drawbacks, offers several other benefits. To date, very limited researches are available to focus on extracting microalgae lipid and PUFA in particular by using ILs. The objective of current work is to screen the potential ILs that can be applied in EPA extraction. In this study, fast ILs screening was performed with the help of a conductor like screening model for real solvents (COSMO-RS) and the ILs with higher capacity values for use in extraction of EPA were compared. According to the results, the highest capacity for EPA extraction among 352 screened cation/anion combinations belongs to [TMAm][SO₄]. It is expected to achieve a higher yield of EPA once applying this combination as the solvent in the process of extraction. ILs with small anions were observed to have higher capacities, as well possessing higher charge density compared to larger ones, and therefore, they are more preferable for extraction purposes. Moreover, shorter alkyl chain cations are preferred when using imidazolium-based IL, which agrees with experimental data.