Solvent Screening Research Articles

Significantly improve film formability of acetylated xylans by structure optimization and solvent screening

Acetylated xylans have great potential in fabricating functional film and coating materials, which need a good solubility/dispersibility and film formability in an easily evaporable solvent. However, the changes of film formability with degree of substitution by acetyls (DSAc) in different solvent systems for xylans have not been extensively studied, which limit the application of acetylated xylans in film materials. In this study, acetylated xylans with DSAc of 0–2 were prepared and the effects of acetyl groups on solubility/dispersibility, crystallinity and film formability of xylans in water and chloroform solvent systems were investigated. Due to the change of polarity, xylans with DSAc of 0–0.62 are only soluble in water solvents, while xylans with DSAc of 1.13–2 are only soluble in chloroform/ethanol (70/30 v/v) organic solvents. We have found that the film formability of acetylated xylans is highly related to their solubility and crystallization. Film formable xylans all had good solubility in the cast solvents. However, although with good solubility, xylans with DSAc of 0–0.3 and DSAc of 1.76–2 cannot form intact films, which is due to the forming of xylan hydrate crystals and xylan diacetate crystals. With the increase of DSAc, the mechanical property of xylan film increases initially at low DSAc and decreases at high DSAc. This study provides theoretical basis for applying xylans and their derivatives in advanced functional film and coating materials with great biocompatibility and biodegradability.

A comparative study of polyamine and piperazine as promoter for CO2 absorption performance in aqueous methyldiethanolamine blend system: 430 MW power plant data simulation and economic assessment

Fossil fuel power plants play a significant role in fulfilling global energy demands but are also responsible for high CO2 emissions. Amine-based post-combustion CO2 capture (PCC) is one of the technologies used in such power plants to reduce CO2 emissions. However, high energy consumption in amine-based PCC technology causes a reduction in the overall energy efficiency of power plants. The energy consumption significantly depends on the solvents used in the PCC process. Therefore, in this work, we performed CO2 absorption in the piperazine (PZ), triethylenetetramine (TETA), and N-methyldiethanolamine (MDEA) blend and tri-blend solvents. For the absorption performance and energy consumption calculation, a simulation is performed for the 430 MW power plant data in the DWSIM software. Initially, solvent screening was done, and four solvents are selected based on the optimum energy consumption of various solvents. Further, the solvents’ performance in terms of energy consumption is done based on the effect of different temperatures, CO2 composition in feed, and %CO2 recovery. Overall, the TETA+PZ MDEA tri-blend solvent showed optimum energy consumption for most of the conditions. Additionally, a techno-economy assessment was done considering annual capital cost and annual operating cost, and results revealed that the cost per ton of CO2 capture in 5 %TETA+ 5 %PZ+ 20 %MDEA solvent is 27.33 % lesser than industrial MEA solvent, and 1.56 % lower than 10 %PZ+ 20 %MDEA.

A green approach for genistein and apigenin extraction optimization from by-products of soybean crops

Soybeans’ genistein and its isomer apigenin are widely studied bioactive compounds owing to their therapeutic potential for treating various health disfunctions. A green extraction of both from soy by-products could lead to a valuable and sustainable approach to adding value to these materials in a biorefinery context. Here Hansen Solubility Parameters (HSP) and the Conductor-like Screening MOdel for Real Solvents (COSMO-RS) were applied to screen green molecular solvents and Natural Eutectic Solvents for the extraction of genistein and apigenin from soy by-products. The predicted solubilities of genistein and apigenin in 18 shortlisted candidates were experimentally tested by dynamic maceration, the most industrially implemented natural products extraction technique. EtOH:H2O (8:2, v/v) and natural eutectic solvent (NAES) betaine:ethylene glycol (1:2, mol/mol) showed the highest performance. These were selected for extraction optimizations by Design of Experiments from soy branches, the largest by-product by mass. The optimum condition of each solvent was applied to extract all other parts of soy collected post-mechanical harvesting. The highest value of apigenin, 591.49 ± 26.7 μg/g, was achieved from soy pods with EtOH:H2O (8:2, v/v), while the highest of genistein, 54.04 ± 3.39 μg/g, was achieved from soybeans using the same solvent. Our findings highlight the necessity of exercising caution when interpreting in silico outcomes in the context of metabolite extractions from complex matrices. A trade-off between in silico solvent screening and experimental work should be followed when developing new phytochemical extraction processes. Furthermore, soy by-products emerged as competitive candidates for a long-term source of the bioactive apigenin in a biorefinery context.

Green solvent-synthesized polyimide membranes for gas separation: Coupling Hansen solubility parameters and synthesis optimization

In quest of sustainable development, research efforts have been focused on replacing conventional toxic solvents with green solvents to fabricate greener membranes, illuminating the significant role of green chemistry and engineering to promote a circular economy. Nevertheless, the use of greensolvents in membrane technology is still in the early stages since its practicality remains elusive. This work demonstrates the preliminary screening of green solvents using Hansen solubility parameters (HSP), followed by an optimization of the synthesis conditions of polyimide using the selected green solvent. Based on HSP evaluation, TamiSolve® NxG is identified as a potential green solvent for polyimide synthesis. Two synthesis approaches such as chemical imidization and one-step high temperature polymerization are examined by varying monomer concentrations, polymerization temperatures, imidization time, and imidization temperatures. The polyimide membranes developed from the optimized synthesis conditions ameliorate the gas separation performance with more than 30 % increment in O2 and CO2 permeabilities without compromising their O2/N2, CO2/N2, and CO2/CH4 selectivities. Additionally, the membranes prepared from green solvent-synthesized polyimide exhibit a remarkable enhancement in CO2 plasticization pressure up to 25 bar which outperform that of conventional membranes within 5–15 bar.

New green biorefineries from cyanobacterial-microalgal consortia: Production of chlorophyll-rich extracts for the cosmetic industry and sustainable biogas

Microalgae are promising and sustainable candidates for developing biorefineries to obtain valuable bioproducts and bioenergy. However, key challenges, such as low biomass production, high-cost harvesting, and non-efficient extractions, are restricting its large-scale production. Symbiotic relationships between microalgae and cyanobacteria can simultaneously mitigate these technical and economic restrictions. This research aims to develop sustainable and cost-effective biorefineries from two microalgae-cyanobacteria consortia to produce valuable ingredients for the cosmetic field (chlorophylls) and bioenergy (biogas). Solvent screening and cell disruption experiments were carried out to optimise the chlorophyll extraction protocols. Green solvents were chosen for both consortia. The mildness method (vortexing) was enough to achieve the maximum extraction level of chlorophylls (4.8 ± 0.2 mg/g) using 96% ethanol from a consortium composed of Chlorella vulgaris, Tetraselmis sp., and Kamptonema sp. The use of bead-beating was even more effective using water in the case of the consortium of C. vulgaris and Arthrospira platensis (13.5 ± 1.1 mg/g). High-potential antioxidant chlorophyll extracts were obtained for the cosmetic sector. As ethanol traces were found in the residual biomass of the C. vulgaris, Tetraselmis sp., and Kamptonema sp. consortium, the highest cumulative biomethane production (472 ± 32 mLCH4/gVS) was achieved with this residual biomass, the corresponding value for the initial consortium being significantly lower (239 ± 32 mLCH4/gVS). The study concludes through the overall mass balances of the best biorefineries that it is possible to recover up to 100% of both consortia weight as cosmetic ingredients, biogas, and fertilisers or cultivation media.

Insights into deep learning framework for molecular property prediction based on different tokenization algorithms

With the rapid development of deep learning, research on quantitative structure–property relationships based on deep learning has received widespread attention. The deep learning architecture combining Bidirectional Encoder Representation from Transformers (BERT) and Feedforward Neural Networks (FNN) is proposed to compare the performance of different tokenization algorithms. And t-distributed stochastic neighbor embedding reveals valuable information about the mechanism of structure–property relationships. Additionally, a deep learning framework, BERT-Convolutional Neural Network (CNN)-FNN, is developed based on the optimal tokenization algorithm to accurately predict the σ-profile and VCOSMO. The molecular structures are vectorized with the BERT model capturing local and global features of the entire molecule. And the CNN model enhances the latent representation associated with molecular properties, while the FNN model establishes the correlation. The deep learning frameworks predict σ-profile and VCOSMO properties with R2 greater than 0.9703, making it a promising intelligent tool for guiding solvent design and screening.

Measurement of the solubility and density of the saturated solution of 3-aminophenol in different pure solvents from 283.1 to 323.1 K: Correlative to pure predictive thermodynamic modeling and molecular dynamic simulation

The solubility of 3-aminophenol was measured in water, isopropanol, ethyl acetate, chloroform, and tert-butyl methyl ether (MTBE) from 283.1 to 323.1 K at atmospheric pressure. In addition, the density of pure solvents and the saturated 3-aminophenol solutions was measured. The experimental solubility data were correlated and predicted using four types of thermodynamic models, including correlative (Wilson, NRTL, and UNIQUAC), semi-predictive (NRTL-SAC, and UNIQAUC-SAC), predictive group contribution methods (UNIFAC, and UNIFAC-DMD), and pure predictive COSMO-SAC model. The NRTL-SAC and UNIQUAC-SAC as semi-predictive models were used to obtain 3-aminophenol conceptual segment numbers, and then, using the NRTL-SAC model, the solubility of 3-aminophenol was predicted in 58 FDA approved solvents in the pharmaceutical industry. The predictive models including UNIFAC and UNIFAC-DMD were also used to predict the 3-aminophenol solubility and compared with the experimental data. The results of the COSMO-SAC model have a large deviation from experimental data. In addition, the experimental solubility data were correlated using the PR equation of state with relative success. The segment-based models such as NRTL-SAC and UNIQUAC-SAC are suggested in the fast solvent screening procedure. Finally, the standard dissolution enthalpy (ΔdisH), the standard dissolution entropy (ΔdisS), and the standard dissolution Gibbs energy (ΔdisG) of 3-aminophenol in the selected solvents were calculated by the Van’t Hoff model and the Gibbs-Helmholtz equation. These values imply that the solubility of 3-aminophenol is an entropy-driven non-spontaneous endothermic process in all chosen single solvents. Using the molecular dynamic (MD) simulation the solvation free energy of 3-aminophenol was calculated. The order of the absolute value of free energy of solvation is as ethyl acetate > isopropanol > MTBE > water > chloroform which is consistent with the order of solubility data.

Solubility prediction and intermolecular interaction energies of four explosives in the studied solvents at different temperatures

ABSTRACT Solubility prediction and intermolecular interaction of four explosives (1,3,5-Trinitroperhydro-1,3,5-triazine,ε‑2,4,6,8,10,12-Hexanitro-2,4,6,8,10,12-hexaazaiso-wurtzitane, 3-Nitro-1,2,4-triazolin-5-one，3,4-dinitro-1 H-pyrazole) are researched by the Conductor-like Screening Model – Realistic solvents (COSMO-RS). The results show that the study of σ-surface and σ-profile can qualitatively analyze the potential mechanism of the dissolution behavior of four explosives in the studied solvents. COSMO-RS can accurately predict the solubility of four explosives in the studied solvents, but some of the predicted solubility is different from the experimental solubility, and the deviation between the predicted solubility and the experimental solubility was analyzed. The molecular interaction energy between explosive molecules and solvent partially reveals the internal mechanism of the dissolution of explosives, however, the solvation of the four explosives is a complex process that requires the synthesis of various molecular interactions. This has a great effect on the study of solvation of explosives, and it is of great significance for solvent screening of other explosives molecules.

Investigation of solvent dewaxing of straight-run diesel: Combination of quantum chemical calculations and experimental condition optimization

Separation of the paraffin contained in straight-run diesel is important to alleviate the surplus and achieve high value utilization of diesel. The differences in the dewaxing effect of straight-run diesel under different solvent systems were investigated using ethanol-butanone (EA-MEK), acetone-butanone (AC-MEK) and ethyl formate (EF) as dewaxing solvents. The differences can be able to explain by the different trends in the magnitude of the forces between the solvent and model components calculated from quantum chemical calculations. The relationship between the solvent-model compound interaction and the solvent dewaxing effect was obtained by combining the results of analytical experiments and quantum chemical calculations. It was also found that the main force between the solvent and the diesel model components is van der Waals (VDW). The stronger the interaction between solvent and paraffin, the higher the dewaxing ratio. AC-MEK exhibits the highest dewaxing ratio of 85.8% owing to the strongest solvent-paraffin interaction. Theoretical guidance is provided for the in-depth understanding of the dewaxing process and the subsequent rapid screening of efficient dewaxing solvents. In addition, the process conditions of solvent dewaxing were studied and optimized. Under optimized conditions, EA-MEK could reach a dewaxing ratio of 73.0% with a wax purity of 86.0 wt%. The condition of dewaxing temperature was also studied in detail and the relationship between the dewaxing temperature and the carbon number of paraffin in the dewaxed oil was obtained.

Development of long-acting injectable suspensions by continuous antisolvent crystallization: An integrated bottom-up process

Our present work elucidated the operational feasibility of direct generation and stabilization of long-acting injectable (LAI) suspensions of a practically insoluble drug, itraconazole (ITZ), by combining continuous liquid antisolvent crystallization with downstream processing (i.e., centrifugal filtration and reconstitution). A novel microchannel reactor-based bottom-up crystallization setup was assembled and optimized for the continuous production of micro-suspension. Based upon the solvent screening and solubility study, N-methyl pyrrolidone (NMP) was selected as the optimal solvent and an impinging jet Y-shaped microchannel reactor (MCR) was selected as the fluidic device to provide a reproducible homogenous mixing environment. Operating parameters such as solvent to antisolvent ratio (S/AS), total jet liquid flow rates (TFRs), ITZ feed solution concentration and the maturation time in spiral tubing were tailored to 1:9 v/v, 50 mL/min, 10 g/100 g solution, and 96 h, respectively. Vitamin E TPGS (0.5% w/w) was found to be the most suitable excipient to stabilize ITZ particles amongst 14 commonly used stabilizers screened. The effect of scaling up from 25 mL to 15 L was evaluated effectively with in situ monitoring of particle size distribution (PSD) and solid-state form. Thereafter, the suspension was subjected to centrifugal filtration to remove excess solvent and increase ITZ solid fraction. As an alternative, an even more concentrated wet pellet was reconstituted with an aqueous solution of 0.5% w/w Vitamin E TPGS as resuspending agent. The ITZ LAI suspension (of 300 mg/mL solid concentration) has the optimal PSD with a D10 of 1.1 ± 0.3 µm, a D50 of 3.53 ± 0.4 µm and a D90 of 6.5 ± 0.8 µm, corroborated by scanning electron microscopy (SEM), as remained stable after 548 days of storage at 25 °C. Finally, in vitro release methods using Dialyzer, dialysis membrane sac were investigated for evaluation of dissolution of ITZ LAI suspensions. The framework presented in this manuscript provides a useful guidance for development of LAI suspensions by an integrated bottom-up approach using ITZ as model API.

Screening of Natural Deep Eutectic Solvents for the Recovery of Valuable Phenolics From Waste of Shalgam Juice Process

Shalgam juice is one of the most popular non-alcoholic beverages in Türkiye and also in many countries. The high amount of production, regarding the high amount of consumption leads to an inevitable amount of solid waste. This amount reaches to almost 12 million kg of fermented black carrot annually. The accumulation of this waste causes fatal environmental pollution. The waste of shalgam juice process, fermented black carrot, retains significant amount of valuable components, such as phenolics, flavonoids, and anthocyanins. In this study new generation green solvents, Natural Deep Eutectic Solvents (NADESs) were screened for the extraction of valuable phenolics from fermented black carrot using ultrasound assisted extraction. The NADESs used were selected from four different groups such as acidic, sugar-based, choline chloride-sugar based and polyol-based. According to the results, the members of polyol and choline chloride sugar NADESs showed up. Choline chloride-glycerol (polyol group), extracted the highest total phenol amount (16.04 mg/g) and also provided the highest antioxidant activity (81.77%). On the other hand, NADESs belonging to choline chloride-sugar group were effective for the extraction of flavonoids and monomeric anthocyanins. Namely, choline chloride-fructose-water extract contained 21.45 mg/g of total flavonoids, while choline chloride-sucrose-water extract contained 1680.51 mg/kg of total monomeric anthocyanins. The performances of NADESs tested were found to be higher than that of water and ethanol showing the high yield recovery of valuable phenolics with NADESs. The results exhibited the significance of the components inside the waste. The remaining valuable content could be easily and efficiently extracted using NADESs and these extracts –as a mixture or after purification- can furtherly be used for different purposes in different fields, such as cosmetics, antioxidant preparations, etc, using a zero-cost waste as the input of the processes.

Antisolvent Effects of C1–C4 Primary Alcohols on Solid-Liquid Equilibria of Potassium Dihydrogen Phosphate in Aqueous Solutions

The focus of this study was to examine antisolvent effects, which hold significance in particulate processes, such as crystallization and precipitation. In the first section, an experimental investigation revealed that C1–C4 primary alcohols significantly reduced the solubility of potassium dihydrogen phosphate (KDP) in water. The solid–liquid equilibria of KDP solutions were determined using an innovative polythermal method, demonstrating time and labor efficiency compared to the traditional isothermal method while maintaining solubility determination quality. This achievement established an efficient tool for high-throughput solvent screening, a crucial aspect of particulate process development. In addition to the experimental approach, in the second part, the influence of these alcohols on KDP solubility was analyzed using the eNRTL thermodynamics model. The model’s estimated parameters confirmed that the addition of these alcohols induced strong non-ideal behavior in the solutions, altered interactions between solute species and solvent components, and reduced KDP solubility. Under the effects of these alcohols, KDP solubility generally increased with the length of the alkyl chain in the added alcohols, although methanol deviated from this observation. Furthermore, the present work also discussed the limitation of the well-known Bromley’s equation, particularly when applied for KDP in alcohol–water mixed solvents. Consequently, binary and ternary systems consisting of KDP, water, and C1–C4 primary alcohols were successfully modeled using eNRTL. Furthermore, it was determined that the obtained model was insufficient for quaternary systems with a higher alcohol content, particularly when high-order interactions were neglected as in the cases of binary and ternary systems. In short, these investigated alcohols have potential for future applications in the design of particulate processes, with a particular emphasis on antisolvent crystallization.

Molecular-based artificial neural networks for selecting deep eutectic solvents for the removal of contaminants from aqueous media

Computational methods for predicting a solvent’s performance in a given application, and for selecting an adequate solvent for that application, are becoming increasingly essential for separation processes. In this context, this article presents a first-of-a-kind machine learning tool based on guided molecular design of solvents to predict the performance of various solvent systems in the solvent extraction process. The tool was demonstrated herein for the extraction of aqueous boron, through the selection of neoteric solvents from a dataset that spans different types of solvents (molecular solvents, deep eutectic solvents (DESs) and ionic liquids). The model was developed by first obtaining the COSMO-RS-based molecular descriptors (σ-profiles) for each solvent system and using them as input parameters to an Artificial Neural Network (ANN), in addition to other operational parameters (e.g., pH, temperature, ion concentration, and A/O ratio), while extraction efficiency as the output. The results showed that the optimal ANN configuration (59–20-15–1) exhibited remarkable predictability for boron extraction with an R2 of 0.988 and 0.977 for the training and testing sets, respectively. The model was used to investigate different solvent systems of which six new DESs were successfully synthesized, experimentally tested, and characterized across different properties such as density, viscosity, and leachability. The combination of Decanol and 2,2,4- trimethyl-1,3-pentanediol exhibited appreciable properties and high experimental extraction efficiency of 97.22%. The experimentally validated model demonstrates the effectiveness of molecular-based descriptors and machine learning for predicting the extraction capabilities of solvents in aqueous media and allows further exploration of new solvent systems based on their extraction performance at different operational conditions. This proof-of-concept approach can be effectively adopted to predict the extraction behavior of different solvent systems towards target contaminants in aqueous environments, thereby supporting both the design of separation processes and solvent screening for future applications.

Screening of ionic liquids and deep eutectic solvents for the extraction of persistent organic pollutants from edible oils and fat

Presence of persistent organic pollutants (POPs) like polychlorinated-p-dibenzodioxins (PCDDs), polychlorinated dibenzofurans (PCDFs) and polychlorinated biphenyls (PCBs) in edible oils poses risk to human health. Ionic liquids (ILs) and deep eutectic solvents (DESs) are novel combinatorial solvents with a proven record of extraction of such persistent organic pollutants (POPs). However, the large possibility of such combinatorial solvents necessitates an a-priori screening strategy to select the best possible ILs and DESs. Therefore, a Conductor like Screening Model Segment Activity Coefficient (COSMO-SAC) based framework has been employed to extract 27 POPs (6 PCDDs, 9 PCDFs, 12 PCBs) from five types of edible oil and animal fat by ILs and DESs. The screening will create such a list and based on that experimentation can be performed. There are five different types of oils and fats such as sunflower oil, soya lecithin, rapeseed oil, fish oil, and milk fat that are selected as potential sources of PCDD/Fs and PCBs. A combination of 58 cations and 39 anions which will yield 2262 types of IL and 217 types of DESs with the proportional combination of 24 HBAs and 62 HBDs are considered. Infinite dilution activity coefficient (IDAC) and solubility are calculated based on COSMO-SAC modelling and a set of ILs and DESs are screened as a potential extractant for POPs. The study aims to select the most suitable green solvents to extract these POPs from edible oils to render them safe for consumption.

The a priori screening of potential organic solvents using artificial neural networks

A QSPR model using artificial neural networks was constructed to estimate the binary interaction parameters for the temperature-dependant form of the NRTL model with the objective of using it as a supplement to assist limitations of group contribution methods in the screening of potential solvents for liquid-liquid extraction processes. Parameters were regressed using experimental LLE and VLE data and checked for consistency. Molecule structures were drawn and descriptors determined with the use of Materials Studio. The QSPR model uses 31 descriptors as input and produced absolute average deviations of 0.23 and 0.19 for each pair of binary interaction parameters respectively. The development of this model is shown to be effective in improving the robustness of solvent screening processes.

Improving the accuracy of the FMO binding affinity prediction of ligand-receptor complexes containing metals.

Polarization and charge transfer strongly characterize the ligand-receptor interaction when metal atoms are present, as for the Au(I)-biscarbene/DNA G-quadruplex complexes. In a previous work (J Comput Aided Mol Des2022, 36, 851-866) we used the ab initio FMO2 method at the RI-MP2/6-31G* level of theory with the PCM [1] solvation approach to calculate the binding energy (ΔEFMO) of two Au(I)-biscarbene derivatives, [Au(9-methylcaffein-8-ylidene)2]+ and [Au(1,3-dimethylbenzimidazole-2-ylidene)2]+, able to interact with DNA G-quadruplex motif. We found that ΔEFMO and ligand-receptor pair interaction energies (EINT) show very large negative values making the direct comparison with experimental data difficult and related this issue to the overestimation of the embedded charge transfer energy between fragments containing metal atoms. In this work, to improve the accuracy of the FMO method for predicting the binding affinity of metal-based ligands interacting with DNA G-quadruplex (Gq), we assess the effect of the following computational features: (i) the electron correlation, considering the Hartree-Fock (HF) and a post-HF method, namely RI-MP2; (ii) the two (FMO2) and three-body (FMO3) approaches; (iii) the basis set size (polarization functions and double-ζ vs. triple-ζ) and (iv) the embedding electrostatic potential (ESP). Moreover, the partial screening method was systematically adopted to simulate the solvent screening effect for each calculation. We found that the use of the ESP computed using the screened point charges for all atoms (ESP-SPTC) has a critical impact on the accuracy of both ΔEFMO and EINT, eliminating the overestimation of charge transfer energy and leading to energy values with magnitude comparable with typical experimental binding energies. With this computational approach, EINT values describe the binding efficiency of metal-based binders to DNA Gq more accurately than ΔEFMO. Therefore, to study the binding process of metal containing systems with the FMO method, the adoption of partial screening solvent method combined with ESP-SPCT should be considered. This computational protocol is suggested for FMO calculations on biological systems containing metals, especially when the adoption of the default ESP treatment leads to questionable results.

Direct Decarboxylative C-2 Alkylation of Azoles through Minisci-Type Coupling.

This note discusses the application of a Minisci-type reaction for the direct alkylation of azoles with carboxylic acids as radical precursors. Different reaction conditions were investigated to achieve high yield of the desired products, focusing on acid strength and solvent screening. Moreover, the reactivity of imidazoles with various carboxylic acids was investigated, showing good yield for most cases. The study reveals the potential of this approach for late-stage functionalization in drug discovery.

Production of (hydroxy)benzoate-derived polyketides by engineered Pseudomonas with in situ extraction

Polyketides from (hydroxy)benzoates are an interesting group of plant polyphenolic compounds, whose biotechnological production is so far underrepresented due to their challenging heterologous biosynthesis. Efficient heterologous production of 2,4,6-tri- and 2,3′,4,6-tetrahydroxybenzophenone, 3,5-dihydroxybiphenyl, and 4-hydroxycoumarin by whole-cell biocatalysis in combination with in situ product extraction with an organic solvent was demonstrated. Production was highly dependent on the used CoA ligase and polyketide synthase type III. Therefore, different combinations of polyketide synthases and benzoate-CoA ligases were evaluated for their biosynthesis performance in the solvent-tolerant Pseudomonas taiwanensis VLB120. A solvent screening yielded 2-undecanone as biocompatible, extraction-efficient solvent with good phase separation. In aqueous-organic two-phase cultivations, this solvent extraction circumvents product instability in the aqueous cultivation medium, and it increases yields by reducing inhibitory effects. Complete de novo synthesis from glucose of all (hydroxy)benzoate-derived polyketides was achieved in two-phase cultivations with metabolically engineered strains. Additionally, mutasynthesis was applied to obtain fluorinated benzophenone derivatives.

Correcting charge distribution in reduced Li-molecule pair for computational screening of battery solvents.

Li-molecule pair is a widely used model for the simulation of reduction in Li-ion batteries. We demonstrate that this model provides incorrect results for some solvents. When an electron is added to the Li-molecule pair, it may go to the lithium-ion and neutralize it. Instead, we suggest placing this additional electron on the molecule using constrained density functional theory (CDFT). This approach resembles electron behaviour in the condensed phase and reproduces the physics of the reduction. We demonstrate that suggested in this work approach provides improved agreement with experimental data. Suggested CDFT-based method is fast, reliable and may be used in computational screening of solvents. We demonstrate the practical application of the method by benchmarking it on a set of 30 molecules from the electrolyte solvent database.

Study of Methanol Extraction from Saturated Alkanes: Solvent Screening, Liquid–Liquid Equilibrium Measurement, and Thermodynamic Simulation

Study of Methanol Extraction from Saturated Alkanes: Solvent Screening, Liquid–Liquid Equilibrium Measurement, and Thermodynamic Simulation