The lack of accurate methods for predicting the viscosity of solvent materials, especially those with complex interactions, remains unresolved. Deep eutectic solvents (DESs), an emerging class of green solvents, have a severe lack of viscosity data, resulting in their application still staying at the stage of random trial and error, and it is difficult for them to be implemented on an industrial scale. In this work, we demonstrate the successful prediction of the viscosity of DESs based on the transition state theory-inspired neural network (TSTiNet). The TSTiNet adopts multilayer perceptron (MLP) for the transition state theory-inspired equation (TSTiEq) parameters calculation and verification using the most comprehensive DESs viscosity data set to date. For the energy parameters of the TSTiEq, the constant assumption and the fast iteration with the help of MLP can allow TSTiNet to achieve the best performance (the average absolute relative deviation on the test set of 6.84% and R 2 of 0.9805). Compared with the traditional machine learning methods, the TSTiNet has better generalization ability and dramatically reduces the maximum relative deviation of prediction under the constraints of the thermodynamic formulation. It requires only the structural information on DESs and is the most accurate and reliable model available for DESs viscosity prediction.

Cross-coupling between difluorocarbene and carbene-derived intermediates generated from diazocompounds was developed to give gem-difluoroolefins, which constitutes a fast practical pathway to achieve hindered gem-difluoroolefins. The cross-coupling between difluorocarbene and aryl diazoacetates proceeded smoothly in the presence of a copper source, whereas its coupling with diaryl diazomethanes occurred well under metal-free conditions. A mechanism involving a copper-difluorocarbene complex was proposed.

Numerous biological activities including antioxidant, antitumor, anti-inflammation, and antivirus of the natural product curcumin were reported. However, the clinical application of it was significantly limited by its instability, poor solubility, less body absorbing, and low bioavailability. This review focuses on the structure modification and antioxidant activity evaluation of curcumin. To study the structure-activity relationship (SAR), five series of curcumin analogs were synthesized and their antioxidant activity were evaluated in vitro. The results showed that electron-donating groups, especially the phenolic hydroxyl group are an essential component to improve the antioxidant activity.

Deep eutectic solvents (DESs) are mixtures of two or more components that have lower melting temperatures compared to their constituting components. DESs possess many advantages, for example, low volatility, low flammability, and low toxicity, which make them promising alternatives to traditional organic solvents. The melting temperature, one of the important physical properties, is of essential importance for industrial applications. In this work, a group and group-interaction contribution method was proposed to estimate the melting temperatures of DESs using an extensive database (1528 DESs, 1541 data points). The average absolute relative deviation (%AARD) between the estimated and experimental values of the melting temperature was 5.67% for binary DESs. Subsequently, this method was also extended to estimate the melting temperature of ternary DESs, with the AARD of 6.13%. The results indicate the high accuracy and broad applicability of the method and pave the way for the rational design of task-specific DESs.

Deep eutectic solvents (DESs), a novel category of sustainable solvents, are expected to achieve the design of the chemical processes without utilizing or generating harmful chemicals. In this work, based on the mathematical model inspired by the transition state theory, the group contribution method is used to accurately predict the viscosity of DESs. The model is constrained by Eyring rate theory and hard sphere free volume theory. A dataset of 2229 experimental viscosity data points of 183 DESs from literature is used to determine the model parameters and subsequently verify the model. The rules introduced by this model are simple and easy to follow. The results show that the proposed model is capable to predict the viscosity of DESs with very high accuracy, using only temperature and composition as inputs. The average absolute relative deviations (AARDs) of the model are 8.12% and 8.64% over the training and test sets, respectively, and the maximum ARD is 34.63%. Therefore, the as-proposed model can be considered a highly reliable tool for predicting the viscosity of DESs when experimental data are absent. It will provide useful guidance for the synthesis of DESs with specific viscosity to meet different application requirements and promote their industrial-scale implementation.

Deep eutectic solvents (DESs) with benign properties as green alternatives are being preferred for a multitude of energy conversion and environmental protection processes and designs. The tunability of the constituents makes the design of novel DESs for specific tasks possible, accompanied by the urgent requirement of accurate characterization and prediction of the properties. Modeling the structure–property relationships of DESs will offer feasible conduction to predict properties and design novel DESs. Particularly, the estimation of density, as a fundamental physical property, is essential for process operation and design. Herein, based on the hydrogen-bond interaction existing in DESs, a general bonding-group interaction contribution (BGIC) method was introduced to predict the densities of DESs by their structures. The optimized parameter values were determined by the training set (2553 data points, 70%) with the average absolute relative deviation (AARD) result of 1.49%. The test set (1094 data points, 30%) was used to evaluate the accuracy of the method with the AARD result of 1.56%. The predictive relative deviation (RD) can be controlled within ±20% except for one abnormal data point. The BGIC method was also extended to estimate densities of ternary DESs, and the resulting AARD was 2.29% for 174 data points. The results illustrate that the BGIC method has provided a valuable and reliable tool for predicting densities of DESs.

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A novel series of apigenin derivatives with phloroglucinol or resorcinol as raw materials were synthesized according to Baker-Venaktaraman reaction and their in vitro inhibitory activities on colorectal adenocarcinoma (HT-29) and leucocythemia (HL-60) cell lines were evaluated by the standard methyl thiazole tetrazolium (MTT) method. The results of biological test showed that some of apigenin derivatives possessed stronger anti-cancer activities than apigenin. Compound 6 showed the strongest activity against colorectal adenocarcinoma (HT-29) and leucocythemia (HL-60) cell lines with IC50 valure of 2.03±0.22 µM, 2.25±0.42 µM, it was better than 5-FU (12.92±0.61 µM, 9.56±0.16 µM), which shows a potential compound for colorectal adenocarcinoma and leucocythemia.