Deep eutectic solvents (DESs) have been widely used to capture CO2 in recent years. Understanding CO2 mechanisms by DESs is crucial to the design of efficient DESs for carbon capture. In this work, we studied the CO2 absorption mechanism by DESs based on ethylene glycol (EG) and protic ionic liquid ([MEAH][Im]), formed by monoethanolamine (MEA) with imidazole (Im). The interactions between CO2 and DESs [MEAH][Im]-EG (1:3) are investigated thoroughly by applying 1H and 13 C nuclear magnetic resonance (NMR), 2-D NMR, and Fourier-transform infrared (FTIR) techniques. Surprisingly, the results indicate that CO2 not only binds to the amine group of MEA but also reacts with the deprotonated EG, yielding carbamate and carbonate species, respectively. The reaction mechanism between CO2 and DESs is proposed, which includes two pathways. One pathway is the deprotonation of the [MEAH]+ cation by the [Im]− anion, resulting in the formation of neutral molecule MEA, which then reacts with CO2 to form a carbamate species. In the other pathway, EG is deprotonated by the [Im]−, and then the deprotonated EG, HO-CH2-CH2-O−, binds with CO2 to form a carbonate species. The absorption mechanism found by this work is different from those of other DESs formed by protic ionic liquids and EG, and we believe the new insights into the interactions between CO2 and DESs will be beneficial to the design and applications of DESs for carbon capture in the future.

The traditional marketing model can no longer meet the needs of users and can not add more benefits to the enterprise, and digital marketing came into being. At present, most of the marketing focus of various enterprises is still mainly on products, and the reflection arc to market changes is long. Therefore, the formulation of marketing activities should always pay attention to changes in user needs and combine corresponding activity planning, product planning, brand building, etc., according to Changes in the target market adjust the content of marketing activities and products in real-time and, at the same time, pay attention to user feedback on products in order to iteratively update products in time, improve product market competitiveness, and optimize the user experience. In this paper, through the study and research of the traditional random forest method and some data processing algorithms, the feature selection and class imbalance problems of random forest are improved, respectively. Through the study of feature selection methods, we can maintain a balance between feature strength and relevance during feature selection and improve the final model classification effect. And through the research and experiment of the imbalanced data classification problem and the random forest algorithm, the method of the random forest model to deal with the imbalanced problem has been improved. After experimental calculation and analysis, it is found that for the effect of the minimum number of samples required for node splitting with different numbers, the best results are obtained when 2 samples are taken as the minimum number of samples required for node splitting, and the average value of the F1 evaluation is 0.1038; for different specifications, the effect of the random forest is the best using the Gini index, and the average value of its F1 evaluation is 0.1033; for the effect analysis of random forests with different numbers of trees, 7 to 10 decision trees are the best, and the F1 evaluation is the best. The average is 0.10175.

The reviews of customers in E-Commerce Systems are typically considered key resources, are reflecting the experiences, sentiments, and readiness of the customer to buy products. All such data may include perspectives of customers on matters of interest, feelings, and opinions. Various studies have demonstrated that individuals with comparable attitudes about the topics are more inclined to trust one another. This study explores searching for and adopting e-commerce services with sentimentsand recommendations that involve some customer trust.A trust-based model for the E-commerce application (TM-ECA) model is proposed in this article. From that perspective, an e-commerce system examines a strategy for examining sentimental similarities based on mining to examine consumers' similarities and confidence. Trust is divided into two classifications: direct trust and common trust that is a connection of trust among two people. The direct level of trust is acquired through feeling resemblance, and a pair of words for retrieving comparable characteristics isavailable.The transitivity characteristic determines the spread of trust. The smallest path is selected to express confidence and offer an enhanced smallest path method to identify the link between consumers' confidence in transmission using the suggested confidence model. To check the correctness and practicality of methods and the designs, a website for e-commerce evaluates the method. The testing results show that the sentimental evaluation of resemblance can be an effective way of finding confidence among e-commerce system customers.