Investigation of Structure and Dynamics of Deep Eutectic Solvent Using Infrared Spectroscopy

Deep Eutectic Solvents (DES) are liquid mixtures prepared from solids. As a new class of green solvents, DES not only share many properties with ionic liquids, such as low volatility, conductivity, tailorable constituents, but also have some advantages over ionic liquids, like easier preparation, safe and inexpensive materials, biodegradability, low toxicity, and excellent solubility. Because of those attractive properties, DES has been studied in many scientific and engineering fields. However, compared with the great number of studies of the application, many questions about structure and dynamics of DES are unanswered. To shed light on the mystery of solvent or solvation structure and dynamics, we investigated the DES systems with Fourier Transform Infrared (FTIR) and Two-Dimensional Infrared spectroscopy (2DIR). Vibrational transitions measured by infrared spectroscopy serve as sensitive probes to report the local environment and dynamics. We studied the solvation structure and dynamics of choline chloride-based DESs with thiocyanate probe. The interactions between solute and solvation shell were assigned by use of FTIR and confirmed by Molecular Dynamics (MD) simulation. The vibrational mode of infrared probes allowed detection of the solvation dynamics in choline chloride-based DESs as in-place and inter-solvation shell motions within picosecond timescale. Assignments were confirmed by the analysis of MD simulation. In another project, we studied the effects of the molecular structure of hydrogen bond acceptor on the structure and dynamics of acetamide based DES. Our studies show that symmetry of the HBA cation will affect the contribution of slow molecular motion of the environment. The overall dynamics is rationalized in terms of a microscopic heterogeneous structure of the DESs, where the heterogeneities create domains that slow making and breaking of the hydrogen bond. Heterogeneity was further supported by the use of MD simulation. Lastly, we investigated the interactions and solvent structure of a non-ionic DES composed of N-methylacetamide and lauric acid. The interactions among the components were observed with FTIR and temperature-dependent experiments. Finally, the heterogeneous structure of DES with polar and non-polar domains was proposed, and verified by observed confined dynamics of amide band and probes, which is similar to reported water dynamics in reverse micelles

Gold-Catalyzed Regio- and Stereoselective Acyloxyalkynylations and Cycloadditions of Alkynes

In Chapter 2, multi-substituted alkenes are accessible by a gold-catalyzed acyloxyalkynylation of ynamides with ethynylbenziodoxolones (EBXs) in an atom-economic fashion. The EBX reagents act as bifunctional reactants providing both the nucleophilic carboxylate as well as the alkynyl entity. Overall this cascade involves the formation of an alkynyl Au(III) species, stereoselective C(sp)-C(sp2) bond formation and C-O coupling at the alkynyl position of the ynamides. The introduced method features mild conditions and wide substrate scope. A number of tetrasubstituted amide enol 2-iodobenzoates bearing diverse functionalities were prepared in good to excellent yield. DFT calculations exlain the observed regioselectivity. The synthetic potential of the sequence was further documented by a number of selected follow-up transformations. In Chapter 3, Tetrasubstituted alkenes including the ester and ether group, is of great interest in chemistry and material sciences. A variety of tetrasubstituted enol ether 2-iodobenzoate derivatives were prepared in good yields at room temperature through a gold-catalyzed acyloxyalkynylation of ynol ethers with ethynylbenziodoxolones (EBXs), which act as bifunctional reactants in an atom-economic fashion. Furthermore, the mechanism involves an in situ formed alkynyl Au(III) species and a gram-scale reaction was efficiently conducted. In Chapter 4, an efficient method for the construction of highly functionalized new spirooxindolocarbamates from a gold-catalyzed cycloaddition reaction of terminal alkyne and ynamides with isatin-derived ketimines is described. This protocol features easily accessible starting materials and good functional group compatibility enabling the introduction of various functionalized alkyne groups into cyclic carbamates. Gram-scale synthesis and proposed mechanism are also presented

Comparison of Graph Databases and Relational Databases When Handling Large-Scale Social Data

Over the past few years, with the rapid development of mobile technology, more people use mobile social applications, such as Facebook, Twitter and Weibo, in their daily lives, and there is an increasing amount of social data. Thus, finding a suitable storage approach to store and process the social data, especially for the large-scale social data, should be important for the social network companies. Traditionally, a relational database, which represents data in terms of tables, is widely used in the legacy applications. However, a graph database, which is a kind of NoSQL databases, is in a rapid development to handle the growing amount of unstructured or semi-structured data. The two kinds of storage approaches have their own advantages. For example, a relational database should be a more mature storage approach, and a graph database can handle graph-like data in an easier way. In this research, a comparison of capabilities for storing and processing large-scale social data between relational databases and graph databases is applied. Two kinds of analysis, the quantitative research analysis of storage cost and executing time and the qualitative analysis of five criteria, including maturity, ease of programming, flexibility, security and data visualization, are taken into the comparison to evaluate the performance of relational databases and graph databases when handling large-scale social data. Also, a simple mobile social application is developed for experiments. The comparison is used to figure out which kind of database is more suitable for handling large-scale social data, and it can compare more graph database models with real-world social data sets in the future research

The Riemannian product structures of spacelike hypersurfaces with constant k-th mean curvature in the de Sitter spaces

AbstractIn this paper, we investigate complete spacelike hypersurfaces in the de Sitter space S1n+1(c) with constant k-th mean curvature and two distinct principal curvatures one of which is simple. We obtain some characterizations of the Riemannian product H1(c1)×Sn−1(c2) or Hn−1(c1)×S1(c2) in the de Sitter space S1n+1(c)

Graph Masked Autoencoder for Sequential Recommendation

While some powerful neural network architectures (e.g., Transformer, Graph Neural Networks) have achieved improved performance in sequential recommendation with high-order item dependency modeling, they may suffer from poor representation capability in label scarcity scenarios. To address the issue of insufficient labels, Contrastive Learning (CL) has attracted much attention in recent methods to perform data augmentation through embedding contrasting for self-supervision. However, due to the hand-crafted property of their contrastive view generation strategies, existing CL-enhanced models i) can hardly yield consistent performance on diverse sequential recommendation tasks; ii) may not be immune to user behavior data noise. In light of this, we propose a simple yet effective Graph Masked AutoEncoder-enhanced sequential Recommender system (MAERec) that adaptively and dynamically distills global item transitional information for self-supervised augmentation. It naturally avoids the above issue of heavy reliance on constructing high-quality embedding contrastive views. Instead, an adaptive data reconstruction paradigm is designed to be integrated with the long-range item dependency modeling, for informative augmentation in sequential recommendation. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art baseline models and can learn more accurate representations against data noise and sparsity. Our implemented model code is available at https://github.com/HKUDS/MAERec.Comment: This paper has been published as a full paper at SIGIR 202