Yantai University

About: Yantai University is a education organization based out in Yantai, China. It is known for research contribution in the topics: Catalysis & Nonlinear system. The organization has 7156 authors who have published 6815 publications receiving 81137 citations.

Molecular imprinting: perspectives and applications

Abstract: Molecular imprinting technology (MIT), often described as a method of making a molecular lock to match a molecular key, is a technique for the creation of molecularly imprinted polymers (MIPs) with tailor-made binding sites complementary to the template molecules in shape, size and functional groups. Owing to their unique features of structure predictability, recognition specificity and application universality, MIPs have found a wide range of applications in various fields. Herein, we propose to comprehensively review the recent advances in molecular imprinting including versatile perspectives and applications, concerning novel preparation technologies and strategies of MIT, and highlight the applications of MIPs. The fundamentals of MIPs involving essential elements, preparation procedures and characterization methods are briefly outlined. Smart MIT for MIPs is especially highlighted including ingenious MIT (surface imprinting, nanoimprinting, etc.), special strategies of MIT (dummy imprinting, segment imprinting, etc.) and stimuli-responsive MIT (single/dual/multi-responsive technology). By virtue of smart MIT, new formatted MIPs gain popularity for versatile applications, including sample pretreatment/chromatographic separation (solid phase extraction, monolithic column chromatography, etc.) and chemical/biological sensing (electrochemical sensing, fluorescence sensing, etc.). Finally, we propose the remaining challenges and future perspectives to accelerate the development of MIT, and to utilize it for further developing versatile MIPs with a wide range of applications (650 references).

Adsorption of antibiotics on microplastics.

Abstract: Microplastics and antibiotics are two classes of emerging contaminants with proposed negative impacts to aqueous ecosystems. Adsorption of antibiotics on microplastics may result in their long-range transport and may cause compound combination effects. In this study, we investigated the adsorption of 5 antibiotics [sulfadiazine (SDZ), amoxicillin (AMX), tetracycline (TC), ciprofloxacin (CIP), and trimethoprim (TMP)] on 5 types of microplastics [polyethylene (PE), polystyrene (PS), polypropylene (PP), polyamide (PA), and polyvinyl chloride (PVC)] in the freshwater and seawater systems. Scanning Electron Microscope (SEM) and X-ray diffractometer (XRD) analysis revealed that microplastics have different surface characterizes and various degrees of crystalline. Adsorption isotherms demonstrated that PA had the strongest adsorption capacity for antibiotics with distribution coefficient (Kd) values ranged from 7.36 ± 0.257 to 756 ± 48.0 L kg−1 in the freshwater system, which can be attributed to its porous structure and hydrogen bonding. Relatively low adsorption capacity was observed on other four microplastics. The adsorption amounts of 5 antibiotics on PS, PE, PP, and PVC decreased in the order of CIP > AMX > TMP > SDZ > TC with Kf correlated positively with octanol-water partition coefficients (Log Kow). Comparing to freshwater system, adsorption capacity in seawater decreased significantly and no adsorption was observed for CIP and AMX. Our results indicated that commonly observed polyamide particles can serve as a carrier of antibiotics in the aquatic environment.

EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks

Abstract: In this paper, a multichannel EEG emotion recognition method based on a novel dynamical graph convolutional neural networks (DGCNN) is proposed. The basic idea of the proposed EEG emotion recognition method is to use a graph to model the multichannel EEG features and then perform EEG emotion classification based on this model. Different from the traditional graph convolutional neural networks (GCNN) methods, the proposed DGCNN method can dynamically learn the intrinsic relationship between different electroencephalogram (EEG) channels, represented by an adjacency matrix, via training a neural network so as to benefit for more discriminative EEG feature extraction. Then, the learned adjacency matrix is used to learn more discriminative features for improving the EEG emotion recognition. We conduct extensive experiments on the SJTU emotion EEG dataset (SEED) and DREAMER dataset. The experimental results demonstrate that the proposed method achieves better recognition performance than the state-of-the-art methods, in which the average recognition accuracy of 90.4 percent is achieved for subject dependent experiment while 79.95 percent for subject independent cross-validation one on the SEED database, and the average accuracies of 86.23, 84.54 and 85.02 percent are respectively obtained for valence, arousal and dominance classifications on the DREAMER database.

Preparation and Characterization of Graphene Oxide

Abstract: Graphene oxide (GO) films with two-dimensional structure were successfully prepared via the modified Hummer method. It is proven that redox method is a promising way to synthesize GO films on a large scale. Comprehensive characterizations of the properties of GO films were conducted. TEM and DFM analyses showed that GO sheets prepared in this study had single and double lamellar layer structure and a thickness of 2~3 nm. X-ray diffraction (XRD) was selected to measure the crystal structure of GO sheet. Fourier-transform infrared spectra analyzer (FT-IR) was used to certify the presence of oxygen-containing functional groups in GO films. The tests of UV-VIS spectrometer and TGA analyzer indicated that GO sheet possessed excellent optical response and outstanding thermal stability. Elemental analyzer (EA) and X-ray photoelectron spectroscope (XPS) analyzed the components synthetic material. Simultaneously, chemical structure of GO sheet was described in this study. Discussion and references for further research on graphene are provided.