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RISS 인기검색어
- 검색키워드
- 저자 : Shuai Teng (검색결과 3 건)
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Separation of arenols from a low-temperature coal tar by liquid-liquid extraction
Hua-Shuai Gao,Zhi-Min Zong,Zheng Yang,Dao-Guang Teng,Xiu-Hua Sun,Li Yan,Xian-Yong Wei,Qing-Jie Guo,Tian-Sheng Zhao,Hong-Cun Bai 한국화학공학회 2020 Korean Journal of Chemical Engineering Vol.37 No.5
Low-temperature coal tar (LTCT) is a sticky liquid mixture produced mainly from coal pyrolysis, which contains various value-added chemicals (VACs). Liquid-liquid extraction is considered as one of the green and effective ways to explore the organic composition and separate the VACs from LTCT. Herein, petroleum ether, methanol, and carbon disulfide were used to extract arenols from a LTCT. As a result, the relative content and absolute content of arenols extracted from the LTCT are ca. 96.3% and 85.9%, respectively. Among them, p-cresol is predominant, accounting for 22.2%. The isolated contents of arenols are up to 84.6%. Moreover, a kilogram-scale operation was carried out under the same conditions, which offers a potential application in industrial production.
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Crack Detection Based on Generative Adversarial Networks and Deep Learning
Gongfa Chen,Shuai Teng,Mansheng Lin,Xiaomei Yang,Xiaoli Sun 대한토목학회 2022 KSCE JOURNAL OF CIVIL ENGINEERING Vol.26 No.4
This paper proposes a novel crack detection method using the three-stages detection model. Deep learning technology has been a focus of attention in the field of crack detection; however, it needs big data to train the corresponding network model. More training samples and the combination of multiple deep learning algorithms help to improve the detection performance. Therefore, this paper employed a generative adversarial network (GAN) model to generate abundant virtual crack images with similar features to real images, these virtual images are used to train the CNN classifier and DeepLab_v3+ respectively, and then the real images are used to evaluate the performance of the three-stages detection method. The results show that the proposed three-stages detection method has excellent detection effect on the crack detection is better than that of the control experiment (the NI_MIoU, NI_Accuracy, NI_F-score and NI_MCC are increased by 22.1% − 55.6%, 5.2% − 9.8%, 37.4% − 40.0% and 6.2% − 11.1%, respectively)). These results demonstrate that the three-stages detection model has made a beneficial contribution to the crack detection.
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Yang Cuiting,Xiangyu Chen,Jinkui Teng,Shuai Chen,Jianmei Yang,Xiaoqing Liu,Junnan He,Jin Zhang,Yan Zhao 한국고분자학회 2024 Macromolecular Research Vol.32 No.1
Supramolecular nanoplatforms with stimuli-responsive behavior feature sensitive performance and effective drug delivery, which are desirable as intelligent drug delivery systems. Generally, tumor cells are characterized by excessive acid production, resulting in a lower pH in the tumor microenvironment (pH < 6.5) than in normal tissues (pH ≈7.4) and providing the possibility for the drug delivery system to exploit this decrease in pH as a trigger for drug release. Here, an acid-sensitive supramolecular nanoplatform (CM-β-CD/FC12 +Br− NPs) with assembly/disassembly properties was designed and constructed, which was exploited to capture, deliver, and release anti-tumor compound CSL. 2D NOESY was utilized to examine the host–guest interaction and the potential mechanism for CM-β-CD/FC12 +Br− NPs loading CSL. CM-β-CD/FC12 +Br− NPs present good blood compatibility. Cytotoxicity assay revealed that CSL-loaded NPs display minimal toxicity against normal cells BEAS-2B and good anticancer ability against five human cancer cell lines, especially for Human hepatoma cell line SMMC-7721. In addition, cell apoptosis and cycle assay further verified that CSL-loaded NPs-induced apoptosis in SMMC- 7721 cells up to ~ 93%, as well as blocking the cells in G0/G1 phase and inhibiting the proliferation of SMMC-7721 cells in a dose-dependent manner. We expect that CM-β-CD/FC12 +Br− NPs will be potential acid-answered drug delivery candidates.
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