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MinHui Wang,김진철 한국공업화학회 2015 Journal of Industrial and Engineering Chemistry Vol.23 No.-
Chitosan–cinnamic acid conjugate (Chi–CA) and Pluronic F127–cinnamic acid conjugate (Plu–CA) were synthesized for the preparation of mixed micelles. The mixtures of Chi–CA and Plu–CA in the molar ratio of 0.25:1, 0.5:1 and 1:1 were well interface-active. The dimerization degree was in the order of Plu–CA > Chi–CA > free CA. Plu–CA/Chi–CA mixed micelles were circular on TEM, and their mean diameter appreciably decreased when the temperature increased, possibly due to the thermally induced hydrophobic interaction of Plu chains. The temperature-sensitivity was lower when the Chi–CA content was higher. Dry Plu–CA micelles and Plu–CA/Chi–CA mixed micelles exhibited the similar endothermic peak.
Shuguo Qu,Chenchen Zhang,Minhui Li,Yan Zhang,Lunbo Chen,Yushuai Yang,Bo Kang,Yiwei Wang,Jihai Duan,Weiwen Wang 한국화학공학회 2019 Korean Journal of Chemical Engineering Vol.36 No.12
Making inexpensive proton exchange membrane with high proton conductivity for the proton exchange membrane fuel cell (PEMFC) is still a challenging problem. Graphene oxide (GO) nanoparticles grafted with (3-aminopropyl) triethoxy silane (APTES) were prepared and then incorporated into sulfonated poly(ether ether ketone) (SPEEK) matrix by solution casting to make the composite proton exchange membrane. The obtained nanoparticles and composite membranes were characterized by XRD, FT-IR, Raman, TGA, SEM, and UTM. GO treated with the silane coupling agent improved the dispersion stability and compatibility of GO in SPEEK, which decreased the agglomeration of GO nanoparticles in the SPEEK membrane. The prepared nanocomposite membranes exhibited better water retention properties and proton conductivity. The proton conductivity of the SPEEK membrane with 2wt% amine functionalized GO (AGO) reached 11.32mS/cm at 120oC, which was 2.45-times higher than that of the pristine SPEEK membrane. The reason was that AGO nanoparticles disperse uniformly in the SPEEK membranes, which provides new channels for proton transfer. The potential application of this composite membrane in the PEMFC was indicated.
Lu Yi,Wu Jiachuan,Hu Minhui,Zhong Qinghua,Er Limian,Shi Huihui,Cheng Weihui,Chen Ke,Liu Yuan,Qiu Bingfeng,Xu Qiancheng,Lai Guangshun,Wang Yufeng,Luo Yuxuan,Mu Jinbao,Zhang Wenjie,Zhi Min,Sun Jiachen 거트앤리버 소화기연관학회협의회 2023 Gut and Liver Vol.17 No.6
Background/Aims: The accuracy of endosonographers in diagnosing gastric subepithelial lesions (SELs) using endoscopic ultrasonography (EUS) is influenced by experience and subjectivity. Artificial intelligence (AI) has achieved remarkable development in this field. This study aimed to develop an AI-based EUS diagnostic model for the diagnosis of SELs, and evaluated its efficacy with external validation. Methods: We developed the EUS-AI model with ResNeSt50 using EUS images from two hospitals to predict the histopathology of the gastric SELs originating from muscularis propria. The diagnostic performance of the model was also validated using EUS images obtained from four other hospitals. Results: A total of 2,057 images from 367 patients (375 SELs) were chosen to build the models, and 914 images from 106 patients (108 SELs) were chosen for external validation. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the model for differentiating gastrointestinal stromal tumors (GISTs) and non-GISTs in the external validation sets by images were 82.01%, 68.22%, 86.77%, 59.86%, and 78.12%, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy in the external validation set by tumors were 83.75%, 71.43%, 89.33%, 60.61%, and 80.56%, respectively. The EUS-AI model showed better performance (especially specificity) than some endosonographers. The model helped improve the sensitivity, specificity, and accuracy of certain endosonographers. Conclusions: We developed an EUS-AI model to classify gastric SELs originating from muscularis propria into GISTs and non-GISTs with good accuracy. The model may help improve the diagnostic performance of endosonographers. Further work is required to develop a multi-modal EUS-AI system.