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Hydroxide MgSn(OH)6: A Promising New Photocatalyst for Methyl Orange Degradation
Jiajia Tao,Zhaoqi Sun,Miao Zhang,Gang He,Xiaoshuang Chen 대한금속·재료학회 2017 ELECTRONIC MATERIALS LETTERS Vol.13 No.4
Highly crystalline hydroxide MgSn(OH)6 (MHS) polyhedral particleswere synthesized by changing reaction time (10, 15 and 20 h) in ahydrothermal process. The structural and morphological poperties ofobtained samples were characterized by X-ray diffraction (XRD),scanning electron microscopy (SEM), and UV-vis diffuse reflectancespectroscopy (DRS). The photocatalytic activity of the MHS wasfurther evaluated by the degradation of methyl orange (MO) underultraviolet (UV) light illumination. Compared with commercial TiO2(Degussa P25), the MHS prepared for 15 h showed similar degradationefficiency of methyl orange (MO), mainly due to its higher specificsurface area (55 m2g−1) and better optical properties.
Shu Wang,Houpu Yang,Jiajia Guo,Miao Liu,Fuzhong Tong,Yingming Cao,Bo Zhou,Peng Liu,Lin Cheng,Fei Xie,Deqi Yang,Jiaqing Zhang 한국바이오칩학회 2011 BioChip Journal Vol.5 No.1
Neo-adjuvant chemotherapy for breast cancer substantially benefits patients who achieve pathological response. However, clinical or pathological response information can only be obtained a period of time after chemotherapy. The identification of novel bio-markers or the application of new technique that can be used to predict treatment response before che-motherapy would allow therapy to be tailored on an individual patient basis. The purpose of this study is to identify the chemo-sensitivity and chemo-resistance related proteins using antibody microarray profiling, and to develop a multi-protein predictive model for breast cancer. Total protein was extracted from core needle biopsy samples obtained from 15 patients before treatment with neo-adjuvant TA(combination of taxanes and anthracycline) chemotherapy. Protein profiling was analyzed by antibody microarray. 10 pati-ents were used as training set to develop the predictive model using the software PAM(prediction analysis of microarray). Another 5 patients were used as a validation set to test the model. In cross-validation, the mole-cular predictive model showed an accuracy of 90%, in independent validation, the model classified the cases with an accuracy of 80%. In conclusion, the proteomic predictive model has the potential to predict pathological response to neo-adjuvant TA chemotherapy.