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| 1 | The Optimization of Ultrasonic Extraction Technology of Total Flavonoids in Leaves of Mallotus apelta by Response Surface Analysis Methodology显示文摘[Objectives] Response surface analysis methodology was used to optimize the extraction technology of total flavonoids from Mallotus apelta leaves. [Methods] The total flavonoids were extracted using ultrasonic assisted ethanol extraction,and extraction rate of total flavonoids was taken as evaluation index. On the basis of single-factor experiment,Box-Behnken response surface methodology was employed for selecting the optimum extraction process. [Results]The optimum extraction conditions of total flavonoids were as follows: 75% of ethanol concentration,1∶ 25 of ratio of material to liquid,31 min of ultrasonic time. Under these conditions,the extraction rate of total flavonoids was 1. 115%.[Conclusions] The extraction process obtained by response surface methodology was stable,reasonable,accurate and reliable. It was a feasible method to extract the total flavonoids from M. apelta leaves. | Dengfeng ZOU Ruifen FAN Xiao HUANG Aize XIE | 2018 | Medicinal Plant2018，9，4： | 8 |
| 2 | Extraction Process Optimization of Total Flavonoids from Mallotus apelta Stems by Central Composite Design/Response Surface Method显示文摘[Objectives] To optimize the extraction process of total flavonoids in stems of Mallotus apelta. [Methods]On the basis of singlefactor test,with volume fraction of ethanol,extraction time and ratio of solvent as independent variables,the content of total flavonoids as dependent variables,the completely secondary response surface regression fitting was conducted on the independent and dependent variables,and the Response Surface Method was used to optimize the optimum extraction process of total flavonoids in Mallotus apelta stems and predict the optimum process. [Results] The optimum extraction process of total flavonoids in Mallotus apelta was determined as follows: ethanol concentration of 71. 5%; extraction time of 154. 6 min; solid-liquid ratio of 1∶19. 2; total flavonoids content of 7. 060 mg/g; fitted binomial squared correlation coefficient R^2= 0. 8751.[Conclusions]Composite Design/Response Surface Method could be used in the extraction process optimization of total flavonoids in Mallotus apelta stems,the mathematical model established had high prediction accuracy,the method was simple and operability was good. | Xiao HUANG Dengfeng ZOU Ruifen FAN Shuoying GUO Hua ZHU Aize XIE | 2017 | Medicinal Plant2017，8，5： | 2 |
| 3 | A small organic molecule strategy for remedying oxygen vacancies by bismuth defects in BiOBr nanosheet with excellent photocatalytic CO_(2)reduction显示文摘Defect modulation currently plays a decisive role in addressing the poor photoabsorption,sluggish electron hole separation,and high CO_(2)activation barrier in photocatalytic CO_(2)reduction.However,hunting for a straightforward strategy to balance the concentration of oxygen vacancy and metal cation defect in one photocatalyst is still a great challenge.Herein,a bismuth vacancies BiOBr nanosheets(BiOBr-1)on the exposed[001]facets were constructed via an acetic acid molecule modification strategy,which can repair oxygen defect by bismuth vacancy in low-temperature solid-state chemical method.Benefiting from the formed bismuth defects that can not only broaden light absorption and elevate charge separation efficiency,but also enhance adsorption and activation of CO_(2)molecules,the evolution rates of photocatalytic CO_(2)conversion into CO(71.23μmol·g^(-1)·h^(-1))and CH4(8.90μmol·g^(-1)·h^(-1))attained by BiOBr-1 are superior 7.1 and 11 times to that of plate-like BiOBr.The photocatalytic mechanisms including adsorption concentration and activation process of CO_(2)are further revealed by the in situ diffuse reflectance infrared flourier transform spectra(DRIFTS).This finding of the existence of distinct defects in ultrathin nanosheets undoubtedly leads to new possibilities for photocatalyst design using two-dimensional materials with high solar-driven photocatalytic activity. | Jing Xie Zhenjiang Lu Yue Feng Jianguo Huang Jindou Hu Aize Hao Yali Cao | 2024 | Nano Research2024，17，1： | 0 |
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