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首页> 外文期刊>International Journal of Biological Macromolecules: Structure, Function and Interactions >Modeling and optimization of ultrasound-assisted high performance adsorption of Basic Fuchsin by starch-capped zinc selenide nanoparticles/AC as a novel composite using response surface methodology
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Modeling and optimization of ultrasound-assisted high performance adsorption of Basic Fuchsin by starch-capped zinc selenide nanoparticles/AC as a novel composite using response surface methodology

机译:淀粉升锌硒纳米粒子/ AC作为基础葡萄锌碱型氟辛碱的超声辅助高性能吸附的建模与优化,用作响应面法作为新型复合材料

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摘要

In this study, the starch-capped zinc selenide nanoparticles loaded on activated carbon (ST-Zn-Se-NPs-AC) composite was fabricated, and then it was used for removing Basic Fuchsin (BF) dye from aqueous solution. The STZn-Se-NPs-AC composite was characterized by FE-SEM, UV-Vis, EDS, and XRD techniques. The removal percentage dependence to different variables such as initial BF concentration, pH, dosage of adsorbent. and time of sonication was investigated by Central Composite Design (CCD) under Response Surface Methodology (RSM). The quadratic model between the independent and dependent variables was predicted. A good agreement between the experimental and predicted data was achieved by the predicted model that showed the performance of the predicted model for predicting of real optimum points, and it was successfully employed to remove BF from aqueous media. The maximum removal percentage of 99.34% was obtained by the predicted model under the optimum conditions (15 mg L-1 of initial BF concentration, pH of 7.0, 12 mg of ST-Zn-Se-NPs-AC, and 6 min of sonication time), which was very dose to the experimental value (99.00%). Moreover, the data were efficiently fitted by Langmuir model, and the saturation adsorption capacity (Q(max)) at 25 degrees C for BF was discovered to be 222.72 mg g(-1). (C) 2020 Published by Elsevier B.V.
机译:在这项研究中,在活性炭上加载的淀粉 - 封端的硒化锌纳米颗粒(ST-Zn基硒的NP-AC)复合材料制成,然后将其用于去除碱性品红(BF)从水溶液中染色。 STZN-SE-NPS-AC复合材料的特征在于FE-SEM,UV-Vis,EDS和XRD技术。去除百分比对不同变量的依赖性,例如初始BF浓度,pH,吸附剂剂量。通过中央复合设计(CCD)在响应面方法(RSM)下调查了超声处理的时间。预测了独立和依赖变量之间的二次模型。通过预测模型实现实验和预测数据之间的良好一致性,该预测模型显示了预测模型以预测真实最佳点的性能,并且成功地用于从含水介质中除去BF。通过预测模型在最佳条件下(初始BF浓度为15mg L-1,pH为7.0,12mg的ST-ZN-SE-NPS-AC,以及6分钟的超声处理,最大去除百分比为99.34%时间),对实验值(99.00%)非常剂量。此外,通过Langmuir模型有效地装配数据,并且BF的25摄氏度下的饱和吸附容量(Q(最大))为222.72mg(-1)。 (c)2020由elsevier b.v发布。

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