在适当条件下用于高度还原对硝基苯酚的 BR@Ag 纳米复合材料的合成、表征和应用。

IF 6.5 3区 工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Fatimah Othman Alqahtani, Nazish Parveen, Gausal A Khan, Meerambika Behera, Sankha Chakrabortty, Suraj K Tripathy
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引用次数: 0

摘要

异相催化反应是制造大量当代产品的最基本化学过程之一,也是最吸引人的化学过程之一。金属纳米结构因其巨大的表面积、大量活性表面位点的组合以及量子约束效应,成为一系列反应的异相催化剂。未受保护的金属纳米粒子存在不可逆团聚、催化剂中毒和生命周期有限等问题。为了规避这些技术上的缺点,催化剂通常被分散在化学惰性材料上,如介孔 Al2O3、ZrO2 和不同类型的陶瓷材料。在这项研究中,我们利用丰富的铝矾土残渣制造了一种低成本的替代催化材料。我们在矾土渣(BR)支撑的银纳米复合材料(Ag NCs)上将对硝基苯酚氢化为对氨基苯酚。我们将对开发的材料分别进行 XRD、FTIR 和 SEM-EDX 的相晶体结构、键结构和形态分析。理想的条件是 150 ppm 的催化剂、0.1 mM 的对-NP 和 10 分钟内对-NP 到对-AP 的转化率达到 99%。使用响应面方法学(RSM)创建的多变量预测模型和基于数据的人工神经网络(ANN)模型被认为是预测最大转化效率的最佳方法。与 RSM 模型相比,ANN 模型能更准确地预测效率,而且相对误差(RE0.10)小、回归系数(R2>0.97)高、Willmott-d 指数(dwill-index>0.95)值大,表明模型预测与实验数据之间具有很强的一致性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Synthesis, characterization and application of BR@Ag nanocomposite material for high degree reduction of p-nitro phenol under a suitable condition.

One of the most essential chemical processes that is utilized in the manufacturing of a great deal of contemporary goods is called heterogeneously catalyzed reactions, and it is also one of the most fascinating. Metallic nanostructures are heterogeneous catalysts for range reactions due to their huge surface area, large assembly of active surface sites, and quantum confinement effects. Unprotected metal nanoparticles suffer from irreversible agglomeration, catalyst poisoning, and limited life cycle. To circumvent these technical disadvantages, catalysts are frequently spread on chemically inert materials like as mesoporous Al2O3, ZrO2, and different types of ceramic material. In this research, plentiful bauxite residue is used to create a low-cost alternative catalytic material. We have hydrogenated p-Nitrophenol to p-Aminophenol on bauxite residue (BR) supported silver nanocomposites (Ag NCs). The phase and crystal structure, bond structure and morphological analysis of the developed material will be done XRD, FTIR, and SEM-EDX respectively. The ideal conditions were 150 ppm of catalyst, 0.1 mM of p-NP, and 10 minutes overall up-to 99% conversion of p-NP to p-AP. A multi-variable predictive model created using Response Surface Methodology (RSM) and a data-based Artificial Neural Network (ANN) model were found to be the best ways to predict the maximum conversion efficiency. ANN models predicted efficiency more accurately than RSM models, and the strong agreement between model predictions and experimental data was indicated by their low relative error (RE0.10), high regression coefficient (R2>0.97), and Willmott-d index (dwill-index > 0.95) values.

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来源期刊
Biotechnology & Genetic Engineering Reviews
Biotechnology & Genetic Engineering Reviews BIOTECHNOLOGY & APPLIED MICROBIOLOGY-GENETICS & HEREDITY
CiteScore
6.50
自引率
3.10%
发文量
33
期刊介绍: Biotechnology & Genetic Engineering Reviews publishes major invited review articles covering important developments in industrial, agricultural and medical applications of biotechnology.
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