纳米材料对阳离子型染料的吸附

摘要

染料工业发展迅速，已广泛应用于食品、医药、印染及化妆品中。随着染料的大规模应用，越来越多含有染料的废水在生产和使用中释放到环境中，带来的环境污染日趋严重。当染料废水排入水体时造成受污染水域色度增加,影响入射光线量,进而对水质、水体生物、人体健康和生态造成严重的危害。三苯甲烷类染料是合成染料中应用最广的染料类型之一，孔雀石绿和结晶紫作为此类染料的典型代表，已有研究表明该类染料对人体具有致癌、致畸和致突变的作用,对人类健康具有潜在危害。因此处理此类染料废水已成为亟待解决的重大问题。本文利用X射线衍射仪与X射线光电子能谱仪对零价纳米锌（购买）进行了表征，并研究了其对孔雀石绿水溶液的吸附。采用响应面实验设计方法，研究了pH、温度、反应时间及初始浓度对染料的吸附影响。并在响应面的基础上，采用神经网络结合粒子群和神经网络结合遗传算法模型预测其最优反应条件，获得最大去除率。结果表明，神经网络结合遗传算法预测的最佳吸附条件为：pH为5.70，温度为27.19℃，反应时间为110.62 min和初始浓度为607.03 mg/L。在上述条件下预测的去除率为94.12%，与之对应的实验结果为90.72%，两者绝对误差为3.4%。由此表明,神经网络结合遗传算法模型具有很好的可靠性。经 Langmuir ，Freundlich和 Temkin等温吸附模型拟合后，发现 Langmuir吸附模型可以很好地描述该过程，且计算得到的最大吸附量为1000.00 mg/g。零价纳米锌对孔雀石绿的吸附符合伪二级动力学方程，且材料对染料的吸附具有自发吸热以及熵增的特性。 本文还借助了X射线衍射仪、能谱仪、扫描电镜、X射线光电子能谱仪、氮气吸附、拉曼光谱仪对所合成的还原氧化石墨烯负载铁镍双金属复合材料进行了表征分析，并研究了其对结晶紫水溶液的吸附作用。采用响应面优化预测结晶紫的最优去除条件，选择pH、温度、反应时间以及初始浓度为影响因子。结果表明；结晶紫的最佳条件为pH为3.0，温度为40.6℃，反应时间为18 min和初始浓度为369.9 mg/L. 在此条件下预测的去除率为88.2%，实验值为75.8%。另外，在响应面的基础上，采用神经网络结合粒子群与神经网络结合遗传算法模型优化预测结晶紫的去除条件。神经网络结合粒子群预测的最佳条件为:pH为4，温度为45℃，反应时间为18 min，初始浓度为300 mg/L。此条件下的预测去除率为88%，实验值为84.5%。神经网络结合遗传算法预测的最佳条件为：pH为3.3，温度为36.1℃，反应时间为15.8 min和初始浓度为331.2 mg/L，此条件下的预测去除率为86.9%，实验值为81.3%。预测值与实验值的绝对误差表明神经网络结合粒子群模型对预测还原氧化石墨烯负载铁镍双金属复合材料去除结晶水溶液具有更好的预测能力。Freundlich与伪二级动力学模型能更好的描述此吸附过程，且该过程具有自发吸热的特性。

目录

第一个书签之前

Contents

ABBREVIATIONS

Abstract

Introduction

1.1. Hazardous dyes of Malachite green and Crystal

1.2. Classification of dyes

1.2.2. Anthraquinone dyes

1.2.3. Triarylmethane dyes

1.3. Dye removal techniques

1.3.1. Biological methods

1.3.1.1. Degradation of dye by fungi

1.3.1.2. Degradation dye by yeast

1.3.1.3. Degradation dyes by bacteria

1.3.2. Chemical methods

1.3.2.1. Coagulation-flocculation

1.3.2.2. Ozonation.

1.3.2.3. Photocatalytic methods

1.3.3. Physical methods

1.3.3.1. Membrane filtration

1.3.3.2. Ion exchange

1.3.3.3. Adsorption

1.4. Adsorption of dyes by nanoparticles

1.4.1. Nano zerovalent iron

1.4.2. Nanomaterials with magnetic properties

1.4.3. Nano magnesium oxide

1.4.4. Graphene oxide and reduced graphene oxide b

1.5. Modelling and optimization techniques

1.6. Main objectives of the present work

Preparation of reduced graphene oxide-supported bi

2.1. Experimental section

2.1.1. Materials

2.1.2. Experimental instruments

2.2. Preparation of the nanomaterials

2.2.1. Synthesis of graphene oxide (GO)

2.3. Characterization of the commercially availabl

2.4. Batch adsorption experiments

2.5. Determine the zero point of charge of rGO/Fe/

2.6. Results and discussion

2.6.1. Characterization of the commercially availa

2.6.2. Characterization of rGO/Fe/Ni composites

2.6.3. The zero point of charge for rGO/Fe/Ni comp

2.7. Summary

Modeling and optimization

3.1. Modeling and Optimization by RSM

3.1.1. Modeling and Optimization for MG removal on

3.1.2. Modeling and optimization for CV removal on

3.2. Prediction by BP-ANN.

3.2.1 Prediction for the adsorption of MG onto the

3.2.2. Prediction for the adsorption of CV onto rG

3.3. Modelling and optimization by ANN-PSO and ANN

3.3.1 Modelling and optimization for the adsorptio

3.3.2. Modeling and optimization for the removal o

3.4. Comparison the optimum removal efficiency amo

3.4.1. The adsorption of MG onto the commercially

3.4.2. The adsorption of CV onto rGO/Fe/Ni composi

3.5. Summary

Equilibrium isotherms, adsorption kinetic and adso

4.1. Equilibrium Isotherms

4.1.1. Equilibrium isotherms for the adsorption of

4.1.2. Equilibrium isotherms for the adsorption of

4.2. Adsorption kinetics

4.2.1. Kinetics study for the adsorption of MG by

4.2.2. Kinetics study for the adsorption of CV by

4.3. Thermodynamics study

4.3.1. Thermodynamics study for the adsorption of

4.3.2. Thermodynamics study for the adsorption of

4.4. Summary

Conclusion

Prospects

References

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