Efficient Removal of 2,4-DCP by Nano Zero-Valent Iron-Reduced Graphene Oxide: Statistical Modeling and Process Optimization Using RSM-BBD Approach

In this study, nano zero-valent iron-reduced graphene oxide (NZVI-rGO) composites were synthesized to remove 2,4-dichlorophenol (2,4-DCP) as an efficient adsorbent. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) indicated that NZVI particles were successfully loaded and dispersed uni...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Qi Jing, Shuo Qiao, Wenyu Xiao, Le Tong, Zhongyu Ren
Formato: article
Lenguaje:EN
Publicado: Hindawi - SAGE Publishing 2021
Materias:
Acceso en línea:https://doaj.org/article/3f183546adfc4c7f84852827ddf698b7
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
id oai:doaj.org-article:3f183546adfc4c7f84852827ddf698b7
record_format dspace
spelling oai:doaj.org-article:3f183546adfc4c7f84852827ddf698b72021-11-22T01:11:16ZEfficient Removal of 2,4-DCP by Nano Zero-Valent Iron-Reduced Graphene Oxide: Statistical Modeling and Process Optimization Using RSM-BBD Approach2048-403810.1155/2021/7130581https://doaj.org/article/3f183546adfc4c7f84852827ddf698b72021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7130581https://doaj.org/toc/2048-4038In this study, nano zero-valent iron-reduced graphene oxide (NZVI-rGO) composites were synthesized to remove 2,4-dichlorophenol (2,4-DCP) as an efficient adsorbent. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) indicated that NZVI particles were successfully loaded and dispersed uniformly on rGO nanosheets. Fourier transform infrared spectroscopy (FTIR) analysis showed that the interaction between NZVI-rGO and 2,4-DCP promoted the adsorption process. A three-level, four-factor Box-Behnken design (BBD) of the response surface methodology (RSM) was used to optimize the influencing factors including NZVI-rGO dosage, 2,4-DCP initial concentration, reaction time and initial pH. A statistically significant, well-fitting quadratic regression model was successfully constructed to predict 2,4-DCP removal rate. The high F value (15.95), very low P value (<0.0001), nonsignificant lack of fit, and appropriate coefficient of determination (R2=0.941) demonstrate a good correlation between the experimental and predicted values of the proposed model. The analyses of variance reveal that NZVI-rGO dosage and reaction time have a positive effect on 2,4-DCP removal, whereas the increase of contaminant concentration and initial pH inhibit the removal, whereas the effect of contaminant concentration and initial pH is in reverse, where the change of NZVI-rGO dosage has the greatest effect. The optimum condition is1.215 g/L of NZVI-rGO dosage, 20.856 mg/L of 2,4-DCP concentration, 4.115 of pH, and 8.157 min of reaction time. It is verified by parallel experiments under the optimum condition, achieving the removal efficiency of100%.Qi JingShuo QiaoWenyu XiaoLe TongZhongyu RenHindawi - SAGE PublishingarticlePhysical and theoretical chemistryQD450-801ENAdsorption Science & Technology, Vol 2021 (2021)
institution DOAJ
collection DOAJ
language EN
topic Physical and theoretical chemistry
QD450-801
spellingShingle Physical and theoretical chemistry
QD450-801
Qi Jing
Shuo Qiao
Wenyu Xiao
Le Tong
Zhongyu Ren
Efficient Removal of 2,4-DCP by Nano Zero-Valent Iron-Reduced Graphene Oxide: Statistical Modeling and Process Optimization Using RSM-BBD Approach
description In this study, nano zero-valent iron-reduced graphene oxide (NZVI-rGO) composites were synthesized to remove 2,4-dichlorophenol (2,4-DCP) as an efficient adsorbent. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) indicated that NZVI particles were successfully loaded and dispersed uniformly on rGO nanosheets. Fourier transform infrared spectroscopy (FTIR) analysis showed that the interaction between NZVI-rGO and 2,4-DCP promoted the adsorption process. A three-level, four-factor Box-Behnken design (BBD) of the response surface methodology (RSM) was used to optimize the influencing factors including NZVI-rGO dosage, 2,4-DCP initial concentration, reaction time and initial pH. A statistically significant, well-fitting quadratic regression model was successfully constructed to predict 2,4-DCP removal rate. The high F value (15.95), very low P value (<0.0001), nonsignificant lack of fit, and appropriate coefficient of determination (R2=0.941) demonstrate a good correlation between the experimental and predicted values of the proposed model. The analyses of variance reveal that NZVI-rGO dosage and reaction time have a positive effect on 2,4-DCP removal, whereas the increase of contaminant concentration and initial pH inhibit the removal, whereas the effect of contaminant concentration and initial pH is in reverse, where the change of NZVI-rGO dosage has the greatest effect. The optimum condition is1.215 g/L of NZVI-rGO dosage, 20.856 mg/L of 2,4-DCP concentration, 4.115 of pH, and 8.157 min of reaction time. It is verified by parallel experiments under the optimum condition, achieving the removal efficiency of100%.
format article
author Qi Jing
Shuo Qiao
Wenyu Xiao
Le Tong
Zhongyu Ren
author_facet Qi Jing
Shuo Qiao
Wenyu Xiao
Le Tong
Zhongyu Ren
author_sort Qi Jing
title Efficient Removal of 2,4-DCP by Nano Zero-Valent Iron-Reduced Graphene Oxide: Statistical Modeling and Process Optimization Using RSM-BBD Approach
title_short Efficient Removal of 2,4-DCP by Nano Zero-Valent Iron-Reduced Graphene Oxide: Statistical Modeling and Process Optimization Using RSM-BBD Approach
title_full Efficient Removal of 2,4-DCP by Nano Zero-Valent Iron-Reduced Graphene Oxide: Statistical Modeling and Process Optimization Using RSM-BBD Approach
title_fullStr Efficient Removal of 2,4-DCP by Nano Zero-Valent Iron-Reduced Graphene Oxide: Statistical Modeling and Process Optimization Using RSM-BBD Approach
title_full_unstemmed Efficient Removal of 2,4-DCP by Nano Zero-Valent Iron-Reduced Graphene Oxide: Statistical Modeling and Process Optimization Using RSM-BBD Approach
title_sort efficient removal of 2,4-dcp by nano zero-valent iron-reduced graphene oxide: statistical modeling and process optimization using rsm-bbd approach
publisher Hindawi - SAGE Publishing
publishDate 2021
url https://doaj.org/article/3f183546adfc4c7f84852827ddf698b7
work_keys_str_mv AT qijing efficientremovalof24dcpbynanozerovalentironreducedgrapheneoxidestatisticalmodelingandprocessoptimizationusingrsmbbdapproach
AT shuoqiao efficientremovalof24dcpbynanozerovalentironreducedgrapheneoxidestatisticalmodelingandprocessoptimizationusingrsmbbdapproach
AT wenyuxiao efficientremovalof24dcpbynanozerovalentironreducedgrapheneoxidestatisticalmodelingandprocessoptimizationusingrsmbbdapproach
AT letong efficientremovalof24dcpbynanozerovalentironreducedgrapheneoxidestatisticalmodelingandprocessoptimizationusingrsmbbdapproach
AT zhongyuren efficientremovalof24dcpbynanozerovalentironreducedgrapheneoxidestatisticalmodelingandprocessoptimizationusingrsmbbdapproach
_version_ 1718418276409671680