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Enhanced uptake of antimonite onto Fe-Zr oxide-loaded MXene: Mass transfer and machine learning data mining

文献类型：期刊论文

作者Liu, Fangfang2; Lu, Mengnan3; Yang, Xiao4; Wang, Yuedi1; Wang, Chunmei2; Dou, Xiaomin2
刊名JOURNAL OF WATER PROCESS ENGINEERING
出版日期2024-07-01
卷号64页码:10
关键词Antimonite Wastewater treatment MXenes Mass transfer Machine learning
ISSN号2214-7144
DOI10.1016/j.jwpe.2024.105583
英文摘要This work proposed introducing nanoscale Fe -Zr oxides onto the layered-surfaces of MXene carriers to maintain well dispersion and anchoring stabilization. The results indicated that Fe -Zr oxide/MXene has a superior adsorption capacity of 209.1 mg/g at pH 7.0. The mass transfer of antimonite onto Fe -Zr oxide/MXene was investigated by numerical solving the homogeneous surface diffusion model. The external mass transfer coefficient ( k f ) and surface diffusion coefficient ( D S ) were determined to be 6.48 x 10 -4 and 1.86 x 10 -8 cm/min, respectively, and the latter was rate-limited. The 2-D profiles of Sb(III) diffusion within the adsorbents were visualized. Data mining and knowledge discovery using machine learning models indicated that the gradient boosting regression (GBR) model successfully predicted the test datasets. The operational conditions outweigh adsorbent properties in capacity prediction, and equilibrium concentration and adsorbent dose, are the most two important features. Using the trained GBR model, the Sb(III) adsorption capacities of MXene (Ti 3 C 2 ) and Fe -Zr/ MXene were well predicted. This work provides insights on understanding the mass transfer mechanisms of Sb (III) in functionalized-MXene adsorbents and employing machine learning models for tuning the treatments for better achievements.
WOS关键词SIMULTANEOUS REMOVAL ; SB(V) ; ADSORPTION ; SORPTION
资助项目National Natural Science Foundation of China[41971024] ; National Natural Science Foundation of China[51978052] ; Beijing Natural Science Foundation[8232039]
WOS研究方向Engineering ; Water Resources
语种英语
WOS记录号WOS:001251978700001
出版者ELSEVIER
资助机构National Natural Science Foundation of China ; Beijing Natural Science Foundation
源URL[http://ir.igsnrr.ac.cn/handle/311030/206455]  
专题陆地表层格局与模拟院重点实验室_外文论文
通讯作者Wang, Chunmei; Dou, Xiaomin
作者单位1.Jilin Univ Architecture, Sch Int Exchange, Civil Engn Sino Foreign Cooperat Running Sch, Changchun 130000, Peoples R China
2.Beijing Forestry Univ, Coll Environm Sci & Engn, Beijing 100083, Peoples R China
3.Beijing Langxinming Environm Technol Co Ltd, 16 W 4th Ring Middle Rd, Beijing 100080, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Fangfang,Lu, Mengnan,Yang, Xiao,et al. Enhanced uptake of antimonite onto Fe-Zr oxide-loaded MXene: Mass transfer and machine learning data mining[J]. JOURNAL OF WATER PROCESS ENGINEERING,2024,64:10.
APA Liu, Fangfang,Lu, Mengnan,Yang, Xiao,Wang, Yuedi,Wang, Chunmei,&Dou, Xiaomin.(2024).Enhanced uptake of antimonite onto Fe-Zr oxide-loaded MXene: Mass transfer and machine learning data mining.JOURNAL OF WATER PROCESS ENGINEERING,64,10.
MLA Liu, Fangfang,et al."Enhanced uptake of antimonite onto Fe-Zr oxide-loaded MXene: Mass transfer and machine learning data mining".JOURNAL OF WATER PROCESS ENGINEERING 64(2024):10.

入库方式： OAI收割

来源：地理科学与资源研究所

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