摘要
针对含Co^(2+)重金属离子废水的处理和粉末状生物质多孔炭难以回收的问题,提出以核桃青皮为原料,通过水热法制备炭前驱体,加入Fe(NO_(3))_(3)·9H_(2)O浸渍后,热解制备磁性生物质多孔炭(MBC_(x))。采用扫描电子显微镜、氮气吸附-脱附、综合物性系统和X射线光电子能谱等手段对MBC_(x)进行表征,并考察了MBC_(x)对废水中Co^(2+)的吸附性能。结果表明,当前驱体与Fe(NO_(3))_(3)·9H_(2)O质量比为15:9时,所得MBC9的比表面积为249 m^(2)·g^(-1),平均孔径为4.45 nm,MBC_(9)表面的O、N和Fe元素的摩尔分数分别为14.04%、3.17%和1.28%。Langmuir吸附等温式能很好地描述MBC_(9)对Co^(2+)的吸附过程,最大理论吸附量为130.38 mg·g^(-1)。此外,该磁性炭材料可在使用后通过磁力作用从溶液中分离回收。
In order to solve the problems of Co^(2+)containing wastewater treatment and biomass carbon powder recovery,magnetic biomass carbon(MBC_(x))was prepared by pyrolysis of carbon precursors impregnated with Fe(NO_(3))_(3)·9H_(2)O.The carbon precursors were from walnut peel and pretreated by a hydrothermal method.MBC_(x) was characterized by scanning electron microscopy,nitrogen adsorption-desorption apparatus,synthetic physical properties system and X-ray photoelectron spectroscopy,and its Co^(2+)adsorption performance was investigated.The results show that when the mass ratio of precursor to Fe(NO_(3))_(3)·9H_(2)O is 15:9,the specific surface area of the as-made MBC_(9) is 249 m^(2)·g^(-1) and the average pore size is 4.45 nm,and the molar fractions of oxygen,nitrogen and Fe elements on MBC_(9) surface are 14.04%,3.17% and 1.28%,respectively.The Co^(2+)adsorption process on MBC_(9) can be fitted by the Langmuir isotherm model,and the maximum theoretical adsorption capacity is 130.38 mg·g^(-1).The used MBC_(x) can be separated from the solution by magnetic force.
作者
余谟鑫
朱博文
张书海
蒯乐
韦一
王晓婷
张晨
李忠
YU Mo-xin;ZHU Bo-wen;ZHANG Shu-hai;KUAI Le;WEI Yi;WANG Xiao-ting;ZHANG Chen;LI Zhong(School of Chemistry and Chemical Engineering,Anhui University of Technology,Ma'anshan 243000,China;Sinosteel New Materials Co.Ltd.,Ma'anshan 243000,China;Magang(Group)Holding Co.Ltd.,Ma'anshan 243000,China;School of Chemistry and Chemical Engineering,South China University of Technology,Guangzhou 510640,China)
出处
《高校化学工程学报》
EI
CAS
CSCD
北大核心
2022年第4期610-616,共7页
Journal of Chemical Engineering of Chinese Universities
基金
中国博士后科学基金(2019M652173)
安徽高校自然科学研究项目(KJ2021A0399)
安徽省博士后科研项目(2021B547)。