Suppr超能文献

超声辅助在羧化纤维素纤维表面合成MOF-199用于高效吸附亚甲基蓝

Ultrasonic-assisted synthesis of MOF-199 on the surface of carboxylated cellulose fibers for efficient adsorption of methylene blue.

作者信息

Liang Zhanpeng, Liang Yuehui, Yu Pengjun, Wang Xin

机构信息

Inner Mongolia Agricultural University College of Material Science and Art Design Hohhot Inner Mongolia China

Inner Mongolia Key Laboratory of Sandy Shrubs Fibrosis and Energy Development and Utilization Hohhot Inner Mongolia China.

出版信息

RSC Adv. 2024 May 8;14(21):15095-15105. doi: 10.1039/d4ra02099e. eCollection 2024 May 2.

Abstract

A high-efficiency porous adsorbent, MOF-199/carboxylated cellulose fibers (MOF-199/CCF), was synthesized at room temperature through carboxylation modification, simple sonication, and vacuum drying. The sonication method produced small MOF-199 particles (tens of nanometers), which allowed for uniform distribution of MOF-199 on CCF and improved its efficiency. The presence of CCF carriers reduces the agglomeration of MOF-199 and enhances its performance. The BET-specific surface area of MOF-199/CCF is 264.83 m g, which is much larger than that of CCF (2.31 m g), proving the successful modification of CCF by MOF-199. MOF-199/CCF exhibits better adsorption capacity than CCF, with an adsorption capacity of 659.6 mg g of methylene blue within 30 minutes, and good recycling performance. This work presents a straightforward method for preparing efficient cellulose-based adsorbent materials and offers a novel approach for synthesizing MOF composites.

摘要

一种高效的多孔吸附剂,即MOF-199/羧化纤维素纤维(MOF-199/CCF),通过羧化改性、简单超声处理和真空干燥在室温下合成。超声处理方法产生了小尺寸的MOF-199颗粒(几十纳米),这使得MOF-199在CCF上均匀分布并提高了其效率。CCF载体的存在减少了MOF-199的团聚并增强了其性能。MOF-199/CCF的BET比表面积为264.83 m²/g,远大于CCF的比表面积(2.31 m²/g),证明了MOF-199对CCF的成功改性。MOF-199/CCF表现出比CCF更好的吸附容量,在30分钟内对亚甲基蓝的吸附容量为659.6 mg/g,并且具有良好的循环性能。这项工作提出了一种制备高效纤维素基吸附材料的简便方法,并为合成MOF复合材料提供了一种新途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd14/11078215/7ec3a33ba3e7/d4ra02099e-f1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验