吸附
丙酮
甲苯
多孔性
微型多孔材料
化学工程
体积热力学
碳纤维
多孔介质
材料科学
化学
有机化学
热力学
复合材料
复合数
物理
工程类
作者
Yu-Wei Jiang,Xiang Xu,Baogen Liu,Changkai Zhou,Huijun Wang,Jingting Qiu,Zheng Zeng,Yan Ge,Liqing Li
标识
DOI:10.1016/j.micromeso.2022.112081
摘要
Adsorption has been proved to be an effective control method of Volatile organic compounds (VOCs). However, the design of porous carbon materials with a high VOCs adsorption performance is still a challenging topic. Herein, we proposed a novel material preparation path, namely, the synthesis of porous carbon materials guided by molecular simulation. Firstly, the optimal adsorption pore size of VOCs was calculated by grand canonical Monte Carlo (GCMC) simulation, and then cork based porous carbon with the optimal pore size controlled by urea and KOH was prepared. The sample treated at 900 °C had a specific surface area of 1940 m 2 /g, a total pore volume of 1.27 cm 3 /g, a micropore volume of 0.71 cm 3 /g, which showed an excellent VOCs adsorption performance at 25 °C. Specifically, the dynamic adsorption capacity of acetone and toluene were 6.1 mmol/g and 5.4 mmol/g, and the static adsorption capacity of acetone and toluene were 17.5 mmol/g (18 kPa) and 9.5 mmol/g (3 kPa), respectively. In terms of the pore size distribution, the contribution of the optimal pore sizes to the adsorption process was estimated to be about 69% for acetone and 59% for toluene. Besides, the relationship between the optimal pore size and its adsorption capacity was explored by mathematical methods, which showed a highly linear one. This study provides a novel idea for the design and optimization of excellent adsorbent materials. • Optimal adsorption pore size of VOCs was calculated by GCMC simulation. • Cork based porous carbon with the optimal pore size was prepared. • NPC-900 showed an excellent VOCs adsorption performance at 25 °C. • The contribution of the optimal pore size to the adsorption process was estimated. • The relationship between optimal pore size and adsorption capacity was explored.
科研通智能强力驱动
Strongly Powered by AbleSci AI