吸附
活性炭
化学工程
碳纤维
聚氨酯
化学
热解
弹性体
比表面积
碳纳米泡沫
亚甲蓝
多孔性
催化作用
材料科学
复合材料
有机化学
光催化
复合数
工程类
作者
Mahitha Udayakumar,Bilal El Mrabate,Tamás Koós,Katalin Szemmelveisz,Ferenc Kristály,Máté Leskó,Ádám Filep,Róbert Géber,Mateusz Schabikowski,Péter Baumli,János Lakatos,Pál Tóth,Zoltán Németh
标识
DOI:10.1016/j.arabjc.2021.103214
摘要
Carbon foams have gained significant attention due to their tuneable properties that enable a wide range of applications including catalysis, energy storage and wastewater treatment. Novel synthesis pathways enable novel applications via yielding complex, hierarchical material structure. In this work, activated carbon foams (ACFs) were produced from waste polyurethane elastomer templates using different synthesis pathways, including a novel one-step method. Uniquely, the produced foams exhibited complex structure and contained carbon microspheres. The ACFs were synthesized by impregnating the elastomers in an acidified sucrose solution followed by direct activation using CO2 at 1000 ℃. Different pyrolysis and activation conditions were investigated. The ACFs were characterized by a high specific surface area (SBET) of 2172 m2/g and an enhanced pore volume of 1.08 cm3/g. Computer tomography and morphological studies revealed an inhomogeneous porous structure and the presence of numerous carbon spheres of varying sizes embedded in the porous network of the three-dimensional carbon foam. X-ray diffraction (XRD) and Raman spectroscopy indicated that the obtained carbon foam was amorphous and of turbostratic structure. Moreover, the activation process enhanced the surface of the carbon foam, making it more hydrophilic via altering pore size distribution and introducing oxygen functional groups. In equilibrium, the adsorption of methylene blue on ACF followed the Langmuir isotherm model with a maximum adsorption capacity of 592 mg/g. Based on these results, the produced ACFs have potential applications as adsorbents, catalyst support and electrode material in energy storage systems.
科研通智能强力驱动
Strongly Powered by AbleSci AI