吸附
煅烧
化学工程
多孔性
生物量(生态学)
碳纤维
比表面积
材料科学
纤维素
解吸
X射线光电子能谱
氮气
微型多孔材料
化学
活性炭
吸附剂
生物炭
水热碳化
热液循环
热解
热稳定性
多孔介质
作者
Xiaoqi Min,Hao Lu,Wenjun Zhao,Huachen Liu,Yuanhao Wang
出处
期刊:ACS omega
[American Chemical Society]
日期:2026-01-27
卷期号:11 (5): 8761-8772
标识
DOI:10.1021/acsomega.5c12518
摘要
Biomass porous carbon materials have broad application prospects in the field of CO2 capture due to their extremely high specific surface area and rich pore structure. However, the biomass precursors, due to their diverse components and complex microstructure, result in poor stability of the prepared porous carbon. Developing methods for preparing high-performance biomass porous carbon is of crucial importance. In this study, high-content plant fibers from cotton stalks were extracted through alkaline treatment and ultrasonic disruption. Biochar was prepared through high-temperature hydrothermal reaction, K2CO3 and urea were introduced as activation agents and nitrogen dopants for the biochar, respectively. After calcination at high temperatures, nitrogen-doped porous carbon materials were obtained. The results showed that the prepared materials had a high specific surface area of 1383 m2/g and formed a rich pore structure dominated by micropores. XPS analysis confirmed that nitrogen-doped materials successfully introduced nitrogen-containing basic functional groups, such as pyridine nitrogen on the surface, enhancing the Lewis acid–base interaction between the samples and CO2, and improving the CO2 adsorption performance. The adsorption capacity of NKCS-3 sample at 1 bar and 0 °C was 5.17 mmol/g, at 1 bar and 25 °C was 3.95 mmol/g. Moreover, this porous carbon exhibited excellent cyclic stability and good regeneration performance. After 50 adsorption and desorption cycles, its maximum adsorption capacity still remained above 95%. This study provides an effective and feasible implementation path for extracting high-content cellulose from biomass precursors to prepare high-performance biomass porous carbon.
科研通智能强力驱动
Strongly Powered by AbleSci AI