当前位置:科学网首页 > 小柯机器人 >详情
通过Co(II)-吡甲酸酯框架从SF6/N2混合物中回收高纯度SF6
作者:小柯机器人 发布时间:2024/7/10 14:15:01

北京工业大学李建荣团队报道了通过Co(II)-吡甲酸酯框架从SF6/N2混合物中回收高纯度SF6。相关研究成果发表在2024年7月6日出版的《美国化学会杂志》。

六氟化硫(SF6)广泛应用于电力工业中。然而,它的排放大大加剧了温室效应。从工业废气中直接回收高纯度SF6将有利于其可持续使用,但这是一个相当大的挑战。

该文中,研究人员报道了在稳定的Co(II)-吡唑盐MOF BUT-53(BUT:北京理工大学)中通过吸附分离从SF6/N2混合物中富集SF6,其具有动态分子陷阱的特征。BUT-53在0.1巴和298K下表现出2.82mmol/g的优异SF6吸附吸收,以及2485的前所未有的SF6/N2(10:90)选择性。

此外,BUT-53显著的SF6/N2选择性使其能够通过突破性实验,从低浓度(10%)的混合物中回收高纯度(>99.9%)的SF6。在潮湿条件下(RH=90%)多次循环也很好地保持了优异的SF6/N2分离效率。分子模拟、单晶衍射和吸附动力学研究阐明了相关的吸附机制和耐水性。

附:英文原文

Title: Recovery of High-Purity SF6 from Humid SF6/N2 Mixture within a Co(II)-Pyrazolate Framework

Author: Xin Zhang, Yan-Long Zhao, Xiang-Yu Li, Xuefeng Bai, Qiancheng Chen, Jian-Rong Li

Issue&Volume: July 6, 2024

Abstract: Sulfur hexafluoride (SF6) is extensively employed in the power industry. However, its emissions significantly contribute to the greenhouse effect. The direct recovery of high purity SF6 from industrial waste gases would benefit its sustainable use, yet this represents a considerable challenge. Herein, we report the enrichment of SF6 from SF6/N2 mixtures via adsorptive separation in a stable Co(II)-pyrazolate MOF BUT-53 (BUT: Beijing University of Technology), which features dynamic molecular traps. BUT-53 exhibits an excellent SF6 adsorption uptake of 2.82 mmol/g at 0.1 bar and 298 K, as well as an unprecedented SF6/N2 (10:90) selectivity of 2485. Besides, the remarkable SF6/N2 selectivity of BUT-53 enables recovery of high purity (>99.9%) SF6 from a low concentration (10%) mixture through a breakthrough experiment. The excellent SF6/N2 separation efficiency was also well maintained under humid conditions (RH = 90%) over multiple cycles. Molecular simulation, single-crystal diffraction, and adsorption kinetics studies elucidate the associated adsorption mechanism and water tolerance.

DOI: 10.1021/jacs.4c05075

Source: https://pubs.acs.org/doi/abs/10.1021/jacs.4c05075

期刊信息

JACS:《美国化学会志》,创刊于1879年。隶属于美国化学会,最新IF:16.383
官方网址:https://pubs.acs.org/journal/jacsat
投稿链接:https://acsparagonplus.acs.org/psweb/loginForm?code=1000