five

Selective Toluene Separation via π–π Interactions in Cobalt sql Network Rubber Composites

收藏
Figshare2026-04-28 收录
下载链接:
https://figshare.com/articles/dataset/Selective_Toluene_Separation_via_Interactions_in_Cobalt_sql_Network_Rubber_Composites/30028353
下载链接
链接失效反馈
官方服务:
资源简介:
Selective removal of aromatic contaminants from water matrices poses substantial difficulties in environmental remediation processes, necessitating sophisticated materials with discriminatory molecular recognition properties. Herein, we report a framework-elastomer hybrid membrane containing [Co­(4-pmntd)2(NO3)2] (4-pmntd represents N,N′-bis­(4-pyridylmethyl)­naphthalene diimide) designed for effective toluene recovery from trace aqueous environments. Systematic structural analysis employing crystallographic diffraction, gas adsorption measurements, surface electron spectroscopy, and proton nuclear magnetic resonance elucidates the material’s architectural characteristics and surface phenomena. Computational modeling through Grand Canonical Monte Carlo methods (GCMC) and dispersion-corrected density functional theory (DFT-D) illuminates the atomic-scale sorption pathways and energetic parameters. The electron-deficient naphthalene diimide rings facilitate selective π–π charge-transfer interactions with electron-rich toluene molecules. The composite demonstrates exceptional separation performance, achieving toluene detection at concentrations as low as 5.3 × 10–6 mol/L with 97.6% extraction purity and 94.0% recovery efficiency. The material operates effectively at ambient temperature with rapid separation kinetics and excellent recyclability. Computational studies confirm optimal binding sites within the framework channels, validating the selective molecular recognition mechanism. This work advances coordination-network-based separation technology, offering a sustainable solution for industrial toluene remediation with immediate applications in environmental water treatment.
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作