AIEgen-based metal-organic frameworks as sensing "toolkit" for identification and analysis of energetic compounds

被引:2
|
作者
Zhu, Long-yi [1 ]
Zhu, Bin [1 ]
Wan, Ying [2 ]
Deng, Sheng-yuan [3 ]
Yu, Zhang-dong [1 ]
Zhang, Chong [1 ]
Luo, Jun [1 ]
机构
[1] Nanjing Univ Sci & Technol, Sch Chem & Chem Engn, Nanjing 210094, Peoples R China
[2] Nanjing Univ Sci & Technol, Sch Mech Engn, Nanjing 210094, Peoples R China
[3] Nanjing Univ Sci & Technol, Sch Environm & Biol Engn, Nanjing 210094, Peoples R China
来源
ENERGETIC MATERIALS FRONTIERS | 2022年 / 3卷 / 04期
基金
中国国家自然科学基金;
关键词
Energetic compounds; Aggregation-induced emission; Metal-organic frameworks; Chemo-sensing; Fluorescence pattern recognition; SENSOR;
D O I
10.1016/j.enmf.2022.09.001
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The identification and analysis of energetic compounds are important technology in the field of national defence and environmental monitoring. However, as the rapid development of high-energy density materials, designing universal detection strategy for energetic compounds and their composites is still challenging. Herein, we construct a suite of AIEgen-based metal-organic frameworks (MOFs) as the sensing "toolkit" for discriminating four types of energetic compounds, including nitroaromatics, nitrogen-rich heterocycles, nitramine and nitro-enamine. Through manipulating the structure of linker and coordination patterns of MOFs scaffold, diversified fluorescence responses can be obtained to simultaneously probe the fluorescence quenching and competitive binding abilities of different energetic compounds in aqueous systems. The "toolkit" sensor array with fluores-cence pattern recognition could successfully discriminate seven iconic energetic compounds by principal component analysis. Further performance studies show that the heterogenous materials of energetic compounds can be quantitatively analyzed with linear relationship between stoichiometries and principal component values. The composites from different types of energetic compounds are rapidly identified via AIE MOF-based logic operations. The resulting sensing "toolkit" provides a new avenue for designing olfactory-mimic sensing system.
引用
收藏
页码:257 / 265
页数:9
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