An Integrating Platform of Ratiometric Fluorescent Adsorbent for Unconventional Real-Time Removing and Monitoring of Copper Ions.
Ting DuJing WangTianshu ZhangLiang ZhangChengyuan YangTianli YueJing SunTao LiMingu ZhouJianlong WangPublished in: ACS applied materials & interfaces (2020)
Nondegradable heavy metals have caused great dangers to the environment and human health. Combining stimuli-responsive materials with conventional MOF-based adsorbents has been considered an effective method to generate intelligent adsorbents for superior control over the adsorption process. Herein, a smart MOF-based ratiometric fluorescent adsorbent was designed to accurately monitor the progression of the removal of copper ions with dual-emitting fluorescence signal. Unlike the traditional difunctional materials, this delicately designed platform overcomes the huge energy gap to achieve two functions simultaneously. This unconventional platform provides a reliable fluorescent response toward Cu2+ during the removing process, changing linearly related to the degree of the adsorption process, which holds extreme promise in effectively monitoring the adsorption process. The underlying relationship of the adsorption and fluorescence response process toward copper was investigated by density functional theory (DFT) calculations. In particular, because of the favorable ion binding affinity of ZIF-8 and self-calibrating effect of RhB, the as-prepared smart adsorbent demonstrates a superior adsorption performance of 608 mg g-1, broad response range (0.05-200 ppm, 2.07 × 10-7to 8.29 × 10-4 M), ultrahigh sensitivity (0.04 ppm, 1.91 × 10-7 M) toward Cu2+ and strong anti-interference ability. This smart adsorbent opens an intelligent pathway to promote substantial advancements in the fields of environmental monitoring and industrial waste management.
Keyphrases
- aqueous solution
- quantum dots
- density functional theory
- living cells
- heavy metals
- human health
- risk assessment
- fluorescent probe
- energy transfer
- molecular dynamics
- climate change
- high throughput
- single molecule
- sensitive detection
- metal organic framework
- sewage sludge
- machine learning
- deep learning
- life cycle
- binding protein
- drinking water
- dna binding