Quantitative principal component analysis of multiple metal ions with lanthanide coordination polymer networks

被引:0
|
作者
Huang L. [1 ]
Yu K. [1 ]
Zhou W. [1 ]
Teng Q. [1 ]
Wang Z. [1 ]
Dai Z. [1 ]
机构
[1] Jiangsu Collaborative Innovation Centre of Biomedical Functional Materials and Jiangsu Key Laboratory of Biofunctional Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing
基金
中国国家自然科学基金;
关键词
Fluorescence method; Lanthanide-related nanomaterials; Metal ions; Multi-target analysis;
D O I
10.1016/j.snb.2021.130469
中图分类号
学科分类号
摘要
Approaches oriented toward multi-target analysis are of practical value in obtaining comprehensive information. Herein, discrimination and quantification of multiple metal ions are achieved with fluorescent lanthanide coordination polymer networks (Ln-CPNs) and principal component analysis (PCA). By self-assembly of poly-phosphates (pyrophosphate, PPi or tripolyphosphate, STPP) and cerium ion (Ce3+), two Ln-CPNs are synthesized and emit intense fluorescence due to the ligand field effects of PPi and STPP on Ce3+. Relying on the coordination between poly-phosphates and various metal ions, additional metal ions may compete with Ce3+ for PPi or STPP, forming metal ion-doped Ln-CPNs and adjusting ligand field effect, which subsequently results in the variation of fluorescence intensity. The changes in fluorescence are further processed with PCA and presented with two principal component factors (PC1 and PC2). Accordingly, 14 lanthanide and 12 transition/post-transition metal ions can be evidently identified. Meanwhile, Cu2+ and Ag+ can be accurately quantified depending on relations between dominant PC1 and concentrations of metal ions. Moreover, the fluorescence-PCA sensor is employed to evaluate the composition of Eu3+/Tb3+ and Fe3+/Fe2+ pairs in their mixtures, manifesting its superiority over inductively coupled plasma-atomic emission spectroscopy. Therefore, this work provides an alternative perspective of developing nanomaterials-based fluorescence sensor for multi-target analysis. © 2021 Elsevier B.V.
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