Key Laboratory of Eco-Environments in Three Gorges Reservoir Region (Ministry of Education), School of Chemistry and Chemical Engineering, Southwest University, Chongqing 400715, China.
Anal Chem. 2020 May 19;92(10):7273-7281. doi: 10.1021/acs.analchem.0c00907. Epub 2020 Apr 28.
To date, the effective discrimination of anionic sulfonate surfactants with tiny differences in structure, considered as environmentally noxious xenobiotics, is still a challenge for traditional analytical techniques. Fortunately, a sensor array becomes the best choice for recognizing targets with similar structures or physical/chemical properties by virtue of principal component analysis (PCA, a statistical technique). Herein, because of the beneficial construction of the statistical strategy and use of two types of luminescent metal-organic frameworks (LMOFs, NH-UiO-66 and NH-MIL-88) as sensing elements, high-throughput discrimination and detection of five anionic sulfonate surfactants and their mixtures are nicely realized for the first time. Significantly, the stacking interaction of aromatic rings and dynamic quenching play essential roles in the generation of diverse fluorescence responses and unique fingerprint maps for individual anionic sulfonate surfactants. Moreover, the mixtures of anionic sulfonate surfactants are also satisfactorily distinguished in environmental water samples, demonstrating the practicability of the sensor array. On the basis of the PCA method, this strategy converts general fluorescence signals into unique optical fingerprints of individual analytes, providing a new opportunity for the application of LMOFs in the field of analytes recognition.
迄今为止，对于被认为具有环境危害性的结构差异微小的阴离子磺酸盐表面活性剂的有效甄别仍然是传统分析技术面临的一项挑战。幸运的是，传感器阵列通过主成分分析（一种统计技术）成为识别具有相似结构或物理/化学性质的目标物的最佳选择。在本研究中，由于统计策略的有益构建以及两种类型的发光金属-有机骨架（LMOFs，NH-UiO-66 和 NH-MIL-88）作为传感元件的使用，首次实现了对五种阴离子磺酸盐表面活性剂及其混合物的高通量甄别和检测。值得注意的是，芳环的堆积相互作用和动态猝灭在产生各种荧光响应和独特的指纹图谱方面发挥了重要作用，可用于鉴别各个阴离子磺酸盐表面活性剂。此外，该传感器阵列还可令人满意地对环境水样中的阴离子磺酸盐表面活性剂混合物进行区分，表明该传感器阵列具有实用性。基于 PCA 方法，该策略将常规荧光信号转换为各个分析物的独特光学指纹，为 LMOFs 在分析物识别领域的应用提供了新的机会。
