Conversion of Fluorescence Signals into Optical Fingerprints Realizing High-Throughput Discrimination of Anionic Sulfonate Surfactants with Similar Structure Based on a Statistical Strategy and Luminescent Metal-Organic Frameworks.

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, NH2-UiO-66 and NH2-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.