From the perspective of high-throughput recognition: Sulfur quantum dots-based multi-channel sensing platform for metal ions detection

For accurately discriminating and detecting metal ions that pose a threat to both public health and the natural environment, higher requirements are put forward for sensor designing. The emergence of sensor arrays diminishes the difficulty of designing specific receptors to a certain extent, but actualizing high-order sensing from one material with different transduction principles remains a challenge. Herein, a dual-channel sensing platform is firstly proposed for high-throughput recognizing eight metal ions based on simultaneously collecting different optical signals from sulfur quantum dots (S dots). The robust statistical techniques (linear discriminant analysis and hierarchical cluster analysis) convert noticeable signal changes (fluorescence, second-order scattering, and ultraviolet-visible absorption) of S dots, resulting from the affinity of sulfur atoms to metal ions, into unique "fingerprints" and "Euclidean distances", respectively. Hence, eight metal ions and their mixtures are well identified, circumventing the drawbacks of multi-materials sensing array in increasing the cost and complexity of experiments. Significantly, the high-order sensing platform allows the qualitative performance of metal ions and the semi-quantitative analysis of mixed metal ions in actual water samples. Therefore, the high-order sensor array for metal ions discrimination provides a high-throughput, cost-effective, rather powerful and competitive solution to analogues recognition and environmental tests, which constitutes an essential motivation for this work.