The development of rapid and sensitive methods for detecting metal ions is essential due to the harmful effects these ions can have on human health and the environment [1, 2]. This study employed an array of d-bCQDs, which emit green fluorescence, and o-gCQDs, which emit blue fluorescence to detect and differentiate various metal ions at different concentrations, including Mn2+, Cu2+, Cr3+ , Co²⁺ , Ni2+, Zn2+, Hg2+, Pb2+, Cd2+, and UO₂ 2+. The effectiveness of the array was evaluated using pattern recognition techniques such as principal component analysis (PCA), hierarchical cluster analysis (HCA), and partial least squares discriminant analysis (PLS-DA) on data matrices obtained from changes in fluorescence data. Using PLS-DA models, samples containing various metal ions were accurately classified into their respective groups, achieving a classification accuracy rate of 97-100% for test samples, which demonstrates high sensitivity and specificity. The metal ion concentrations were evaluated by examining the relationship between the dominant Factor 1 of PLS-DA and the concentrations of the metal ions. A linear range of 50 to 450 µM was established for these metal ions. The detection limits were determined to be 1.7 µM for Ni2+, 6.6 µM for Hg²+ , 4.3 µM for Zn²+ , and 11.2 µM for UO₂ 2+, based on a signal-to-background ratio of 3Sb/m. Additionally, the sensor array was utilized to evaluate the composition of binary and ternary mixtures of the metal ions, as well as to analyze the composition of real samples such as tap water and bottled drinking water. In conclusion, this study provides valuable insights into the development of fluorescence sensors using nanomaterials for the multi-target analysis of metal ions.
