Pollution by heavy metals is threatening the environment and human health, yet there is a lack of a rapid methods to detect multiple metal ions. Here, we built a fluorescence sensor array based on carbon quantum dots to detect Cr⁶⁺, Fe³⁺, Fe²⁺, and Hg²⁺ in environmental samples. We added xylenol orange as the receptor to construct the sensor array under pH regulation. We also designed a SX-model by combining stepwise prediction and machine learning to assist the fluorescence sensor array in detecting single and mixed heavy metal ions in deionized water and real samples. Results show that the sensor array detects four heavy metal ions within a concentration range of 1–50 μM with an accuracy of 95%, and the sensor identifies binary mixed samples with an accuracy of 95%. In addition, metal ions occurring in 144 lake water samples were discriminated with 100% accuracy. Overall, the SX-model-assisted fluorescence sensor array is an efficient method for detecting heavy metal ions in environmental samples.

重金属污染正在威胁环境和人类健康，但缺乏快速检测多种金属离子的方法。在这里，我们构建了一个基于碳量子点的荧光传感器阵列来检测Cr ⁶⁺、Fe ³⁺、Fe ²⁺和Hg ^{2+ 。}在环境样品中。我们添加了二甲酚橙作为受体来构建 pH 调节下的传感器阵列。我们还通过结合逐步预测和机器学习设计了一个 SX 模型，以帮助荧光传感器阵列检测去离子水和真实样品中的单一和混合重金属离子。结果表明，传感器阵列检测浓度范围为 1-50 μM 的四种重金属离子，准确率达到 95%，传感器识别二元混合样品的准确率达到 95%。此外，对 144 个湖水样品中的金属离子进行了 100% 的准确度判别。总体而言，SX 模型辅助荧光传感器阵列是检测环境样品中重金属离子的有效方法。