Chen Ming-Ming, Zhang Yan-Qing, Cheng Lu-Chen, Zhao Fang-Jie, Wang Peng
Centre for Agricultural and Environmental Health, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, 210095, China.
Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, China.
Biosens Bioelectron. 2025 Nov 1;287:117706. doi: 10.1016/j.bios.2025.117706. Epub 2025 Jun 18.
The co-contamination of multiple pollutants in complex environmental matrices poses a significant threat to ecosystems and public health, necessitating advanced detection methods. In this study, we developed a machine learning-powered chemical sensor array capable of simultaneously identifying and discriminating nine heavy metal(loid)s (Cr[III], Cd[II], Hg[II], Pb[II], Co[II], Zn[II], Mn[II], As[III], and Se[VI]) and five pesticides (propiconazole, penconazole, cyproconazole, indoxacarb, and azoxystrobin). Using three distinct copper nanoclusters (Cu NCs) with unique ligand-based binding affinities, the system generated characteristic fluorescent "fingerprints". By coupling with machine-learning algorithms (LDA and HCA), the sensor array achieved 100 % identification accuracy within 10 min, with exceptional sensitivity (limits of detection: ∼0.5 nM for heavy metal(loid)s and ∼7.1 ppb for pesticides). This approach was validated using real-world samples, including blood, urine, soil, tap water, vegetables, and fruits, demonstrating high selectivity, anti-interference capability, and practical applicability. This proposed nanosensor array provides a robust, rapid, and sensitive platform for multi-target detection, offering transformative solutions in food safety, environmental monitoring, and public health surveillance.
复杂环境基质中多种污染物的共同污染对生态系统和公众健康构成了重大威胁，因此需要先进的检测方法。在本研究中，我们开发了一种由机器学习驱动的化学传感器阵列，能够同时识别和区分九种重金属（类金属）（Cr[III]、Cd[II]、Hg[II]、Pb[II]、Co[II]、Zn[II]、Mn[II]、As[III]和Se[VI]）和五种农药（丙环唑、戊唑醇、环丙唑醇、茚虫威和嘧菌酯）。该系统使用三种具有独特基于配体结合亲和力的不同铜纳米簇（Cu NCs）生成特征荧光“指纹”。通过与机器学习算法（LDA和HCA）相结合，该传感器阵列在10分钟内实现了100%的识别准确率，具有出色的灵敏度（检测限：重金属（类金属）约为0.5 nM，农药约为7.1 ppb）。该方法通过使用包括血液、尿液、土壤、自来水、蔬菜和水果在内的实际样品进行了验证，证明了其具有高选择性、抗干扰能力和实际适用性。所提出的纳米传感器阵列提供了一个强大、快速且灵敏的多目标检测平台，为食品安全、环境监测和公共卫生监测提供了变革性解决方案。
