Heavy metal marker-amplified recognition analysis: enhanced water body differentiation via carbon nanomaterial fluorescence and machine learning

Background

Traditional water monitoring methods usually track multiple elements and rely on multi-dimensional data for dimensionality reduction, but they primarily focus on assessing water resource information and spatiotemporal changes rather than effectively distinguished between regional water bodies. Distinguishing regional water bodies is essential for sustainable resource management. Therefore, it is important to establish new strategy for fast and efficient recognition analysis of water bodies.

Result

This study develops a novel fluorescent sensor array based on carbon nanomaterials (CDs, CNSs, CNPs) and machine learning for efficient differentiation of regional water bodies, to which heavy metal markers was added. The concept of heavy metal marker was proposed to amplify the recognition analysis, i.e., a heavy metal marker system comprising of Pb²⁺, Zn²⁺, Cd²⁺ and Ni²⁺ was engineered as signal amplifiers, markedly amplifying fluorescence disparities through competitive binding interaction between the nanomaterials and sample matrices. By combining linear discriminant analysis (LDA), the system achieved 100% classification accuracy validated by near-zero Wilks' Lambda values for both adjacent water bodies and cross-regional counterparts. The sensor exhibits exceptional anti-interference capability, accurately differentiating high-salinity and polluted water sources. Simultaneously, it can achieve high spatial resolution in water source discrimination, resolving adjacent sources separated by <2 km.

Significance

As a proof of concept of heavy metal marker with a combination of Pb²⁺, Zn²⁺, Cd²⁺, and Ni²⁺ as an example, it was proved that adding the markers is an efficient way to achieve the best discrimination goal. This approach may provide a reliable tool for pollution source tracing, demonstrating significant potential for environmental monitoring applications in the future.