Nitrate-nitrogen (NO₃-N) is a significant contaminant in groundwater and seawater, primarily due to the oxidation of ammonia through nitrification. This process disrupts the nitrogen cycle, leading to environmental pollution and posing risks to marine aquaculture and human health. Excessive NO₃-N concentrations can cause severe health effects, such as methemoglobinemia in infants and the formation of carcinogenic N-nitrosamines. In aquatic ecosystems, elevated NO₃-N levels contribute to algal blooms, oxygen depletion, and the decline of marine life, while in aquaculture, they reduce fish growth and immunity, leading to increased mortality.

Traditional NO₃-N detection methods, including ion chromatography and continuous flow analysis, require pretreatment and are time-consuming. Many existing techniques suffer from measurement uncertainties due to interference from complex chemical substances in seawater. Moreover, field-deployable NO₃-N sensors with high sensitivity and selectivity remain scarce. In this study, we developed a transition-metal-based colorimetric sensor capable of detecting NO₃-N on-site without pretreatment. By mixing transition metals (Mn, V, Fe, Co, Cr, Cu, Ni) with solvents and additives, we analyzed the color changes induced by NO₃-N at concentrations ranging from 1 to 100 ppm.

We selected sensors that exhibited a linear increase in color velocity with increasing NO₃-N concentrations and designed an array sensor using these optimal candidates. The performance of the array was validated through hierarchical cluster analysis (HCA) and compositional analysis, confirming its ability to detect NO₃-N in complex matrices. The sensor array's effectiveness was further demonstrated by analyzing seawater samples with varying NO₃-N concentrations, where HCA classification results based on color distance measurements corresponded closely with results obtained from conventional seawater testing methods. Additional HCA classification, incorporating pH, phosphate-phosphorus, total dissolved phosphorus, and chloride ion data, revealed differing clustering patterns, emphasizing the specificity of our sensor for NO₃-N detection. Furthermore, experiments conducted with varying NaCl concentrations confirmed that clustering was primarily driven by NO₃-N levels rather than mineral content. These findings validate that our transition-metal-based colorimetric array sensor can selectively detect NO₃-N in complex seawater matrices, offering a promising approach for rapid, on-site environmental monitoring and multi-target sensing applications.

- Authors (Pusan National University)

 · First author: Jung-Geun Lee (Institute of Nanobio Convergence)

 · Corresponding authors: Tae-Young Jeong (Institute of Nanobio Convergence), Jin-Woo Oh (Department of Nano Fusion Technology)

- Title of original paper: Point-of-Care-Testing NO₃-N Detection Technology with Selected Transition-Metal-Based Colorimetric Sensor Arrays

- Journal: ACS sensors

- DOI: https://pubs.acs.org/doi/10.1021/acssensors.4c02771