Detection of total nitrogen contaminants in agricultural water using a poly(m-aminophenol) and silver nanoparticle hybrid sensor

• Hybrid of poly(m-aminophenol) with 4-quinonimine functionalized silver nanoparticles. • Chemiresistive sensing of total nitrogen as nitrogenous contaminants in agricultural water. • Selective nitrogenous contaminants, viz., ammonium, nitrate and nitrite ions. • Sensitivity of 10 µA mM −1 and a limit of detection of (LOD) of 0.01 mM. • 96 ± 4 % accuracy for the sensing of nitrogen in agricultural river water. The nitrogen cycle in the environment is negatively impacts the biosphere through the accumulation of nitrogenous pollutants, including nitrite, nitrate, and ammonium ions, in aquatic systems. There is a real need to adapt/further develop easy and reliable real-time sensing of total nitrogen, as well as to monitor nitrogen in aqueous medium. The aim was to develop a chemiresistive sensor based on a hybrid of poly( meta -aminophenol) (PmAP), with 4-quinonimine functionalized silver nanoparticles for sensitive and selective detection of total nitrogen as nitrogenous contaminants in aqueous media. First, 4-quinonimine functionalized material was synthesized, followed by the successful preparation of a core–shell hybrid with PmAP. The prepared hybrid was confirmed to have a polymer shell measuring 20–40 nm surrounding a functionalized silver core, which has an average diameter of 23 nm and varies between 5 to 50 nm. A two-probe sensor layer was fabricated on a screen-printed carbon electrode for the efficient detection of total nitrogen as nitrite, nitrate, and ammonium ions in aqueous media. The sensor exhibited a selective sensing response toward nitrogen, with a sensitivity of 10 µA mM −1 , a limit of detection (LOD) of 0.01 mM and a sensor resolution of 0.012 mM within the linearity range of 0.01–2 mM for the nitrogen in terms of those nitrogenous contaminants. The fabricated sensor shows strong applicability for real-time nitrogen monitoring in aqueous media, particularly for agricultural applications and smart agriculture systems. An average 96 ± 4 % accuracy was also verified for nitrogen sensing in agricultural river water samples.