A Zinc(II) Schiff Base Complex as Fluorescent Chemosensor for the Selective and Sensitive Detection of Copper(II) in Aqueous Solution.
Ivan Pietro OliveriAgostino AttinàSanto Di BellaPublished in: Sensors (Basel, Switzerland) (2023)
The development of chemosensors able to detect analytes in a variety of sample matrices through a low-cost, fast, and direct approach is of current interest in food, health, industrial, and environmental fields. This contribution presents a simple approach for the selective and sensitive detection of Cu 2+ ions in aqueous solution based on a transmetalation process of a fluorescent substituted Zn(salmal) complex. Transmetalation is accompanied by relevant optical absorption changes and quenching of the fluorescence emission, leading to high selectivity and sensitivity of the chemosensor, with the advantage of not requiring any sample pretreatment or pH adjustment. Competitive experiments demonstrate a high selectivity of the chemosensor towards Cu 2+ with respect to the most common metal cations as potential interferents. A limit of detection down to 0.20 μM and a dynamic linear range up to 40 μM are achieved from fluorometric data. By exploiting the fluorescence quenching upon formation of the copper(II) complex, simple paper-based sensor strips, visible to naked eyes under UV light, are used for the rapid, qualitative, and quantitative in situ detection of Cu 2+ ions in aqueous solution over a wide concentration range, up to 10.0 mM, in specific environments, such as in industrial wastewater, where higher concentrations of Cu 2+ ions can occur.
Keyphrases
- aqueous solution
- sensitive detection
- loop mediated isothermal amplification
- quantum dots
- energy transfer
- low cost
- wastewater treatment
- human health
- heavy metals
- oxide nanoparticles
- single molecule
- label free
- high resolution
- living cells
- healthcare
- risk assessment
- optical coherence tomography
- systematic review
- electronic health record
- health information
- high speed
- data analysis
- real time pcr
- anaerobic digestion
- deep learning
- neural network