Development of a flow system for decentralized electrochemical analysis of heavy metals using screen-printed electrodes: the importance of sensor stability
Abstract
Year after year, the need for decentralized tools to tackle the monitoring of heavy metal levels in the environment gradually increases. In this context, suitable electrochemical methodologies are widely established and particularly attractive for the production of low-cost miniaturized field-deployable analytical platforms. This work focused on the development of an automatable portable system based on square-wave anodic stripping voltammetry (SWASV) for the on-line detection of heavy metals. The surface of the sensors is appropriately modified and coupled with a fluidic system equipped with an ad-hoc designed flow cell. A custom software tool was introduced to handle the remote-controlled potentiostat and automate the various steps of the procedure, including stirring operations, cleaning phases, SWASV measurements, and data collection. After studying technical and analytical challenges, the final system developed was applied to the simultaneous detection of Cd(II), Pb(II), and Cu(II) in solution, achieving sub-ppb detection limits. Additionally, the practical applicability of the method was successfully applied to river water samples collected from the Loire basin in France.
Read the paper
Related studies
- Nano Biosensors: Properties, applications and electrochemical techniques
- Electrochemically Synthesized MIP Sensors: Applications in Healthcare Diagnostics
- Toward a Harmonization for Using in situ Nutrient Sensors in the Marine Environment
- Electrochemical determination of rutin by using NiFe2O4 nanoparticles-loaded reduced graphene oxide
- Application of Polyoxometalate-based composites for sensor systems: A review
- Sensitive Electrochemical and Thermal Detection of Human Noroviruses Using Molecularly Imprinted Polymer Nanoparticles Generated against a Viral Target
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.