Electrochemical sensor designs for the simultaneous detection of various heavy metals using DPN technology

Ron Peretz, Chemical Engineering , Sami Shamoon College of Engineering, Beer-Sheva, Israel (ronpe2@ac.sce.ac.il)
Moshe Zohar, Electrical And Electronics Engineering, Sami Shamoon College Of Engineering, Beer-sheva, Israel
Dror Shamir, Analytical Chemistry, Nuclear Research Centre Negev, Beer-sheva, Israel
Ariela Burg, Chemical Engineering , Sami Shamoon College Of Engineering, Beer-sheva, Israel

The rapid pace of global population growth and resource consumption necessitates continuous, on-site monitoring of water sources for toxic contaminants, such as heavy metals. Current regulatory limits for most heavy metals demand detection capabilities at the parts-per-billion (ppb) level. While standard laboratory methods, such as ICP, are highly sensitive, their lack of portability drives the urgent need for efficient, field-deployable alternatives. The global focus has therefore shifted toward developing electrochemical nanosensors. A significant challenge in this field, however, is the overlap of electrochemical signals when detecting multiple metal cations simultaneously in a solution. Our research addresses this crucial limitation by leveraging Dip-Pen Nanolithography (DPN). The current study focuses on developing a single electrochemical sensor capable of the parallel, selective detection of Copper (Cu(II)), Lead (Pb(II)), and Cadmium (Cd(II)). By employing DPN, we patterned a proprietary ink (comprising PDMS, hexane, and the ligand 1,8-diaminonaphthalene (DAN) as highly controlled nanoclusters on a conductive surface. DPN provides tight control over critical parameters, including nanocluster size, ligand concentration, and the pattern pitch (the precise distances between the clusters) – parameters we hypothesize are key to overcoming signal overlap. The research successfully demonstrates that the developed electrochemical sensor achieves selective, parallel detection of Cu(II), Pb(II), and Cd(II) with excellent sensitivity. These findings validate DPN as a robust methodology for manufacturing advanced nanosensors capable of addressing the complex challenge of simultaneous multi-analyte detection in environmental monitoring.