A hand-held optoelectronic tongue for the identification of heavy-metal ions

Abstract Heavy-metal ions detection conventionally relies on standard instruments that are complicated, time-consuming and constrained to laboratories. To mitigate this problem, we developed a hand-held and cost-effective smartphone-integrated analysis platform that allows fast identification of heavy-metal ions pollution caused by industrial wastewater discharge. This portable platform integrates a colorimetric sensor array consisting of plasmonic nanocolorants and organic chromophores to detect typical heavy-metal ions at the ppm level within 15 s. The image of the colorimetric sensor array is transmitted to the Complementary metal–oxide–semiconductor (CMOS) imager of the smartphone via 96 individual optical fibers, generating RGB differential fingerprints and quantitatively analyzed by pattern recognition algorithms. Facile identification of 13 common heavy-metal ions (Hg2+, Cd2+, Cr6+, Pb2+, Ni2+, Se4+, Mg2+, Ba2+, Zn2+, As3+, Mn6+, Fe3+, Cu2+) was demonstrated using several different multivariate analyses of the digital data library, including principal component and hierarchical cluster analysis. Furthermore, we demonstrated the multiplexed detection and classification of 4 typical industrial wastewater models (Electroplating, Battery, Metallurgical, Pesticide wastewater) and real water samples with this portable platform, and achieved good discrimination and reproducibility. Combined with edge computing of smartphones and Internet-of-Things (IoT) cloud infrastructure, the portable platform shows feasible potential for pollution traceability, environmental monitoring, and water quality analysis.