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

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 waste-water) 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.

Automated summary

A portable and cost-effective smartphone-integrated analysis platform was developed for fast identification of heavy-metal ions pollution caused by industrial wastewater discharge, utilizing a colorimetric sensor array and pattern recognition algorithms for detection and classification purposes. Achieved accurate identification of common heavy-metal ions and industrial wastewater models, demonstrating the potential for pollution traceability and water quality analysis with the use of smartphones and IoT technology.