Multilayer activated biochar/UiO-66-NH2 film as intelligent sensing platform for ultra-sensitive electrochemical detection of Pb2+ and Hg2+

In this work, a novel electrochemical sensor based on multilayer activated biochar (AC)/Zr-metal-organic frameworks ([AC/UiO-66-NH2]), combing with artificial neural network (ANN) integrated system is proposed for simultaneous determination of Pb2+ and Hg2+ in water samples. The layer-by-layer (LbL) deposition technique is adopted to obtain multi-layer film of [AC/UiO-66-NH2] with adjustable surface structure and controllable thickness. UiO-66-NH2 with porous structures and high specific surface area is profitable to the adsorption and preconcentration of Pb2+ and Hg2+, meanwhile, the use of AC that is activated by KOH can enlarge the active surface area of the electrode, increase the conductivity and load of UiO-66-NH2. Besides, the layered structure of composite film provides more mass transfer channels, increases ions and electronic diffusion coefficient and creates more additional active sites. Attributing to these impressive features, the detection signals of Pb2+ and Hg2+ are greatly amplified on the [AC/UiO-66-NH2](2) modified electrode. Both the Pb2+ and Hg2+ can be detected with the detection limit down to 1.0 ng.L-1 . In addition, the integration of the prepared sensor and the ANN system is capable of the prediction of Pb2+ and Hg2+ concentrations in water samples with the recovery ranging from 95.93% to 101.4%. Results from ANN model present high correlation (0.99991 and 0.99993 for Pb2+ and Hg2+, respectively) with those from the experiments, validating the promising application for sensing analysis.

Automated summary

A novel electrochemical sensor based on multilayer activated biochar and metal-organic frameworks successfully achieved simultaneous detection of Pb2+ and Hg2+ in water samples with a low detection limit. Integration with an artificial neural network system enables accurate prediction of Pb2+ and Hg2+ concentrations in water samples, showing high correlation with experimental results and promising applications for sensing analysis.