Paper-based colorimetric sensor using bimetallic Nickel-Cobalt selenides nanozyme with artificial neural network-assisted for detection of H2O2 on smartphone

Abstract

Paper-based analytical devices (PADs) integrated with smartphones have shown great potential in various fields, but they also face challenges such as single signal reading, complex data processing and significant environmental impacting. In this study, a colorimetric PAD platform has been proposed using bimetallic nickel-cobalt selenides as highly active peroxidase mimic, smartphone with 3D-printing dark-cavity as a portable detector and an artificial neural network (ANN) model as multi-signal processing tool. Notably, the optimized nickel-cobalt selenides (Ni_0.75Co_0.25Se with Ni to Co ratio of 3/1) exhibit excellent peoxidase-mimetic activities and are capable of catalyzing the oxidation of four chromogenic reagents in the presence of H₂O₂. Using a smartphone with image capture function as a friendly signal readout tool, the Ni_0.75Co_0.25Se based four channel colorimetric sensing paper is used for multi-signal quantitative analysis of H₂O₂ by determining the Grey, red (R), green (G) and blue (B) channel values of the captured pictures. An intelligent on-site detection method for H₂O₂ has been constructed by combining an ANN model and a self-programmed easy-to-use smartphone APP with a dynamic range of 5 μM to 2 M. Noteworthy, machine learning-assisted smartphone sensing devices based on nanozyme and 3D printing technology provide new insights and universal strategies for visual ultrasensitive detection in a variety of fields, including environments monitoring, biomedical diagnosis and safety screening.

Keywords: Artificial neural network; Bimetallic nickel–cobalt selenides nanozyme; Colorimetric sensor; Paper-based analytical device; Smartphone.