Colorimetric detection of H2O2 with Fe3O4@Chi nanozyme modified µPADs using artificial intelligence.

ABSTRACT

Peroxidase mimicking Fe3O4@Chitosan (Fe3O4@Chi) nanozyme was synthesized and used for high-sensitive enzyme-free colorimetric detection of H2O2. The nanozyme was characterized in comparison with  Fe3O4 nanoparticles (NPs) using X-ray diffraction, Fourier-transform infrared spectroscopy, dynamic light scattering, and thermogravimetric analysis. The catalytic performance of Fe3O4@Chi nanozyme was first evaluated by UV-Vis spectroscopy using 3,3',5,5'-tetramethylbenzidine. Unlike Fe3O4NPs, Fe3O4@Chi nanozyme exhibited an intrinsic peroxidase activity with a detection limit of 69 nM. Next, the nanozyme was applied to a microfluidic paper-based analytical device (µPAD) and colorimetric analysis was performed at varying concentrations of H2O2 using a machine learning-based smartphone app called "Hi-perox Sens++ ." The app with machine learning classifiers made the system user-friendly as well as more robust and adaptive against variation in illumination and camera optics. In order to train various machine learning classifiers, the images of the µPADs were taken at 30 s and 10 min by four smartphone brands under seven different illuminations. According to the results, linear discriminant analysis exhibited the highest classification accuracy (98.7%) with phone-independent repeatability at t = 30 s and the accuracy was preserved for 10 min. The proposed system also showed excellent selectivity in the presence of various interfering molecules and good detection performance in tap water.