Due to the lack of a general descriptor to predict the activity of nanomaterials, the current exploration of nanozymes mainly depended on trial-and-error strategies, which hindered the effective design of nanozymes. Here, with the help of a large number of Ni-O-Co bonds at the interface of heterostructures, a prediction descriptor was successfully determined to reveal the double enzyme-like activity mechanisms for Ni/CoMoO4. Additionally, DFT calculations revealed that interface engineering could accelerate the catalytic kinetics of the enzyme-like activity. Ni-O-Co bonds were the main active sites for enzyme-like activity. Finally, the colorimetric signal and intelligent biosensor of Ni/CoMoO4 based on deep learning were used to detect organophosphorus and ziram sensitively. Meanwhile, the in situ FTIR results uncovered the detection mechanism: the target molecules could block Ni-O-Co active sites at the heterostructure interface leading to the signal peak decreasing. This study not only provided a well design strategy for the further development of nanozymes or other advanced catalysts, but it also designed a multifunctional intelligent biosensor platform. Furthermore, it also provided preferable ideas regarding the catalytic mechanism and detection mechanism of heterostructure nanozymes.
Keywords: catalytic mechanism; heterostructure nanozymes; intelligent biosensor; interface engineering; multifunctional application.