Although the application of nanozymes has been widely studied, it is still a huge challenge to develop highly active and multifunctional nanozyme catalysts with a wider application prospect. Co3O4/CoFe2O4 hollow nanocubes (HNCs) with oxygen vacancies were proposed in this study, which had a porous oxide heterostructure with CoFe2O4 as the core and Co3O4 as the shell. The Co3O4/CoFe2O4 HNCs had three enzyme activities: peroxidase-like, oxidase-like, and catalase-like. Combining XPS depth profiling with density functional theory (DFT), the catalytic mechanism of peroxidase-like activity was explored in depth, which was mainly originated from ·OH produced by the synergistic effect between the outer oxygen and inner oxygen and electron transfer between Co and Fe. A colorimetry/smartphone dual sensing platform was designed based on the peroxidase-like activity. Especially, a multifunctional intelligent sensing platform based on deep learning-YOLO v3 algorithm-assisted smartphone was constructed to realize real-time and rapid in situ detection of l-cysteine, norfloxacin, and zearalenone. Surprisingly, the detection limit of norfloxacin was low at 0.015 μM, which was better than that of the newly published detection method in the field of nanozymes. Meanwhile, the detection mechanism of l-cysteine and norfloxacin was successfully investigated by in situ FTIR. In fact, it also showed outstanding applications in detecting l-cysteine in the food environment and norfloxacin in drugs. Furthermore, Co3O4/CoFe2O4 HNCs also could degrade 99.24% of rhodamine B, along with good reusability even after 10-cycle runs. Therefore, this work provided an in-depth understanding of the synergistic effect between the outer and inner oxygen in the reaction mechanism and an efficient method for establishing a deep-learning-assisted intelligent detection platform. In addition, this research also offered a good guideline for the further development and construction of nanozyme catalysts with multienzyme activities and multifunctional applications.
Keywords: deep learning; nanozyme; oxygen vacancy; smartphone; synergistic effect.