Abstract: The enzymatic determination of glucose concentration represents a fundamental experiment in undergraduate chemistry laboratory education. The method operates on the principle of cascade enzymatic catalysis for glucose oxidation and chromogenic reaction, coupled with spectrophotometric quantification. However, current challenges in quantitative analysis—including the demand for rapid, convenient, and intelligent detection—reveal limitations in this conventional approach, particularly its high enzyme costs and inadequate integration of intelligent technologies, which fall short of meeting the cutting-edge requirements of higher education and digital literacy cultivation. To address these issues, this study developed an improved system employing microwave-assisted synthesized Fe-MOF nanozymes as substitutes for horseradish peroxidase (HRP), establishing a robust cascade catalytic system. Furthermore, we incorporated computer vision and convolutional neural network (CNN)-based image recognition technologies to develop intelligent methods for particle size characterization and concentration detection, thereby creating an interdisciplinary glucose detection platform. This improved experiment synergizes knowledge from materials synthesis, analytical chemistry, and artificial intelligence. By incorporating MOFs materials (related to the 2025 Nobel Prize in Chemistry) to stimulate student engagement, while simultaneously streamlining experimental procedures and reducing costs, this approach offers a practical pedagogical framework for cultivating interdisciplinary talent in emerging engineering education.

Key words: Fe-MOF, Microwave-assisted synthesis, Glucose concentration determination, Intelligent detection