Integrating Computer Vision into the SiO2 Precipitation Synthesis Experiment: Digital Innovation Enhancing Chemistry Education

Artificial intelligence (AI) has become a transformative tool, reshaping research methodologies and expanding our understanding of complex systems. The integration of AI into chemical education offers exciting opportunities to innovate traditional laboratory experiments. This study introduces computer vision (CV), a key branch of AI, into the classic precipitation experiment for synthesizing SiO₂. By using CV to precisely detect color changes in an acid–base indicator, the experiment automates the alternate addition of reagents (sodium silicate solution and sulfuric acid), eliminating the variability associated with manual operations and enhancing the product quality. The experimental platform is developed using open-source Python code, enabling computer-based CV recognition and control and also integrates a freely accessible Android application for performing the experiment via smartphone-based control. Cost-effective and easy to implement, this experiment can be seamlessly integrated into undergraduate chemistry and related laboratory courses. Students not only assemble the CV apparatus but also actively engage in the AI-assisted synthesis process. This hands-on experience enables them to witness firsthand how AI can be applied in chemical experiments, fostering their interdisciplinary thinking and expanding their understanding of both chemistry and technology.