Abstract: The rapid advancement of artificial intelligence (AI) technology has established AI for Science (AI4S) as an emerging paradigm in scientific research, particularly revolutionizing experimental methodologies in chemistry. This study innovatively introduces the concept of Self-Driving Laboratories (SDL) to address future challenges in chemical research and explore its application in AI-driven digital transformation of fundamental chemical experiments. Employing a recyclable solid acid catalyst for the esterification reaction between acetic acid and benzyl alcohol, we developed a cost-effective, education-oriented closed-loop system for reaction condition optimization. The integrated system comprises an automated reactor based on the Neodawn automatic synthesizer, a custom-designed liquid-phase automatic sampling and monitoring system, and a Python-based decision optimization algorithm. This work successfully overcomes existing challenges in hardware compatibility and algorithm optimization, providing a robust foundation for future innovations in chemical experimentation.

Key words: Digitization, Fully automatic, Solid acid catalysis, Esterification reaction