大学化学 >> 2026, Vol. 41 >> Issue (1): 1-8.doi: 10.12461/PKU.DXHX202505049

所属专题： 化学实验数字化设计竞赛获奖作品

专题 上一篇    下一篇

自驱动固体酸催化乙酸苄酯的合成

牛昊蕃, 吴郁菡, 李欣燃, 李龙妹, 王栋, 张永策, 刘凤玉, 白伟   

  1. 大连理工大学化学学院, 辽宁 大连 116024
  • 收稿日期:2025-05-18 录用日期:2025-07-24 发布日期:2025-12-30
  • 通讯作者: 张永策, 刘凤玉, 白伟 E-mail:yongcezhang@dlut.edu.cn;liufengyu@dlut.edu.cn;baiwei@dlut.edu.cn Yongce Zhang, Fengyu Liu, Wei Bai
  • 基金资助:
    大连理工大学教育教学改革基金项目

Self-Driving Solid Acid Catalyzed Synthesis of Benzyl Acetate

Haofan Niu, Yuhan Wu, Xinran Li, Longmei Li, Dong Wang, Yongce Zhang, Fengyu Liu, Wei Bai   

  1. School of Chemistry, Dalian University of Technology, Dalian 116024, Liaoning Province, China
  • Received:2025-05-18 Accepted:2025-07-24 Published:2025-12-30
  • Contact: Yongce Zhang, Fengyu Liu, Wei Bai E-mail:yongcezhang@dlut.edu.cn;liufengyu@dlut.edu.cn;baiwei@dlut.edu.cn

PDF

474

赞

摘要： 随着人工智能(AI)技术的迅猛发展，AI for Science (AI4S)正逐渐成为科学研究的新范式，尤其在化学领域的实验方法上引发了一场革命。本研究创新性地引入了自驱动实验室(SDL)的概念，旨在解决未来化学研究中的挑战，并探索其在基础化学实验中的AI数字化升级应用。研究团队采用了可循环利用的固体酸作为催化剂，以乙酸和苄醇的酯化反应为研究对象，开发了一种相对低成本、适用于教学的反应条件优化闭环系统。该系统由基于芯曙光自动合成仪的自动化反应器、自主设计的液相自动进样监控系统，以及基于Python的决策优化算法所组成。本研究展示了在现有条件下如何克服硬件兼容性和算法优化的挑战，为化学实验的未来创新发展奠定了坚实的基础。

关键词: 数字化, 全自动, 固体酸催化, 酯化反应

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