Lu Jia-Min, Wang Hui-Feng, Guo Qi-Hang, Wang Jian-Wei, Li Tong-Tong, Chen Ke-Xin, Zhang Meng-Ting, Chen Jian-Bo, Shi Qian-Nuan, Huang Yi, Shi Shao-Wen, Chen Guang-Yong, Pan Jian-Zhang, Lu Zhan, Fang Qun
Department of Chemistry, Zhejiang University, Hangzhou, China.
Institute of Intelligent Chemical Manufacturing and iChemFoundry Platform, Engineering Research Center of Functional Materials Intelligent Manufacturing of Zhejiang Province, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, China.
Nat Commun. 2024 Oct 12;15(1):8826. doi: 10.1038/s41467-024-53204-6.
The current throughput of conventional organic chemical synthesis is usually a few experiments for each operator per day. We develop a robotic system for ultra-high-throughput chemical synthesis, online characterization, and large-scale condition screening of photocatalytic reactions, based on the liquid-core waveguide, microfluidic liquid-handling, and artificial intelligence techniques. The system is capable of performing automated reactant mixture preparation, changing, introduction, ultra-fast photocatalytic reactions in seconds, online spectroscopic detection of the reaction product, and screening of different reaction conditions. We apply the system in large-scale screening of 12,000 reaction conditions of a photocatalytic [2 + 2] cycloaddition reaction including multiple continuous and discrete variables, reaching an ultra-high throughput up to 10,000 reaction conditions per day. Based on the data, AI-assisted cross-substrate/photocatalyst prediction is conducted.
传统有机化学合成目前的通量通常是每位操作人员每天进行几次实验。我们基于液芯波导、微流控液体处理和人工智能技术,开发了一种用于光催化反应的超高通量化学合成、在线表征和大规模条件筛选的机器人系统。该系统能够自动制备、更换和引入反应物混合物,在几秒钟内进行超快速光催化反应,对反应产物进行在线光谱检测,并筛选不同的反应条件。我们将该系统应用于光催化[2+2]环加成反应12000个反应条件的大规模筛选,其中包括多个连续和离散变量,实现了每天高达10000个反应条件的超高通量。基于这些数据,进行了人工智能辅助的交叉底物/光催化剂预测。