The parallel experiment and intelligent control technology of catalyst synthesis workstation have entered a new era of efficient and precise development of positive catalysts. Parallel experimental technology enables simultaneous testing of dozens to hundreds of catalyst formulations through a multi-channel reaction system, significantly accelerating the screening process. For example, in the development of platinum based catalysts for fuel cells, workstations can test hundreds of carrier platinum ratio combinations in parallel, quickly identifying the optimal solution that balances activity and cost, and shortening the research and development cycle by more than 60%. The wet chemistry parallel synthesis technology developed by the team of Anhui University has broken through 100 units, combined with sol gel, hydrothermal solvothermal and other processes, to solve the problems of low flux of traditional equipment and uneven powder synthesis, and realize the technological leap from laboratory to industrialization.
Intelligent control technology integrates machine learning algorithms with high-precision sensors to achieve dynamic optimization of reaction parameters. The robotic arm system equipped on the Oushisheng fully automatic workstation can automatically complete operations such as solid powder weighing, liquid pipetting, and reaction vessel transfer. Combined with online weighing and pH monitoring modules, it reduces human error to a minimum. More importantly, its built-in decision tree model can predict the optimal synthesis path based on historical data. For example, in MOF morphology control, by analyzing the influence of H ₂ O/HCOOH concentration on crystal structure, it guides the synthesis of cubic nMOFs with a 4-fold increase in olefin hydrogenation activity. The AI-EDISON platform of Dalian Institute of Chemical Physics, Chinese Academy of Sciences, went further, and successfully prepared monodisperse gold nanostructures through intensive learning and regulation of reaction parameters. The dispersion index (PDI) of spherical, rod shaped and star shaped nanoparticles were all lower than 0.1.
This collaborative model of "parallel experiments+intelligent control" not only improves the efficiency of catalyst research and development by several orders of magnitude, but also promotes the transformation of materials science towards a data-driven paradigm. In the future, with the deep integration of digital twin technology and cross modal databases, catalyst synthesis workstations are expected to achieve full chain intelligence from atomic level structure prediction to large-scale production.