RESUMO
The automation of organic compound synthesis is pivotal for expediting the development of such compounds. In addition, enhancing development efficiency can be achieved by incorporating autonomous functions alongside automation. To achieve this, we developed an autonomous synthesis robot that harnesses the power of artificial intelligence (AI) and robotic technology to establish optimal synthetic recipes. Given a target molecule, our AI initially plans synthetic pathways and defines reaction conditions. It then iteratively refines these plans using feedback from the experimental robot, gradually optimizing the recipe. The system performance was validated by successfully determining synthetic recipes for three organic compounds, yielding that conversion rates that outperform existing references. Notably, this autonomous system is designed around batch reactors, making it accessible and valuable to chemists in standard laboratory settings, thereby streamlining research endeavors.
RESUMO
This paper presents the concept of a new rubber compression mechanism for rotary compact series elastic elements, which can be used in robotic joints in human-machine interaction devices. A compact elastic element is realized using rubber materials with a higher specific energy and energy density than steel or fiberglass, which are commonly used in commercial springs. To overcome the nonlinearity and hysteresis of the proposed compression mechanism, a mathematical Bouc-Wen model developed through parameter identification is proposed. This paper describes the working principle of the new elastic element and presents the modeling method. Experiments on the Bouc-Wen model conducted to demonstrate the suggested mechanism are reported, and the results show that the proposed elastic element can be used for robotic joints in human-machine interaction devices.