Robotic-based Experimental Procedure for Colorimetric Gas Sensing Development.

This paper presents a robot-based experimental program aimed at developing an efficient and fast colorimetric gas sensor. The program employs an automated Design-Build-Test-learning (DBTL) approach, which optimizes the search process iteratively while optimizing multiple recipes for different concentration intervals of the gas. In each iteration, the algorithm generates a batch of recipe suggestions based on various acquisition functions, and with the increase in the number of iterations, the values of weighted objective function for each concentration interval significantly improve. The DBTL method begins with parameter initialization, setting up the hardware and software environment. Baseline tests establish performance standards. Subsequently, the DBTL method designs the following round of optimization based on the proportion of recipes in each round and tests performance iteratively. Performance evaluation compares baseline data to assess the effectiveness of the DBTL method. If the performance improvement does not meet expectations, the method will be performed iteratively; if the objectives are achieved, the experiment concludes. The entire process maximizes system performance through the DBTL iterative optimization process. Compared to the traditional manual developing process, the DBTL method adopted by this experimental process uses multi-objective optimization and various machine learning algorithms. After defining the upper and lower limits of component volume, the DBTL method dynamically optimizes iterative experiments to obtain the optimal ratio with the best performance. This method greatly improves efficiency, reduces costs, and performs more efficiently within the multi-formulation variable space when finding the optimal recipe.