Robot-accelerated development of a colorimetric CO2 sensing array with wide ranges and high sensitivity via multi-target Bayesian optimizations

The one-variable-at-a-time method for sensor R&D has received extensive research effort, yet it has reached the local maxima for specific sensing characteristics. To achieve the quasi-global maxima for suitable sensors, we developed the Design-Build-Test-Machine learning (DBTM) method for efficiently developing sensors on de-mand. In addition, the automation of the preparation and characterization processes frees researchers from labor-intensive work, generates adequate high-quality data, and enables researchers to discover valuable information in high-dimensional space. As a proof-of-concept, we built a high-throughput algorithm-driven autonomous system (HAAS) that supports the DBTM approach for developing a CO2 sensor. With the DBTM approach, we can simultaneously optimize multiple sensor units, each for a specific concentration interval. Therefore, such array can achieve an extensive range and sound sensitivity. Our work demonstrates the superiority of the DBTM method for multi-target and multi-variable sensor development. In contrast to single target optimization in other research areas, multiple characteristics should be improved for sensors. Our multi-target optimization algorithm optimizes four sensor characteristics. Our sensor array could rapidly detect CO2 concentrations from 400 ppm 30 % with a root mean square error (RMSE) of 0.27 %. The DBTM method is anticipated to be a new paradigm and accelerator of practical application for sensors after the initial proof of the sensing mechanism.