首页|Zhejiang Laboratory Reports Findings in Robotics (On-Demand Op- timization of Colorimetric Gas Sensors Using a Knowledge-Aware Algorithm-Driven Robotic Experimental Platform)
Zhejiang Laboratory Reports Findings in Robotics (On-Demand Op- timization of Colorimetric Gas Sensors Using a Knowledge-Aware Algorithm-Driven Robotic Experimental Platform)
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2024 FEB 22 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Robotics is the subject of a report. According to news reporting originating in Zhejiang, People's Republic of China, by NewsRx journalists, research stated, "Synthesizing the best material globally is challenging; it needs to know what and how much the best ingredient composition should be for satisfying multiple figures of merit simultaneously. Traditional one-variable-at-a-time methods are inefficient; the design-build-test-learn (DBTL) method could achieve the optimal composition from only a handful of ingredients." The news reporters obtained a quote from the research from Zhejiang Laboratory, "A vast design space needs to be explored to discover the possible global optimal composition for on-demand materials synthesis. This research developed a hypothesis-guided DBTL (H-DBTL) method combined with robots to expand the dimensions of the search space, thereby achieving a better global optimal performance. First, this study engineered the search space with knowledge-aware chemical descriptors and customized multiobjective functions to fulfill on-demand research objectives. To verify this concept, this novel method was used to optimize colorimetric ammonia sensors across a vast design space of as high as 19 variables, achieving two remarkable optimization goals within 1 week: first, a sensing array was developed for ammonia quantification of a wide dynamic range, from 0.5 to 500 ppm; second, a new state-of-the-art detection limit of 50 ppb was reached."
ZhejiangPeople's Republic of ChinaAsiaAlgorithmsEmerging TechnologiesMachine LearningRoboticsRobots
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