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Robot-assisted optimized array design for accurate multi-component gas quantification
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AI
DOI：10.1016/j.cej.2024.154225.png)
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Designing sensor arrays is a common strategy for detecting mixtures. However, a sensor array designed based on human experience leads to inaccuracies due to cross-sensitivity and information overlap. This difficulty led to the realization that arrays should be optimized as a whole. The conventional array optimizing method involves selecting the best subarray from a limited sensor pool. However, this method did not consider the vast continuous multi-dimensional variable space of each sensing element's recipe, thus only generating a simpler array with limited detection capacity. To address this problem, we developed a Robot-assisted Optimized Array Design (ROAD) method. This innovative method holistically optimizes the sensor array across a continuous and vast variable space, overcoming the bottlenecks of high dimensionality and high-quality data collection. We applied ROAD to an optoelectronic nose, tasked with detecting a mixture of CO2, 2 , NH3, 3 , and water vapor. The method explored an immense space, estimated at the order of 2<^>(10<^>7). The implementation of ROAD led to a revolutionary quantitative accuracy, achieving an average relative standard deviation of 1.90%. This advancement has propelled the application of optoelectronic nose from qualitative to quantitative. The successful ROAD of systemwide array optimization has been demonstrated in the optoelectronic nose, validating the potential of the holistically optimized sensor array for accurately quantifying each component in a gas mixture.
Keyword:
Mixed gas quantification
Array optimization
Self-Driving Labs
Optoelectronic nose
期刊
IF:
13.2
论文数:
7.4W
被引数:
48.5W
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