Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, Zhejiang 310024, China.
Research Center for High Efficiency Computing System, Zhejiang Laboratory, Hangzhou, Zhejiang 311121, China.
ACS Sens. 2024 Aug 23;9(8):4143-4153. doi: 10.1021/acssensors.4c01083. Epub 2024 Aug 1.
One challenge for gas sensors is humidity interference, as dynamic humidity conditions can cause unpredictable fluctuations in the response signal to analytes, increasing quantitative detection errors. Here, we introduce a concept: Select humidity sensors from a pool to compensate for the humidity signal for each gas sensor. In contrast to traditional methods that extremely suppress the humidity response, the sensor pool allows for more accurate gas quantification across a broader range of application scenarios by supplying customized, high-dimensional humidity response data as extrinsic compensation. As a proof-of-concept, mitigation of humidity interference in colorimetric gas quantification was achieved in three steps. First, across a ten-dimensional variable space, an algorithm-driven high-throughput experimental robot discovered multiple local optimum regions where colorimetric humidity sensing formulations exhibited high evaluations on sensitivity, reversibility, response time, and color change extent for 10-90% relative humidity (RH) in room temperature (25 °C). Second, from the local optimum regions, 91 sensing formulations with diverse variables were selected to construct a parent colorimetric humidity sensor array as the sensor pool for humidity signal compensation. Third, the quasi-optimal sensor subarrays were identified as customized humidity signal compensation solutions for different gas sensing scenarios across an approximately full dynamic range of humidity (10-90% RH) using an ingenious combination optimization strategy, and two accurate quantitative detections were attained: one with a mean absolute percentage error (MAPE) reduction from 4.4 to 0.75% and the other from 5.48 to 1.37%. Moreover, the parent sensor array's excellent humidity selectivity was validated against 10 gases. This work demonstrates the feasibility and superiority of robot-assisted construction of a customizable parent colorimetric sensor array to mitigate humidity interference in gas quantification.
气体传感器面临的一个挑战是湿度干扰，因为动态湿度条件会导致对分析物的响应信号不可预测地波动，从而增加定量检测误差。在这里，我们引入了一个概念：从传感器池中选择湿度传感器来补偿每个气体传感器的湿度信号。与传统的极度抑制湿度响应的方法不同，传感器池通过提供定制的高维湿度响应数据作为外部补偿，可以在更广泛的应用场景中实现更准确的气体定量，从而提供更准确的气体定量。作为概念验证，通过三步实现了比色气体定量中的湿度干扰缓解。首先，在一个十维变量空间中，一个由算法驱动的高通量实验机器人发现了多个局部最优区域，在这些区域中，比色湿度传感配方在灵敏度、可逆性、响应时间和颜色变化程度方面对室温（25°C）下 10-90%的相对湿度（RH）表现出高度评价。其次，从局部最优区域中选择了 91 种具有不同变量的传感配方，构建了一个母比色湿度传感器阵列作为湿度信号补偿的传感器池。第三，使用巧妙的组合优化策略，从大约全湿度动态范围内（10-90% RH）的不同气体传感场景中确定了准最优传感器子阵作为定制湿度信号补偿解决方案，并实现了两种准确的定量检测：一种是平均绝对百分比误差（MAPE）从 4.4%降低到 0.75%，另一种是从 5.48%降低到 1.37%。此外，还验证了母传感器阵列对 10 种气体的优异湿度选择性。这项工作证明了机器人辅助构建可定制母比色传感器阵列以缓解气体定量中的湿度干扰的可行性和优越性。
