Abstract：
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.
摘要：
气体传感器的一个挑战是湿度干扰，由于动态湿度条件可能导致对分析物的响应信号发生不可预测的波动，增加定量检测误差。这里,我们介绍一个概念：从水池中选择湿度传感器来补偿每个气体传感器的湿度信号。与极端抑制湿度响应的传统方法相比，传感器池允许更准确的气体定量在更广泛的应用场景提供定制,高维湿度响应数据作为外在补偿。作为一个概念证明,通过三个步骤实现了比色气体定量中湿度干扰的缓解。首先,跨越十维变量空间，一个算法驱动的高通量实验机器人发现了多个局部最佳区域，在这些区域中，比色湿度传感公式对灵敏度具有很高的评价，可逆性,响应时间,和在室温(25°C)下10-90%相对湿度(RH)的颜色变化程度。第二,从局部最优区域，选择具有不同变量的91种传感配方来构建母体比色湿度传感器阵列作为湿度信号补偿的传感器池。第三，准最佳传感器子阵列被确定为定制的湿度信号补偿解决方案，适用于大约全动态湿度范围(10-90%RH)的不同气体传感场景，使用巧妙的组合优化策略，并获得了两个准确的定量检测：一个平均绝对百分比误差（MAPE）从4.4％降低到0.75％，另一个从5.48％降低到1.37％。此外，父传感器阵列出色的湿度选择性已针对10种气体进行了验证。这项工作证明了机器人辅助构建可定制的父比色传感器阵列以减轻气体定量中的湿度干扰的可行性和优越性。
