National Institute of Advanced Industrial Science and Technology (AIST), 2266-98 Anagahora, Shimoshidami, Moriyama, Nagoya 463-8560, Japan.
Department of Chemical Systems Engineering, Graduate School of Engineering, Nagoya University, Nagoya 464-8603, Japan.
ACS Sens. 2022 Jan 28;7(1):142-150. doi: 10.1021/acssensors.1c01864. Epub 2021 Dec 16.
Through the improvement of nanomaterial technologies, a gas sensor was developed for detecting ppm or ppb levels of gas. Our SnO nanosheet gas sensor can detect 50 ppb of acetone without the requirement of a novel metal catalyst by exposing the (101) facet containing the Sn state. Despite the high performance, the fluctuation of the gas response value based on operating conditions, even at the same concentration, is a critical problem in gas sensors. Thus, the alarm criteria of the sensor are typically determined by a safety factor. However, this method is not suitable for application in ultrasensitive sensors that require distinguishing minute differences in extremely low concentrations for medical examination or odor analysis. Therefore, we suggest a self-adaptive system that is based on operating conditions in collaboration with the data prediction model. The sensor system is based on a predictive model obtained by the response surface methodology. When the system detects a change in conditions, the alarm criteria are changed appropriately through the calculated values from the predictive model. To prepare a database for an effective predictive model, the gas responses of the SnO nanosheet sensor were measured with 20 treatments with 3 independent variables, namely, the temperature, flow rate, and concentration. Our prediction model achieved its best performance on training data with = 0.9299 and less than 5% error in the prediction of unseen data.
通过改进纳米材料技术，开发出了一种用于检测 ppm 或 ppb 级气体的气体传感器。我们的 SnO 纳米片气体传感器可以在不使用新型金属催化剂的情况下通过暴露含有 Sn 状态的（101）面来检测 50ppb 的丙酮。尽管性能很高，但基于操作条件的气体响应值波动，即使在相同浓度下，也是气体传感器中的一个关键问题。因此，传感器的报警标准通常由安全系数决定。然而，这种方法不适用于需要区分极低浓度下微小差异的超灵敏传感器，例如医疗检查或气味分析。因此，我们建议使用基于操作条件的自适应系统与数据预测模型相结合。传感器系统基于通过响应面方法获得的预测模型。当系统检测到条件发生变化时，通过预测模型的计算值适当改变报警标准。为了为有效的预测模型准备数据库，使用 SnO 纳米片传感器对 20 种具有 3 个独立变量（温度、流速和浓度）的处理方法进行了气体响应测量。我们的预测模型在训练数据上的表现最佳， = 0.9299，对未见数据的预测误差小于 5%。
