发表机构
National Institute for Materials Science (NIMS); University of Tsukuba（物质材料研究机构; 筑波大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种物理约束神经网络，通过同时估算气体浓度和重建传感器信号，实现单次测量的自验证时间分辨多组分气体分析，并在十二通道传感器阵列上验证了其可靠性。
AI 中文摘要
气体传感器阵列可以根据其响应模式估算气体混合物的组成。然而，当传感器在实验室外使用时，真实气体浓度是未知的，因此通常没有直接方法来确定估算结果是否可靠。在此，我们开发了一种物理约束神经网络，该网络同时估算随时间变化的气体浓度并重建相应的传感器信号。我们使用一个十二通道膜式表面应力传感器阵列，暴露于浓度随时间变化的水-乙醇混合物中，对该方法进行了测试。该模型从原始信号中重建了两种组分的浓度分布。通过测量信号与重建信号之间的不一致性，检测到了单个通道中的人工偏移和灵敏度变化。通过违反吸附和力学方程，检测到了部分测量记录的删除或替换。这种方法使得单次测量即可实现具有自验证功能的时间分辨多组分气体分析。
英文摘要
Gas sensor arrays can estimate the composition of gas mixtures from their response patterns. However, when a sensor is used outside the laboratory, the true gas concentrations are unknown, so there is usually no direct way to determine whether the estimated result is reliable. Here, we develop a physics-constrained neural network that estimates time-varying gas concentrations and reconstructs the corresponding sensor signals at the same time. We tested the method with a twelve-channel membrane-type surface stress sensor array exposed to water-ethanol mixtures whose concentrations changed over time. The model reconstructed the concentration profiles of both components from the raw signals. Artificial offsets and sensitivity changes in one channel were detected from disagreement between the measured and reconstructed signals. Deletion or replacement of part of a measurement record was detected from violations of the sorption and mechanical equations. This approach enables time-resolved multicomponent gas analysis with self-verification from a single measurement.
Comments18 pages, 5 figures; 30 pages supplementary material,
