Miao Lei, Song Peng, Xue Yibei, Hou Zhufeng, Hasegawa Takuya, Okawa Ayahisa, Goto Tomoyo, Seo Yeongjun, Maezono Ryo, Sekino Tohru, Yin Shu
Institute of Multidisciplinary Research for Advanced Materials (IMRAM), Tohoku University, Sendai, 980-8577, Japan.
School of Information Science, JAIST, Ishikawa, 923-1292, Japan.
Adv Mater. 2025 Feb;37(7):e2413023. doi: 10.1002/adma.202413023. Epub 2024 Dec 19.
Traditional selectivity of gas sensors determined by the magnitude of the response value has significant limitations. The distinctive inversion sensing behavior not only defies the traditional sensing theory but also provides insight into defining selectivity. Herein, the novel definition of selectivity is established in a study with VO(M1). The sensing behavior of VO(M1) is investigated after its synthesis conditions optimization by machine learning. In gases of the same nature, VO(M1) shows remarkably selective for NH marked with unique resistance increase behavior. Such anomalous behavior is attributed to the formation of the Schottky junction between VO(M1) and the electrode. The "work function-electron affinity" relation is summarized as the selectivity coefficient, a parameter for predicting the selectivity of the sensing material effectively.
由响应值大小决定的传统气体传感器选择性存在显著局限性。这种独特的反向传感行为不仅违背了传统传感理论，还为定义选择性提供了思路。在此，通过对VO(M1)的研究建立了选择性的新定义。通过机器学习优化VO(M1)的合成条件后，对其传感行为进行了研究。在性质相同的气体中，VO(M1)对NH表现出显著的选择性，具有独特的电阻增加行为。这种异常行为归因于VO(M1)与电极之间形成的肖特基结。“功函数-电子亲和势”关系被总结为选择性系数，这是一个有效预测传感材料选择性的参数。
