Minami Kosuke, Yoshikawa Genki
Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan.
International Center for Young Scientists (ICYS), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan.
Anal Chem. 2025 Sep 9;97(35):19306-19312. doi: 10.1021/acs.analchem.5c03397. Epub 2025 Aug 27.
Nanomechanical sensors and their arrays have attracted significant attention for detecting, distinguishing, and identifying target analytes, especially complex mixtures of odors. In the static mode operation, sensing signals are obtained by a concentration-dependent sorption-induced mechanical strain/stress. Understanding of the dynamic responses is crucial for developing practical artificial olfaction; however, the analytical formulations are still limited to single-component analytes. Here, we derive an analytical model of viscoelastic material-based static mode nanomechanical sensing for multicomponent analytes by extending the theoretical model via solving differential equations. The present model can reduce the dynamic responses to the multicomponent target analytes observed in the experimental signal responses. Moreover, the use of optimized fitting parameters extracted from pure vapors with viscoelastic parameters allows us to predict the concentration of each analyte in the multicomponent system.
纳米机械传感器及其阵列在检测、区分和识别目标分析物,特别是复杂气味混合物方面引起了广泛关注。在静态模式操作中,传感信号是通过浓度依赖性吸附诱导的机械应变/应力获得的。理解动态响应对于开发实用的人工嗅觉至关重要;然而,分析公式仍然仅限于单组分分析物。在这里,我们通过求解微分方程扩展理论模型,推导出了基于粘弹性材料的多组分分析物静态模式纳米机械传感的分析模型。本模型可以减少实验信号响应中观察到的对多组分目标分析物的动态响应。此外,使用从具有粘弹性参数的纯蒸汽中提取的优化拟合参数,使我们能够预测多组分系统中每种分析物的浓度。