An Analytical Model of Sorption-Induced Static Mode Nanomechanical Sensing for Multi-Component Analytes

TL;DR

This paper develops an analytical model for static mode nanomechanical sensors capable of detecting and quantifying multiple analytes in complex mixtures, advancing artificial olfaction technology.

Contribution

It extends existing models to multi-component analytes, enabling prediction of concentrations using viscoelastic parameters derived from pure vapors.

Findings

Model accurately predicts multi-component analyte responses.

Allows quantification of individual analytes in mixtures.

Extends single-component models to complex mixtures.

Abstract

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 concen-tration-dependent sorption-induced mechanical strain/stress. Understanding of the dynamic responses is crucial for develop-ing 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 multi-component analytes by extending the theoretical model via solving differential equations. The present model can reduce the dynamic responses to the multi-component 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 multi-component system.