Rational Design of QCM‑D Virtual Sensor Arrays Based on Film Thickness, Viscoelasticity, and Harmonics for Vapor Discrimination

Herein, we demonstrate an alternative strategy for creating QCM-based sensor arrays by use of a single sensor to provide multiple responses per analyte. The sensor, which simulates a virtual sensor array (VSA), was developed by depositing a thin film of ionic liquid, either 1-octyl-3-methylimidazolium bromide ([OMIm]­[Br]) or 1-octyl-3-methylimidazolium thiocyanate ([OMIm]­[SCN]), onto the surface of a QCM-D transducer. The sensor was exposed to 18 different organic vapors (alcohols, hydrocarbons, chlorohydrocarbons, nitriles) belonging to the same or different homologous series. The resulting frequency shifts (Δ<i>f</i>) were measured at multiple harmonics and evaluated using principal component analysis (PCA) and discriminant analysis (DA) which revealed that analytes can be classified with extremely high accuracy. In almost all cases, the accuracy for identification of a member of the same class, that is, intraclass discrimination, was 100% as determined by use of quadratic discriminant analysis (QDA). Impressively, some VSAs allowed classification of all 18 analytes tested with nearly 100% accuracy. Such results underscore the importance of utilizing lesser exploited properties that influence signal transduction. Overall, these results demonstrate excellent potential of the virtual sensor array strategy for detection and discrimination of vapor phase analytes utilizing the QCM. To the best of our knowledge, this is the first report on QCM VSAs, as well as an experimental sensor array, that is based primarily on viscoelasticity, film thickness, and harmonics

Virtual Colorimetric Sensor Array: Single Ionic Liquid for Solvent Discrimination

There is a continuing need to develop high-performance sensors for monitoring organic solvents, primarily due to the environmental impact of such compounds. In this regard, colorimetric sensors have been a subject of intense research for such applications. Herein, we report a unique virtual colorimetric sensor array based on a single ionic liquid (IL) for accurate detection and identification of similar organic solvents and mixtures of such solvents. In this study, we employ eight alcohols and seven binary mixtures of ethanol and methanol as analytes to provide a stringent test for assessing the capabilities of this array. The UV–visible spectra of alcoholic solutions of the IL used in this study show two absorption bands. Interestingly, the ratio of absorbance for these two bands is found to be extremely sensitive to alcohol polarity. A virtual sensor array is created by using four different concentrations of IL sensor, which allowed identification of these analytes with 96.4–100% accuracy. Overall, this virtual sensor array is found to be very promising for discrimination of closely related organic solvents