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 (Δf) 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.

A miniaturized, low-cost lens tube based laser-induced fluorescence detection system for automated microfluidic analysis of primary amines.

In situ analyses are essential to ascertain potential past or present habitability in celestial bodies. One technique that provides the sensitivity and miniaturization needed to successfully detect trace organics in the outer Solar System is laser-induced fluorescence (LIF) detection, which, when coupled with microfluidic systems, provides a powerful wet chemistry platform that can meet the size and resource consumption constraints of a remote analysis mission. Herein, a portable LIF detection module (44-mm long, 18-mm wide) was prototyped and utilized to quantify bulk organics in a liquid sample via manual and automated analysis utilizing a programmable microfluidic architecture. The experimental limit of detection (LOD) for primary amines was 11.8 µM. A sample (Y31B) collected from the Atacama Desert in Yungay, Chile, was analyzed manually and found to contain 300 ± 50 µM of bulk primary amine organics, while the automated microfluidic protocol found the sample to contain 289 ± 4 µM of primary amines. Automated analyses showed no statistically significant differences when compared to the manual analyses (t-test, C.I. 95%). Our results demonstrate that the coupling of programmable microfluidic devices with a custom lens tube-based LIF detector enables automated analysis of primary amines using a protocol appropriate for remote analyses. This technique is an invaluable tool for in situ analysis applications in distant, resource-restricted environments.

A modular, easy-to-use microcapillary electrophoresis system with laser-induced fluorescence for quantitative compositional analysis of trace organic molecules.

Microcapillary electrophoresis (µCE) enables high-resolution separations in miniaturized, automated microfluidic devices. Pairing this powerful separation technique with laser-induced fluorescence (LIF) enables a highly sensitive, quantitative, and compositional analysis of organic molecule monomers and short polymers, which are essential, ubiquitous components of life on Earth. Improving methods for their detection has applications to multiple scientific fields, particularly those related to medicine, industry, and space science. Here, a modular benchtop system using µCE with LIF detection was constructed and tested by analyzing standard amino acid samples of valine, serine, alanine, glycine, glutamic acid, and aspartic acid in multiple borate buffered solutions of increasing concentrations from 10 mM to 50 mM, all pH 9.5. The 35 mM borate buffer solution generated the highest resolution before Joule heating dominated. The limits of detection of alanine and glycine using 35 mM borate buffer were found to be 2.12 nM and 2.91 nM, respectively, comparable to other state-of-the-art µCE-LIF instruments. This benchtop system is amenable to a variety of detectors, including a photomultiplier tube, a silicon photomultiplier, or a spectrometer, and currently employs a spectrometer for facile multi-wavelength detection. Furthermore, the microdevice is easily exchanged to fit the desired application of the system, and optical components within the central filter cube can be easily replaced to target alternative fluorescent dyes. This work represents a significant step forward for the analysis of small organic molecules and biopolymers using µCE-LIF systems.

Class specific discrimination of volatile organic compounds using a quartz crystal microbalance based multisensor array.

The use of quartz crystal microbalance (QCM) sensor arrays for analyses of volatile organic compounds (VOC) has attracted significant interest in recent years. In this regard, a group of uniformed materials based on organic salts (GUMBOS) has proven to be promising recognition elements in QCM based sensor arrays due to diverse properties afforded by this class of tunable materials. Herein, we examine the application of four novel phthalocyanine based GUMBOS as recognition elements for VOC sensing using a QCM based multisensor array (MSA). These synthesized GUMBOS are composed of copper (II) phthalocyaninetetrasulfonate (CuPcS4) anions coupled with ammonium or phosphonium cations respectively (tetrabutylammonium (TBA), tetrabutylphosphonium (P4444), 3-(dodecyldimethyl-ammonio)propanesulfonate (DDMA), and tributyl-n-octylphosphonium (P4448)). These materials were characterized using ESI-MS and FTIR, while thermal properties were investigated using TGA. Vapor sensing properties of these GUMBOS towards a set of common VOCs at three sample flow rate ratios were examined. Upon exposure to VOCs, each sensor generated analyte specific response patterns that were recorded and analyzed using principal component and discriminant analyses. Use of this MSA allowed discrimination of analytes into different functional group classes (alcohols, chlorohydrocarbons, aromatic hydrocarbons, and hydrocarbons) with 98.6% accuracy. Evaluation of these results provides further insight into the use of phthalocyanine GUMBOS as recognition elements for QCM-based MSAs for VOC discrimination.