Adaptation and evaluation of a personal electronic nose for selective multivapor analysis

Functional Nanoparticles-Coated Nanomechanical Sensor Arrays for Machine Learning-Based Quantitative Odor Analysis

A sensing signal obtained by measuring an odor usually contains varied information that reflects an origin of the odor itself, while an effective approach is required to reasonably analyze informative data to derive the desired information. Herein, we demonstrate that quantitative odor analysis was achieved through systematic material design-based nanomechanical sensing combined with machine learning. A ternary mixture consisting of water, ethanol, and methanol was selected as a model system where a target molecule coexists with structurally similar species in a humidified condition. To predict the concentration of each species in the system via the data-driven approach, six types of nanoparticles functionalized with hydroxyl, aminopropyl, phenyl, and/or octadecyl groups were synthesized as a receptor coating of a nanomechanical sensor. Then, a machine learning model based on Gaussian process regression was trained with sensing data sets obtained from the samples with diverse concentrations. As a result, the octadecyl-modified nanoparticles enhanced prediction accuracy for water while the use of both octadecyl and aminopropyl groups was indicated to be a key for a better prediction accuracy for ethanol and methanol. As the prediction accuracy for ethanol and methanol was improved by introducing two additional nanoparticles with finely controlled octadecyl and aminopropyl amount, the feedback obtained by the present machine learning was effectively utilized to optimize material design for better performance. We demonstrate through this study that various information which was extracted from plenty of experimental data sets was successfully combined with our knowledge to produce wisdom for addressing a critical issue in gas phase sensing.

Chamber evaluation of a portable GC with tunable retention and microsensor-array detection for indoor air quality monitoring

The evaluation of a novel prototype instrument designed for on-site determinations of VOC mixtures found in indoor working environments is described. The instrument contains a miniature multi-stage preconcentrator, a dual-column separation module with pressure-tunable retention capabilities, and an integrated array of three polymer-coated surface acoustic wave sensors. It was challenged with dynamic test-atmospheres of a set of 15 common indoor air contaminants at parts-per-billion concentrations within a stainless-steel chamber under a range of conditions. Vapours were reliably identified at a known level of confidence by combining column retention times with sensor-array response patterns and applying a multivariate statistical test of pattern fidelity for the chromatographically resolved vapours. Estimates of vapour concentrations fell within 7% on average of those determined by EPA Method TO-17, and limits of detection ranged from 0.2 to 28 ppb at 25 degrees C for 1 L samples collected and analyzed in <12 min. No significant humidity effects were observed (0-90% RH). Increasing the chamber temperature from 25 to 30 degrees C reduced the retention times of the more volatile analytes which, in turn, demanded alterations in the scheduling of column-junction-point pressure (flow) modulations performed during the analysis. Reductions in sensor sensitivities with increasing temperature were predictable and similar among the sensors in the array such that most response patterns were not altered significantly. Short-term fluctuations in concentration were accurately tracked by the instrument. Results indicate that this type of instrument could provide routine, semi-autonomous, near-real-time, multi-vapour monitoring in support of efforts to assess air quality in office environments.

Surface acoustic wave (SAW) microsensor array for measuring VOCs in drinking water

Exposure to volatile organic chemicals (VOCs) in drinking water has been linked to a number of adverse health effects including cancer, liver, and kidney damage. However, the large number of potential contaminants and the cost and complexity of existing analytical methods limits the extent to which water quality is routinely characterized. This project focused on the laboratory development and evaluation of an instrument for field analysis of VOCs in drinking water. The instrument is based on an array of six polymer-coated surface-acoustic-wave microsensors. A test-set consisting of dichloromethane, chloroform, 1,1,1-trichloroethane, perchloroethylene, and m-xylene was used in a series of experiments designed to optimize the purge-trap preconcentration system, calibrate the instrument over the concentration range of 0.2-2 times the USEPA maximum contaminant levels (MCLs), and compare results to those of a reference laboratory. The primary goal was to develop a cost-effective alternative for on-site evaluation of VOCs in water. Calibration and evaluation test results for spiked water samples demonstrate adequate sensitivity for 19 of the 21 regulated VOCs considered using a ten minute sampling and analysis cycle. Monte Carlo simulations characterized the performance of trained artificial neural networks (ANNs) which had correct classification rates of 99%, 90%, and 80% for the five individual test-set vapors and their binary and ternary mixtures, respectively. These results demonstrate the excellent potential of this technology for addressing the need for improved VOC field-screening methods for water supplies.

First-generation hybrid MEMS gas chromatograph

The fabrication, assembly, and initial testing of a hybrid microfabricated gas chromatograph (microGC) is described. The microGC incorporates capabilities for on-board calibration, sample preconcentration and focused thermal desorption, temperature-programmed separations, and "spectral" detection with an integrated array of microsensors, and is designed for rapid determinations of complex mixtures of environmental contaminants at trace concentrations. Ambient air is used as the carrier gas to avoid the need for on-board gas supplies. The microsystem is plumbed through an etched-Si/glass microfluidic interconnection substrate with fused silica capillaries and employs a miniature commercial pump and valve subsystem for directing sample flow. The latest performance data on each system component are presented followed by first analytical results from the working microsystem. Tradeoffs in system performance as a function of volumetric flow rate are explored. The determination of an 11-vapor mixture of typical indoor air contaminants in less than 90 s is demonstrated with projected detection limits in the low part-per-billion concentration range for a preconcentrated air-sample volume of 0.25 L.

Limits of recognition for simple vapor mixtures determined with a microsensor array

The "limit of recognition" (LOR) has been defined as the minimum concentration at which reliable individual vapor recognition can be achieved with a multisensor array, and methodology for determining the LORs of individual vapors probabilistically on the basis of sensor array response patterns has been reported. This article explores the problems of defining and evaluating LORs for vapor mixtures in terms of the absolute and relative component vapor concentrations, where the mixture must be discriminated from those component vapors and from the subset of possible lower-order component mixtures. Monte Carlo simulations and principal components regression analyses of an extant database of calibrated responses to a set of 16 vapors from an array of 6 diverse polymer-coated surface acoustic wave sensors are used to illustrate the approach and to examine trends in LOR values among the 120 possible binary mixtures and 560 possible ternary mixtures in the data set. At concentrations exceeding the LOD, 89% of the binary mixtures could be reliably recognized (<5% error) over some composite concentration range, while only 3% of the ternary mixtures could be recognized. Most binary mixtures could be recognized only if the constituent vapor relative concentration ratio, defined in terms of multiples of the LOD for each vapor, was < or =20. Correlations with the Euclidean distance(s) separating the normalized constituent vapor response vectors allow reasonably accurate predictions of the limiting recognizable mixture composition ranges for binary and ternary cases. Results are considered in the context of using microsensor arrays for vapor detection and recognition in microanalytical systems.

Laboratory and field evaluation of a SAW microsensor array for measuring perchloroethylene in breath

This article describes the laboratory and field performance evaluation of a small prototype instrument employing an array of six polymer-coated surface acoustic wave (SAW) sensors and a thermal desorption preconcentration unit for rapid analysis of perchloroethylene in breath. Laboratory calibrations were performed using breath samples spiked with perchloroethylene to prepare calibration standards spanning a concentration range of 0.1-10 ppm. A sample volume of 250 mL was preconcentrated on 40 mg of Tenax GR at a flow rate of 100 mL/min, followed by a dry air purge and thermal desorption at a temperature of 200 degrees C. The resulting pulse of vapor was passed over the sensor array at a flow rate of 20 mL/min and sensor responses were recorded and displayed using a laptop computer. The total time per analysis was 4.5 min. SAW sensor responses were linear, and the instrument's limit of detection was estimated to be 50 ppb based on the criterion that four of the six sensors show a detectable response. Field performance was evaluated at a commercial dry-cleaning operation by comparing prototype instrument results for breath samples with those of a portable gas chromatograph (NIOSH 3704). Four breath samples were collected from a single subject over the course of the workday and analyzed using the portable gas chromatograph (GC) and SAW instruments. An additional seven spiked breath samples were prepared and analyzed so that a broader range of perchloroethylene concentrations could be examined. Linear regression analysis showed excellent agreement between prototype instrument and portable GC breath sample results with a correlation coefficient of 0.99 and a slope of 1.04. The average error for the prototype instrument over a perchloroethylene breath concentration range of 0.9-7.2 ppm was 2.6% relative to the portable GC. These results demonstrate the field capabilities of SAW microsensor arrays for rapid analysis of organic vapors in breath.