Bulk and Surface Acoustic Wave Sensor Arrays for Multi-Analyte Detection: A Review

Bulk acoustic wave (BAW) and surface acoustic wave (SAW) sensor devices have successfully been used in a wide variety of gas sensing, liquid sensing, and biosensing applications. Devices include BAW sensors using thickness shear modes and SAW sensors using Rayleigh waves or horizontally polarized shear waves (HPSWs). Analyte specificity and selectivity of the sensors are determined by the sensor coatings. If a group of analytes is to be detected or if only selective coatings (i.e., coatings responding to more than one analyte) are available, the use of multi-sensor arrays is advantageous, as the evaluation of the resulting signal patterns allows qualitative and quantitative characterization of the sample. Virtual sensor arrays utilize only one sensor but combine itwith enhanced signal evaluation methods or preceding sample separation, which results in similar results as obtained with multi-sensor arrays. Both array types have shown to be promising with regard to system integration and low costs. This review discusses principles and design considerations for acoustic multi-sensor and virtual sensor arrays and outlines the use of these arrays in multi-analyte detection applications, focusing mainly on developments of the past decade

Simulation of an experimental database of infrared spectra of complex gaseous mixtures for detecting specific substances. The case of drug precursors

This work is motivated by the need to develop suitable databases in absence of real experimental data, for instance when spectra measured with a newly developed instrumentation on real samples are not available yet. This notwithstanding, in fact, the realization of the physical project should be addressed by a starting database, also invaluable in order to test its effectiveness. In this article we face the issue of simulating gas mixtures spectra for the development of a new sensor for External Cavity-Quantum Cascade Laser Photoacoustic Spectroscopy (EC-QCLPAS) starting from literature FT-IR spectra of pure components: a dataset is realized suitable to realistically represent the ensemble of spectra of the gas mixtures of interest. The informative data deriving from the literature spectra were combined with the stochastic component extracted from a sample spectrum recorded with a prototype instrument, allowing us to build a matrix containing thousands of simulated spectra of gaseous mixtures, accounting for the presence of different components at different concentrations. Signal processing and experimental design techniques were used along the whole path leading to the dataset of simulated spectra. In particular, the goal of the construction of the database lies in the development of a final system to detect drug precursors in the vapour phase. The comparison of some EC-QCLPAS spectra with the corresponding simulated signals confirms the validity of the proposed approach

Development of Arduino-based portable systems for electroanalytical detection

This work presents the development of a multi-mode electroanalytical detection system based on Arduino microcontroller board. First, a multichannel impedance readout system is designed for alternating current electrokinetics (ACEK) based capacitive sensing. ACEK phenomena on 100μm interdigitated electrodes are observed via fluorescent particles as well as bioparticles, which illustrate the mechanisms of ACEK target enrichment for the capacitive sensing method. I2C multiplexer is applied to allow multiple impedance converters to work together providing continuous AC signals for ACEK capacitive sensing. Second, an electronic nose composed of three modules including a gas sensor array, a circuit for signal acquisition integrated with Arduino microcontroller board, and a PC for signal analysis is designed. A backpropagation neural network with one hidden layer and one output layer is trained to classify gas samples from binary and ternary mixtures of acetone, ethanol, and isopropyl alcohol. Three features are extracted from transient signals in a short time (as compared to steady-state signals), and the classification is done within 1 minute after gas reached the surface of the sensors. Third, a low-cost portable potentiometric sensing system for the detection of heavy metals in water is developed and assessed by testing with hand-fabricated all-solid-state Pb2+ and Cd2+ ion-selective electrodes (ISEs). To avoid the use of a multimeter, an extended-gate metal-oxide-semiconductor field-effect transistor (MOSFET) is applied to the readout circuit and integrated with an Arduino microcontroller board. ALD1106 matched MOSFET pair is chosen for differential sensing to overcome the possible drift problem of ISEs. With a threshold voltage of 0.7 V while operating at the subthreshold region, the MOSFET could be biased via a potentiometer to avoid the use of a voltage source. Last, the three different analytical detections are integrated into one multi-mode system in the design

Microfabricated Optofluidic Ring Resonators for Sensitive, High-Speed Detection of Volatile Organic Compounds

The development of microfabricated sensors and sensor arrays for volatile organic compounds (VOC) and their evaluation as detectors in micro-scale gas chromatographic (μGC) instrumentation are described. Initial efforts explored the discrimination of VOCs with arrays of chemiresistors (CR) employing interface layers of thiolate-monolayer-protected gold nanoparticles (MPNs) or tin-oxide nanowires (NWs). The response diversity of several possible MPN-CR arrays was found to exceed that of the NW-CR array, and was not enhanced by combining the former with the latter. The next study demonstrated that the response diversity of MPN-CR arrays could be enhanced moderately by combining them with arrays of mass-sensitive MPN-coated thickness-shear-mode resonators. However, the analysis of binary VOC mixtures was not satisfactory even with the best of these multi-transducer arrays. A new type of optical vapor sensor was then created: the microfabricated optofluidic ring resonator (μOFRR). This sensor combines vapor sensing and fluidic transport functions in a monolithic microstructure comprising a hollow, vertical SiOx cylinder (250 μm i.d.) with a central quasi-toroidal mode-confinement section, grown and partially released from a Si substrate. It also integrates fluidic-interconnection and fiber-optic probe alignment features. High-Q whispering gallery modes (WGM) generated with a tunable near-IR laser exhibited shifts in resonant wavelength, λWGM, from polymer swelling and refractive index changes as vapors reversibly partitioned into the thin sorptive-polymer film lining the cylinder. Remarkably high sensitivity and rapid responses were obtained with this μOFRR sensor installed downstream from a single μGC separation column and a two-dimensional μGC subsystem. Since MPN films exhibit localized surface plasmon resonance (LSPR) they also have the potential to serve as interface layers in optical sensor arrays. Indeed, it was shown that VOC discrimination was possible by probing an MPN film at just two wavelengths flanking its LSPR absorbance maximum in a custom-built reflectance measurement system. In a first attempt to adapt multi-wavelength plasmonic sensing to the μOFRR platform, measured shifts in λWGM from an MPN coated μOFRR sensor were shown to be proportional to concentration for several VOCs. Results suggest that arrays of MPN-coated μOFRR sensors show great promise as detectors in single- and multi-dimensional μGC systems.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111492/1/keesc_1.pd

Optical In-Process Measurement Systems

Information is key, which means that measurements are key. For this reason, this book provides unique insight into state-of-the-art research works regarding optical measurement systems. Optical systems are fast and precise, and the ongoing challenge is to enable optical principles for in-process measurements. Presented within this book is a selection of promising optical measurement approaches for real-world applications

Experimental study on transcritical Rankine cycle (TRC) using CO2/R134a mixtures with various composition ratios for waste heat recovery from diesel engines

A carbon dioxide (CO2) based mixture was investigated as a promising solution to improve system performance and expand the condensation temperature range of a CO2 transcritical Rankine cycle (C-TRC). An experimental study of TRC using CO2/R134a mixtures was performed to recover waste heat of engine coolant and exhaust gas from a heavy-duty diesel engine. The main purpose of this study was to investigate experimentally the effect of the composition ratio of CO2/R134a mixtures on system performance. Four CO2/R134a mixtures with mass composition ratios of 0.85/0.15, 0.7/0.3, 0.6/0.4 and 0.4/0.6 were selected. The high temperature working fluid was expanded through an expansion valve and then no power was produced. Thus, current research focused on the analysis of measured operating parameters and heat exchanger performance. Heat transfer coefficients of various heat exchangers using supercritical CO2/R134a mixtures were provided and discussed. These data may provide useful reference for cycle optimization and heat exchanger design in application of CO2 mixtures. Finally, the potential of power output was estimated numerically. Assuming an expander efficiency of 0.7, the maximum estimations of net power output using CO2/R134a (0.85/0.15), CO2/R134a (0.7/0.3), CO2/R134a (0.6/0.4) and CO2/R134a (0.4/0.6) are 5.07 kW, 5.45 kW, 5.30 kW, and 4.41 kW, respectively. Along with the increase of R134a composition, the estimation of net power output, thermal efficiency and exergy efficiency increased at first and then decreased. CO2/R134a (0.7/0.3) achieved the maximum net power output at a high expansion inlet pressure, while CO2/R134a (0.6/0.4) behaves better at low pressure

Multi-Dimensional Sensors and Sensing Systems

A universal microelectromechanical (MEMS) nano-sensor platform having a substrate and conductive layer deposited in a pattern on the surface to make several devices at the same time, a patterned insulation layer, wherein the insulation layer is configured to expose one or more portions of the conductive layer, and one or more functionalization layers deposited on the exposed portions of the conductive layer to make multiple sensing capability on a single MEMS fabricated device. The functionalization layers are adapted to provide one or more transducer sensor classes selected from the group consisting of: radiant, electrochemical, electronic, mechanical, magnetic, and thermal sensors for chemical and physical variables and producing more than one type of sensor for one or more significant parameters that need to be monitored

Preliminary experimental comparison and feasibility analysis of CO2/R134a mixture in Organic Rankine Cycle for waste heat recovery from diesel engines

This paper presents results of a preliminary experimental study of the Organic Rankine Cycle (ORC) using CO2/R134a mixture based on an expansion valve. The goal of the research was to examine the feasibility and effectiveness of using CO2 mixtures to improve system performance and expand the range of condensation temperature for ORC system. The mixture of CO2/R134a (0.6/0.4) on a mass basis was selected for comparison with pure CO2 in both the preheating ORC (P-ORC) and the preheating regenerative ORC (PR-ORC). Then, the feasibility and application potential of CO2/R134a (0.6/0.4) mixture for waste heat recovery from engines was tested under ambient cooling conditions. Preliminary experimental results using an expansion valve indicate that CO2/R134a (0.6/0.4) mixture exhibits better system performance than pure CO2. For PR-ORC using CO2/R134a (0.6/0.4) mixture, assuming a turbine isentropic efficiency of 0.7, the net power output estimation, thermal efficiency and exergy efficiency reached up to 5.30 kW, 10.14% and 24.34%, respectively. For the fitting value at an expansion inlet pressure of 10 MPa, the net power output estimation, thermal efficiency and exergy efficiency using CO2/R134a (0.6/0.4) mixture achieved increases of 23.3%, 16.4% and 23.7%, respectively, versus results using pure CO2 as the working fluid. Finally, experiments showed that the ORC system using CO2/R134a (0.6/0.4) mixture is capable of operating stably under ambient cooling conditions (25.2–31.5 °C), demonstrating that CO2/R134a mixture can expand the range of condensation temperature and alleviate the low-temperature condensation issue encountered with CO2. Under the ambient cooling source, it is expected that ORC using CO2/R134a (0.6/0.4) mixture will improve the thermal efficiency of a diesel engine by 1.9%