Analysis of the Detection of Organophosphate Pesticides in Aqueous Solutions Using Hydrogen-Bond Acidic Coating on SH-SAW Devices

The work presented in this paper focuses on the synthesis and characterization of a hybrid organic/inorganic chemically sensitive layer for rapid detection and analysis of OPs in aqueous solutions using SH-SAW devices. Coated SH-SAW devices on 36° YX-LiTaO and 42.75° YX-Quartz (ST-90° X Quartz), are used to determine the optimum operating conditions for achieving rapid sensor responses with high sensitivity. Three analytes (parathion-methyl, parathion, and paraoxon), having similar molecular mass and volume, are used to evaluate the performance of the hybrid organic/inorganic coating in terms of sensor properties of interest including sensitivity, selectivity, reproducibility. It is shown that the coating has a high degree of partial selectivity and sensitivity towards the analytes. With the present non-optimized chemical sensor, a limit of detection of 60 (ppb), 20 (ppb) and 100 (ppb) is estimated for parathion-methyl, parathion, and paraoxon, respectively, when using a 0.5 -thick BPA-HMTS sensing layer. Concentrations as low as 500 (ppb) parathion have been measured. This concentration is significantly much lower than the typical concentrations found on agricultural produce (≥10 ppm)

Detection and Quantification of Multi-Analyte Mixtures Using a Single Sensor and Multi-Stage Data-Weighted RLSE

This work reports the development and experimental verification of a sensor signal processing technique for online identification and quantification of aqueous mixtures of benzene, toluene, ethylbenzene, xylenes (BTEX) and 1, 2, 4-trimethylbenzene (TMB) at ppb concentrations using time-dependent frequency responses from a single polymer-coated shear-horizontal surface acoustic wave sensor. Signal processing based on multi-stage exponentially weighted recursive leastsquares estimation (EW-RLSE) is utilized for estimating the concentrations of the analytes in the mixture that are most likely to have produced a given sensor response. The initial stages of EW-RLSE are used to eliminate analyte(s) that are erroneously identified as present in the mixture; the final stage of EW-RLSE with the corresponding sensor response model representing the analyte(s) present in the mixture is used to obtain a more accurate quantification result of the analyte(s). The success of this method in identifying and quantifying analytes in real-time with high accuracy using the response of just a single sensor device demonstrates an effective, simpler, lower-cost alternative to a sensor array that includes the advantage of not requiring a complex training protocol

High-Sensitivity Magnetic Sensors Based on GMI Microwire-SAW IDT Design

This work presents a design approach for a highly sensitive, miniaturized magnetic sensor. The design makes use of GMI microwires and a multi-electrode SAW IDT. The use of SAW IDTs allows for the magnetic effect of the GMI microwire to be measured through the transduction process. This approach permits simultaneous measurement at different frequencies of operation, enabling highly sensitive measurement over a wide range of magnetic fields. This technique may find application in magnetic sensing for non-invasive battery SOC measurement

On the Relative Sensitivity of Mass-sensitive Chemical Microsensors

In this work, the chemical sensitivity of mass-sensitive chemical microsensors with a uniform layer sandwich structure vibrating in their lateral or in-plane flexural modes is investigated. It is experimentally verified that the relative chemical sensitivity of such resonant microsensors is -to a first order- independent of the microstructure\u27s in-plane dimensions and the flexural eigenmode used, and only depends on the layer thicknesses and densities as well as the sorption properties of the sensing film. Important implications for the design of mass-sensitive chemical microsensors are discussed, whereby the designer can focus on the layer stack to optimize the chemical sensitivity and on the in-plane dimensions and mode shape to optimize the resonator\u27s frequency stability

Obtaining Chemical Selectivity from a Single, Nonselective Sensing Film: Two-Stage Adaptive Estimation Scheme with Multiparameter Measurement to Quantify Mixture Components and Interferents

A new approach is reported to detect and quantify the members of a group of small-aromatic-molecule target analytes: benzene, toluene, ethylbenzene, and xylenes (BTEX), dissolved in water, in the presence of interferents, using only the data collected from a single polymer-coated SH-SAW (shear horizontal surface acoustic wave) device and a two-stage adaptive estimation scheme. This technique is composed of exponentially weighted recursive least-squares estimation (EW-RLSE) and a bank of Kalman filters (BKFs) and does not require any prior knowledge of the initial concentration range of the target analytes. The proposed approach utilizes the transient sensor response to sorption and/or desorption of the analytes as well as the error range associated with the response time constants to provide more information about the analyte-specific interactions with the polymer film. The approach assumes that the sensor response to contaminated groundwater is a linear combination of the responses to the single target analytes, the interferents that interact with the selected polymer sensor coatings, and measurement noise. The proposed technique was tested using actual sensor responses to contaminated groundwater samples containing multiple BTEX compounds with concentrations ranging from 10 to 2000 parts per billion, as well as common interferents including ethanol, 1,2,4-trimethylbenzene, naphthalene, n-heptane, and MTBE (methyl tert-butyl ether). Estimated concentration values, accurate to ±10% for benzene/toluene and ±15% for ethylbenzene/xylenes, are obtained in near-real time. The utilization of sorption and/or desorption data enables detection and quantification of BTEX compounds with improved accuracy, high tolerance to measurement noise, and improved chemical selectivity

Sensor-Based Estimation of BTEX Concentrations in Water Samples Using Recursive Least Squares and Kalman Filter Techniques

This work investigates sensor signal processing approaches that can be used with a sensor system for direct on-site monitoring of groundwater, enabling detection and quantification of BTEX (benzene, toluene, ethylbenzene and xylene) compounds at μg/L (ppb) concentrations in the presence of interferents commonly found in groundwater. A model for the sensor response to water samples containing multiple analytes was first formulated based on experimental results. The first signal processing approach utilizes only RLSE (recursive least squares estimation) whereas the second, a two-step processing technique, utilizes both RLSE and bank of Kalman filters for the estimation process. The estimation techniques were tested using actual sensor responses to contaminated groundwater samples. Results indicate that relatively accurate concentration estimates (within ±15–23% for benzene) can be obtained in near-real time using these techniques. The two-step processing technique gave more accurate results. This approach allows the use of a single sensor, even for multiple analyte detection and quantification

In-Plane Vibration of Hammerhead Resonators for Chemical Sensing Applications

Thermally excited and piezoresistively detected in-plane cantilever resonators have been previously demonstrated for gas- and liquid-phase chemical and biosensing applications. In this work, the hammerhead resonator geometry, consisting of a cantilever beam supporting a wider semicircular “head”, vibrating in an in-plane vibration mode, is shown to be particularly effective for gas-phase sensing with estimated limits of detection in the sub-ppm range for volatile organic compounds. This paper discusses the hammerhead resonator design and the particular advantages of the hammerhead geometry, while also presenting mechanical characterization, optical characterization, and chemical sensing results. These data highlight the distinct advantages of the hammerhead geometry over other cantilever designs

Identification and Quantitation of Aqueous Single- and Multianalyte Solutions of the Isomers Ethylbenzene, m-, p-, and o-Xylene Using a Single Specifically Tailored Sensor Coating and Estimation Theory-Based Signal Processing

The isomer-specific detection and quantitation of m-, p-, and o-xylene and ethylbenzene, dissolved singly and as mixtures in aqueous solutions at concentrations from 100 to 1200 ppb by volume, is reported for a specifically designed polymer-plasticizer coating on a shear-horizontal surface acoustic wave (SH-SAW) device. The polystyrene-ditridecyl phthalate-blend coating was designed utilizing Hansen solubility parameters and considering the dipole moment and polarizability of the analytical targets and coating components to optimize the affinity of the sensor coating for the four chemical isomers. The two key coating sorption properties, sensitivity and response time constant, are determined by the (slightly different) dipole moments and polarizabilities of the four target analytes: as analyte dipole moment decreases, coating sensitivity increases; as analyte polarizability decreases, coating response time lengthens. Using the measured sensitivities and time constants for the targets, sensor signals were processed with exponentially weighted recursive-least-squares estimation (EW-RLSE) to identify (with near 100% accuracy) and quantify (with ± 5–7% accuracy) the isomers. This impressive performance was achieved by combining the specifically tailored, high-sensitivity coating and an SH-SAW platform (yielding a detection limit of 5 ppb for the analytes) and using the EW-RLS estimator, which estimates unknown parameters accurately even in the presence of measurement noise and for analytes with only minor differences in response. Identification of the xylene isomers is important for applications including environmental monitoring and chemical manufacturing

Quantitative Detection of Complex Mixtures using a Single Chemical Sensor: Analysis of Response Transients using Multi-Stage Estimation

Most chemical sensors are only partially selective to any specific target analyte(s), making identification and quantification of analyte mixtures challenging, a problem often addressed using arrays of partially selective sensors. This work presents and experimentally verifies a signal-processing technique based on estimation theory for online identification and quantification of multiple analytes using only the response data collected from a single polymer-coated sensor device. The demonstrated technique, based on multiple stages of exponentially weighted recursive least-squares estimation (EW-RLSE), first determines which of the analytes included in the sensor response model are absent from the mixture being analyzed; these are then eliminated from the model prior to executing the final stage of EW-RLSE, in which the sample’s constituent analytes are more accurately quantified. The overall method is based on a sensor response model with specific parameters describing each coating-analyte pair and requires no initial assumptions regarding the concentrations of the analytes in a given sample. The technique was tested using the measured responses of polymer-coated shear-horizontal surface acoustic wave devices to multi-analyte mixtures of benzene, toluene, ethylbenzene, xylenes, and 1,2,4-trimethylbenzene in water. The results demonstrate how this method accurately identifies and quantifies the analytes present in a sample using the measured response of just a single sensor device. This effective, simple, lower-cost alternative to sensor arrays needs no arduous training protocol, just measurement of the response characteristics of each individual target analyte and the likely interferents and/or classes thereof