Polymer-coated Surface Acoustic Wave Sensors Research Articles

Effects of temperature and humidity on the performance of a PECH polymer coated SAW sensor.

The influences of environment, such as temperature, humidity and interfering gases, on the performance of a surface acoustic wave (SAW) sensor in the detection of 2-chloroethyl ethyl sulfide (CEES) were invested. The 150 MHz SAW dual delay lines were used, coated with a poly(epichlorohydrin) (PECH) thin layer, and CEES was detected under different concentrations. Linear correlation between the frequency-shift and the exposure time of the sensor to CEES could be observed, and the limit of CEES could be detected as low as 1.5 mg m−3. Under different temperature (0–50 C°) and humidity (30–80% RH) conditions, CEES was detected by the fabricated SAW sensor coated with PECH, the frequency shifts were measured and the performance of the sensor was evaluated. The results proved that temperature and humidity were the most important factors to influence the performance of SAW sensors; with the decreasing of temperature and the increasing of humidity, there would be larger frequency shifts. In the interference experiments, it was found that most gases existing in the environment in high concentrations would not influence the detection of CEES. Then, the SAW sensor having been fabricated was kept under the conditions of 25 °C and 35% RH for 18 months to further verify the quality, and CEES was detected every so many months. It proved that the performance of the sensor would decrease about 16.39% after 18 months. Although it reflected the attenuation of the sensor to some extent, the sensor was still in good condition. Additionally, the related mechanisms were also discussed.

Detection of 2,4-dinitrotoluene using hydrogen-bond acidic polymer coated SAW sensor

A strong hydrogen-bond acidic (HBA) polymer linear fluoroalcoholic polysiloxane (PLF), synthesized by one-step hydrosilylation, was introduced for 2,4-dinitrotoluene (2,4-DNT) detection using surface acoustic wave (SAW) devices. FT-IR spectra and 1H NMR spectra characterizations confirmed its molecular structure. Then, PLF was dissolved in chloroform, and spray coated onto a 434 MHz SAW device. And subsequently, the sensitivity and selectivity of the fabricated sensor were studied based on a SAW test platform. To 2,4-DNT of 1 ppm and 100 ppb (for DNT, 1 ppm = 8.1 mg/m3; 1 ppb = 8.1 μg/m3), the responses of the sensor rose 7.7 and 1.5 kHz, respectively. Furthermore, compared with some common interference vapors at 10 mg/m3, the response of the sensor to 2,4-DNT at 1 ppm was 5 times stronger. The sensitive property was attributed to the hydrogen bond interaction between the hexafluoroisopropanol (HFIP) group in PLF and the basic lone electron pairs of nitro-groups in 2,4-DNT. Comparison between the PLF sensor and hydroxyl group functionalized polymer (carbowax and MOBCD)-coated sensors was investigated, and the results showed that the PLF sensor exhibited better sensitivity. In a word, the PLF sensor exhibited fast response, negligible baseline drifts and excellent reversibility, indicating good candidates of HFIP group functionalized HBA polymers for practical nitroaromatic explosives detection.

Ratiometric info-chemical communication system based on polymer-coated surface acoustic wave microsensors

A novel ratiometric info-chemical communication system has been developed that is based upon an array of four surface acoustic wave (SAW) resonator microsensors operating at a frequency of 262 MHz and under ambient conditions. The info-chemical mixtures were generated by a micro-evaporator and transported to the SAW sensors inside a flow chamber. Binary mixtures of 3-methylbutan-1-ol and ethyl acetate were used to demonstrate the principle of encoding and decoding different ratios of volatile info-chemicals in this system. The resonant frequencies of the four polymer-coated SAW sensors and the associated reference (uncoated) SAW sensors were used to determine differential responses corresponding to the different ratiometric mixtures of the info-chemicals. The SAW sensors were spray coated with four different stationary phase polymer compounds, namely polycaprolactone, polyvinylcarbazole, polystyrene-co-butadiene, and polyethylene-co-vinylacetate. Principal components analysis was performed on both steady-state and dynamic features extracted from the sensor responses. Our results show clear linear separability of the different chemical ratios as distinct clusters in multi-variate space. In conclusion, we believe that this is the first demonstration of the encoding, transmitting, and decoding ratiometric information using a system based upon an array of SAW-based microsensors. This novel info-chemical communication system, based upon fixed ratios, transmits chemical information over distances that would otherwise be impossible using absolute concentrations.

Effect of film thickness and viscoelasticity on separability of vapour classes by wavelet and principal component analyses of polymer-coated surface acoustic wave sensor transients

The transient response of a polymer-coated surface acoustic wave (SAW) vapour sensor depends on partitioning and diffusion of vapour species into the polymer in conjunction with its thickness and viscoelastic properties. The shapes of transient signals carry information about vapour identities due to specificity of the partition coefficient and the diffusion coefficient. The analysis of transient signals therefore offers a simpler approach for vapour identification in comparison to conventional electronic nose systems that employ a broadly selective sensor array. The transient response-based methods are however not developed to a similar level of maturity as their sensor array counterparts. The main reason for this is associated with complex signal generation kinetics and polymer viscoelasticity. The latter is independent of vapour identities (assuming low concentrations) but influences sensor response through nonlinear dependences on polymer thickness and viscoelastic coefficients. In this paper, we endeavour to find out whether viscoelasticity and its manifestation through thickness dependences could be turned into an advantage for transient-based vapour identification. Using an established SAW sensor model with additive noise we analyse sensor transients by wavelet decomposition and principal component analysis (PCA) for various combinations of polymer thickness, viscoelastic storage and loss moduli and noise level. We calculate vapour class separability measures defined on the basis of scatter matrices of principal components of wavelet coefficients to determine the discrimination ability of sensor transients for various combinations of film thickness and viscoelastic parameters. The simulation experiments are performed by considering a polyisobutylene-coated SAW oscillator sensor under exposure to seven volatile organic compounds (chloroform, chlorobenzene, o-dichlorobenzene, n-heptane, toluene, n-hexane and n-octane). The film thicknesses are varied from thin film (where mass loading dominates) to thick film through fundamental film resonance (where viscoelastic effects dominate) to very thick film regions spanning over a few higher order resonances. The storage and loss shear moduli are varied to simulate conditions of glassy, glassy-rubbery and rubbery phases of polymer. The transient response generation incorporates an additive noise source with uniform distribution over a specified range. Effect of noise variation on class separability is also studied. A comparison of the wavelet transform method is made with the phase space-based partial-least-squares regression method for feature extraction. In conclusion, it is found that (i) vapour class separability increases with polymer thickness for all viscoelastic conditions from glassy to rubbery, except near film resonances, (ii) near resonance class separability dramatically declines for films with no or low loss, (iii) by imparting finite viscoelastic losses to polymer coatings, not only are the detrimental effects of film resonance eliminated but also the sensor performance improves, and (iv) viscoelastic effects produce better noise immunity in thick film sensors. This analysis provides a new perspective for designing high-performance transient sensors through optimization of film thickness for specific polymer selections.

Modeling electrical response of polymer-coated SAW resonators by equivalent circuit representation

The paper presents an equivalent circuit model of the polymer coated surface acoustic wave (SAW) resonators by combining coupling-of-mode (COM) description of SAW resonators and perturbation calculation of SAW propagation under polymer loading. An expression for the motional load produced by polymer coating is deduced in terms of COM parameters and polymer characteristics. In addition, expressions for the shifts in resonance frequency and attenuation due to polymer loading are obtained. Simulation results are presented for one-port and two-port resonator devices coated with viscoelastic thin polymer film. The influence of polymer film on resonator response is studied with regard to variations in film thickness and shear modulus. The model simplifies understanding of polymer-coated SAW sensors.

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.

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.

Adaptation and Evaluation of a Personal Electronic Nose for Selective Multivapor Analysis

The evaluation of a commercial, belt-mountable “electronic nose” modified for the rapid recognition and quantification of individual solvent vapors and simple vapor mixtures at low ppm concentrations is described. Marketed under the name VaporLab® this direct-reading instrument was designed for qualitative determinations of the presence or absence of selected individual vapors and was adapted in this study for quantitative determinations of vapors and vapor-mixture components. Vapor samples are concentrated on a small adsorbent bed and then thermally desorbed for analysis by an array of four polymer-coated surface acoustic wave sensors. Tests were performed with 13 organic solvent vapors individually and in selected binary, ternary, and quaternary mixtures at concentrations ranging from 0.1 to 12 times the respective American Conference of Governmental Industrial Hygienists' (ACGIH®) threshold limit value (TLV®). Pattern recognition analyses yielded a library of response patterns to which subsequent actual and virtual (i.e., Monte-Carlo simulated) samples were compared to assess performance. Limits of detection < 0.025 × TLV are achieved (based on the most sensitive sensors) for 0.25 L of preconcentrated air samples collected over a 2-min period. Individual vapors from different functional group classes can be recognized, quantified, and discriminated from other vapors with little error, and discrimination of the components of binary mixtures is possible where the component vapor response patterns are sufficiently different. Within-class individual vapor and binary-mixture discriminations are more difficult and most ternary and higher-order mixtures could not be analyzed with acceptable accuracy. Changes in ambient humidity have no effect on responses and changes in temperature lead to well-behaved and compensable changes in responses. Tests of fluctuating concentrations demonstrate the capability for accurately tracking short-term variations in exposure. Overall, results suggest that this instrument could serve effectively as a personal exposure monitor in previously characterized occupational environments with proper revisions in design.

Development of a preconcentration unit for a SAW sensor micro array and its use for indoor air quality monitoring

A new surface acoustic wave (SAW) sensor system for continuous monitoring of air quality was developed. The system employs a miniaturized array of eight polymer coated SAW sensors, a preconcentration unit (‘trap’), and methods of pattern recognition. Care was taken to minimize both the response times of the sensors and the gas volume of the sensor array. Thus, a small trap with low heat capacity can be used, resulting in low power consumption and rapid thermal desorption. The capabilities of the system are demonstrated by successful discrimination of closely related aromatic compounds (BTXE) in the low- and sub-ppm ranges. Design considerations are made with particular emphasis on the necessities arising from the interplay between sensors, coatings, trap, gas fluidics, and pattern recognition software.

Classical least squares transformations of sensor array pattern vectors into vapor descriptors: Simulation of arrays of polymer-coated surface acoustic wave sensors with mass-plus-volume transduction mechanisms

A new method of processing multivariate response data to extract chemical information has been developed. Sensor array response patterns are transformed into a vector containing values for solvation parameter descriptors of the detected vapor’s properties. These results can be obtained by using a method similar to classical least squares (CLS), and equations have been derived for mass- or volume-transducing sensors. Polymer-coated acoustic wave devices are an example of mass-transducing sensors. However, some acoustic wave sensors, such as polymer-coated surface acoustic wave (SAW) devices give responses resulting from both mass-loading and decreases in modulus. The latter effect can be modeled as a volume effect. In this paper, we derive solutions for obtaining descriptor values from arrays of mass-plus-volume-transducing sensors. Simulations were performed to investigate the effectiveness of these solutions and compared with solutions for purely mass-transducing sensor arrays. It is concluded that this new method of processing sensor array data can be applied to SAW sensor arrays even when the modulus changes contribute to the responses. The simulations show that good estimations of vapor descriptors can be obtained by using a closed form estimation approach that is similar to the closed form solution for purely mass-transducing sensor arrays. Estimations can be improved using a nonlinear least squares optimization method. The results also suggest ways to design SAW arrays to obtain the best results, either by minimizing the volume sensitivity or matching the volume sensitivities in the array.

Dual-Chemiresistor GC Detector Employing Monolayer-Protected Metal Nanocluster Interfaces

The synthesis and testing of two gold-thiolate monolayer-protected (nano)clusters as interfacial layers on a dual-chemiresistor vapor sensor array are described. Responses (changes in dc resistance) to each of 11 organic solvent vapors are rapid, reversible, and linear with concentration at low vapor concentrations, becoming sublinear at higher concentrations. Limits of detection (LODs) range from 0.1 to 24 parts per million and vary inversely with solvent vapor pressure. When configured as a GC detector and used to analyze 0.5-L preconcentrated samples of the 11-vapor mixture, the array provides LODs of < or = 700 parts per trillion for most vapors, comparing favorably with those from an integrated array of polymer-coated surface acoustic wave sensors configured and tested similarly. This first report on the use of such an array as a GC detector shows that the combination of response patterns and GC retention times improves capabilities for vapor recognition compared to the sensor array alone or to single-detector GC systems. Spray-coated nanocluster thin films can be deposited reproducibly and exhibit response stability in air that ranges from fair to excellent for up to several months. Scaling the active device area down by a factor of 16 has no significant effect on sensitivity. Implications of these results for portable vapor sensing systems are discussed.

Inverse Least-Squares Modeling of Vapor Descriptors Using Polymer-Coated Surface Acoustic Wave Sensor Array Responses

In previous work, it was shown that, in principle, vapor descriptors could be derived from the responses of an array of polymer-coated acoustic wave devices. This new chemometric classification approach was based on polymer/vapor interactions following the well-established linear solvation energy relationships (LSERs) and the surface acoustic wave (SAW) transducers being mass sensitive. Mathematical derivations were included and were supported by simulations. In this work, an experimental data set of polymer-coated SAW vapor sensors is investigated. The data set includes 20 diverse polymers tested against 18 diverse organic vapors. It is shown that interfacial adsorption can influence the response behavior of sensors with nonpolar polymers in response to hydrogen-bonding vapors; however, in general, most sensor responses are related to vapor interactions with the polymers. It is also shown that polymer-coated SAW sensor responses can be empirically modeled with LSERs, deriving an LSER for each individual sensor based on its responses to the 18 vapors. Inverse least-squares methods are used to develop models that correlate and predict vapor descriptors from sensor array responses. Successful correlations can be developed by multiple linear regression (MLR), principal components regression (PCR), and partial least-squares (PLS) regression. MLR yields the best fits to the training data, however cross-validation shows that prediction of vapor descriptors for vapors not in the training set is significantly more successful using PCR or PLS. In addition, the optimal dimension of the PCR and PLS models supports the dimensionality of the LSER formulation and SAW response models.

Development and application of an automated gas chromatographic sampling system for verification of test vapor stream concentrations for chemical sensor studies

An automated GC sampling system is described which permits real-time verification of vapor stream concentrations used in the testing and development of chemical sensors. The system consists of a computer-actuated injector loop placed in-line between a VG-400 vapor generator (Microsensor Systems) and a surface acoustic wave (SAW) sensor. Optimization of the system was performed by comparing the predicted VG-400 vapor output with the GC-determined vapor stream concentrations. The system was utilized in the identification of hystersis effects in a polymer-coated SAW sensor.

The Polymer-Coated SAW Sensor as a Gravimetric Sensor

The absorption and desorption of chlorobenzene, o-dichlorobenzene, and chloroform in poly[n-butyl methacrylate] (PBMA) was studied in polymer-coated 104 MHz surface acoustic wave (SAW) sensors, and in free-standing polymer films by thermogravimetric analysis (TGA). The sorption processes were analyzed by a Fickian simulation and best-fit partition, and diffusion coefficients were derived. Good correlations were found between simulated and observed data. Partition coefficients derived from SAW response were independent of coating thickness and were found to be about two to three times bigger than those derived from the gravimetric response. In contrast, the diffusion coefficients increased linearly with coating thickness in the range 70−560 kHz. For the thickest polymer coating, SAW-derived diffusion coefficients were comparable with TGA-related diffusion coefficients. This study reconfirms the finding for other polymers that the response of SAW chemosensors is higher than that anticipated from a mass chan...

Optimal coating selection for the analysis of organic vapor mixtures with polymer-coated surface acoustic wave sensor arrays.

A method for determining the optimal set of polymer sensor coatings to include in a surface acoustic wave (SAW) sensor array for the analysis of organic vapors is described. The method combines an extended disjoint principal components regression (EDPCR) pattern recognition analysis with Monte Carlo simulations of sensor responses to rank the various possible coating selections and to estimate the ability of the sensor array to identify any set of vapor analytes. A data base consisting of the calibrated responses of 10 polymer-coated SAW sensors to each of six organic solvent vapors from three chemical classes was generated to demonstrate the method. Responses to the individual vapors were linear over the concentration ranges examined, and coatings were stable over several months of operation. Responses to binary mixtures were additive functions of the individual component responses, even for vapors capable of strong hydrogen bonding. The EDPCR-Monte Carlo method was used to select the four-sensor array that provided the least error in identifying the six vapors, whether present individually or in binary mixtures. The predicted rate of vapor identification (87%) was experimentally verified, and the vapor concentrations were estimated within 10% of experimental values in most cases. The majority of errors in identification occurred when an individual vapor could not be differentiated from a mixture of the same vapor with a much lower concentration of a second component. The selection of optimal coating sets for several ternary vapor mixtures is also examined. Results demonstrate the capabilities of polymer-coated SAW sensor arrays for analyzing of solvent vapor mixtures and the advantages of the EDPCR-Monte Carlo method for predicting and optimizing performance.

Characterization of polymeric surface acoustic wave sensor coatings and semiempirical models of sensor responses to organic vapors.

Responses from an array of four polymer-coated surface acoustic wave sensors exposed to a series of 39 organic vapors were used to investigate sensor response models based on vapor boiling point, solubility parameters, and solvation parameters in conjunction with linear solvation energy relationships. As part of this effort, sensor response data were used to estimate the solubility parameters and solvation parameters of the sensor coatings by adaptation of methods originally developed for use with gas-liquid chromatographic retention data. Values of these parameters were found to be consistent with the structures of the coatings though in some cases different from those determined by other methods. Discrepancies were attributed to differences in the conditions used for the determinations. Sensor responses were linear over the concentration ranges examined and could be summarized using the empirically determined partition coefficient, Ke, for each vapor-coating pair. Linear correlations were found between log Ke and vapor boiling point, and the slopes of the regressions lines were similar to those expected for ideal behavior. The strength of the correlations decreased with increasing coating polarity, and it was necessary to divide the vapors into two or three broad chemical classes in order to obtain satisfactory results. Improved correlations were found by use of Hildebrand solubility parameters in a model based on regular solution theory which attempts to account for nonideal vapor-coating interactions. The use of solvation parameters in linear solvation energy relationships, however, provided the strongest correlations, with modeled K values falling within a factor of 2 of experimental values in all cases and within +/- 25% of experimental values in 83% of the cases. Application of these models to the prediction of sensor array response patterns appears promising.