The Polymer-Coated SAW Sensor as a Gravimetric Sensor

Abstract

The absorption and desorption of chlorobenzene, odichlorobenzene, and chloroform in poly[n-butyl methacrylate] (PBMA) was studied in polymer-coated 104 MHz surface acoustic wave (SAW) sensors, and in freestanding 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 change only. The viscoelastic effect is again more pronounced than the gravimetric effect. Moreover, the similarity of diffusion coefficients obtained at higher polymer thicknesses suggests that the rate of change of the SAW viscoelastic component is similar to that of the gravimetric element. It is fair to assume that both processes originate from the same event: the absorption of the analyte in the polymer. In this view the polymer-coated SAW sensor may be regarded as an enhanced gravimetric sensor.

Analysis of Liquid-Phase Chemical Detection Using Guided Shear Horizontal-Surface Acoustic Wave Sensors

“…The contribution of mass loading to the sensor response is well accepted in this field while the contribution of the viscoelastic property of polymer coating is still under investigation, even in gasphase sensors. 18,19,[25][26][27] Unfortunately, results in gas phase cannot be totally adapted or used to explain liquid-phase responses. For example, ongoing work in liquid-phase sensing has also shown that for a rubbery polymer such as PDMS viscoelasticity changes can clearly dominate sensor response.…”

“…This is consistent with reported amplification factors due to viscoelastic effects that are reported in the literature for gas-phase sensing, which are in the range of 2−4. 18,19,[25][26][27] Figure 6 Selectivity comparison of 0.64-μm PIB-coated guided SH-SAW sensor platform in the detection of toluene, xylenes, and ethylbenzene in DI water. -is the simulation result using predicted added mass loading and incremental viscoelastic changes based on experimental loss data.…”

ATR-FTIR spectroscopic analysis of sorption of aqueous analytes into polymer coatings used with guided SH-SAW sensors

“…An absorption spectrum can be obtained by monitoring the intensity of reflected IR radiation and using a Beer-Lambert law expression similar to equation (4) where the path length l is replaced by an effective thickness, de, which is determined from the number of reflections and the wavelength-dependent dp [11,15]. The depth of penetration of the evanescent field is ~ 1-2 m from the surface of the prism for the wavelength range of interest in this study.…”

Solvation, transduction and independent component analysis for pattern recognition in SAW electronic nose

“…(7) appears similar to the linear solvation energy relationship (LSER) for modeling vapor-polymer interaction under infinite dilution [3,29]. The LSER model has been widely used to explain equilibrium partition coefficients in gas-liquid chromatography (GLC), thermogravimetry (TG), and quartz crystal microbalance (QCM) and surface acoustic wave (SAW) sensing experiments [30][31][32][33][34][35]. In this model, each interaction term on the right of Eq.…”

“…It is always a tricky issue with materials researchers. Some representative articles and references there amply illustrate this [4][5][6][7][8][9][10][11][12][13]. The pattern recognition methods applied to SAW sensor array data has been mostly conventional ones from the statistical as well as the artificial neural network families.…”