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

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.

Application-Specific Adaptable Coatings for Sensors: Using a Single Polymer-Plasticizer Pair to Detect Aromatic Hydrocarbons, Mixtures, and Interferents in Water with Single Sensors and Arrays

A relatively simple design procedure is presented for new, adaptable chemical sensor coatings made from a single polymer-plasticizer pair to detect single or a mixture of chemical compounds (e.g., BTEX, the small aromatic hydrocarbon family). Affinity between coating components and target analytes, expressed through Hansen solubility parameters and relative energy difference values, describes the sensitivity of the resultant coatings to each analyte. While analyte affinity is paramount for plasticizer selection, for the aqueous-phase sensing application described here, it must be traded off with the permanence in the host polymer, i.e., resistance to leaching into the ambient aqueous phase; deleterious effects including coating creep must also be minimized. By varying the polymer:plasticizer mixing ratio, the physical and chemical properties of the resultant coatings can be tuned across a range of sensing properties, in particular the differential response magnitude and rate, for multiple analytes. Together with the measurement of multiple sensor response parameters (relative sensitivity and response time constant) for each coating, this approach allows for identification and quantification of target analytes not previously separable using commercial off-the-shelf (COTS) polymer sensor coatings. Sensing results using a five-sensor array based on five different mixing ratios of a single plasticizer polymer pair (plasticizer: ditridecyl phthalate; polymer: polystyrene) demonstrate unique identification of mixtures of BTEX analytes, including differentiation of the chemical isomers ethylbenzene and total xylene (or "xylenes"), something not previously feasible for separation-free liquid-phase sensing with commercially available polymer coatings. Ultimately, the response of a single optimized sensor coating identified and quantified the components of various mixtures, including identification of likely interferents, using a customized estimation-theory-based multivariate signal-processing technique.

Real-Time Measurement of Polymer Brush Dynamics Using Silicon Photonic Microring Resonators: Analyte Partitioning and Interior Brush Kinetics

Polymer brushes are found in biomedical and industrial technologies, where they exhibit functionalities considerably dependent on polymer brush-solvent-analyte interactions. It remains a difficult challenge to quickly analyze solvent-swollen polymer brushes, both at the solvent-polymer brush interface and in the brush interior, as well as to monitor the kinetics of interaction of solvent-swollen brushes with key analytes. Here, we demonstrate the novel use of silicon photonic microring resonators to characterize in situ swollen polymer brush-analyte interactions. By monitoring resonant wavelength shifts, we find that brush-solvent-analyte interaction parameters can be extracted from a single set of data or from successive analyte introductions using a single brush-coated sensor. The partition coefficient of three industrially relevant plasticizers into hydrophobic and hydrophilic brushes was determined and found to be in agreement with known solubility trends. We found that the diffusion coefficient of the plasticizer into the brush decreases as brush thickness increases, supporting a model of a dense inner brush layer and diffuse outer layer. pK_a's of pH-sensitive brushes were determined on the microring resonator platform; upon increasing the dry brush thickness, the pK_a for poly(2-dimethylamino ethyl methacrylate) decreased from 8.5 to approach the bulk material pK_a of 7.3 and showed dependence on the presence and concentration of salt. These proof-of-concept experiments show how the surface-sensitive nature of the microring resonator detection platform provides valuable information about the interaction of the polymer brushes with the solvents and analytes, not easily accessed by other techniques.

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 it with 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.

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.