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.

Abstract

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.