Orthogonal Ambipolar Semiconductors with Inherently Multi-Dimensional Responses for the Discriminative Sensing of Chemical Vapors

Numerous examples of field-effect transistor (FET) biosensors and chemical sensors with good sensitivity and selectivity have now been developed. However, effectively discriminating between analytes has required either the use of receptors that selectively bind specific analytes or the fabrication of an array of sensors with varying but nonspecific responses. Both approaches exhibit significant limitations. In the first case, it can be difficult to design sufficiently specific receptors for many compounds, whereas the number of receptors required scales with the number of analytes to be detected, making it impractical to recognize many different compounds. In the second case, existing approaches to FET sensor arrays are generally material-inefficient and provide modest sensitivity. Here, we demonstrate that orthogonal ambipolar semiconductors consisting of semiconducting p-type polymers and n-type small-molecule nanowires with perpendicular in-plane orientations provide a platform with high sensitivity and inherently multi-dimensional response. This allows for discrimination between even closely related derivatives such as aromatic isomers and n-alkyl alcohols varying in length by a single carbon atom resolution using only a single sensor element.

Graphene Nanoplatelet-Polymer Chemiresistive Sensor Arrays for the Detection and Discrimination of Chemical Warfare Agent Simulants

A cross-reactive array of semiselective chemiresistive sensors made of polymer-graphene nanoplatelet (GNP) composite coated electrodes was examined for detection and discrimination of chemical warfare agents (CWA). The arrays employ a set of chemically diverse polymers to generate a unique response signature for multiple CWA simulants and background interferents. The developed sensors' signal remains consistent after repeated exposures to multiple analytes for up to 5 days with a similar signal magnitude across different replicate sensors with the same polymer-GNP coating. An array of 12 sensors each coated with a different polymer-GNP mixture was exposed 100 times to a cycle of single analyte vapors consisting of 5 chemically similar CWA simulants and 8 common background interferents. The collected data was vector normalized to reduce concentration dependency, z-scored to account for baseline drift and signal-to-noise ratio, and Kalman filtered to reduce noise. The processed data was dimensionally reduced with principal component analysis and analyzed with four different machine learning algorithms to evaluate discrimination capabilities. For 5 similarly structured CWA simulants alone 100% classification accuracy was achieved. For all analytes tested 99% classification accuracy was achieved demonstrating the CWA discrimination capabilities of the developed system. The novel sensor fabrication methods and data processing techniques are attractive for development of sensor platforms for discrimination of CWA and other classes of chemical vapors.