Environmental Chemical and Biological Sensing Using Colorimetric Arrays

Abstract

Introduction This effort seeks to develop chemical and biological sensing capabilities suitable for use in long duration, distributed sensing applications. For this type of application, it is desirable to provide inexpensive devices that can detect a range of targets, both known and unknown, with minimal power requirements. Porphyrins and metalloporphyrins are intensely colored, aromatic molecules with spectrophotometric characteristics that are sensitive to interaction with other chemicals. Porphyrins offer diverse chemical interactions that can be changed through alterations to the molecular structure and the coordinated metal. Through selection of a group of porphyrin indicators, it is possible to generate an array for which the relative responses of the indicator materials can provide target discrimination. The indicators selected for this effort provide reversible responses to chemical targets, offering the potential for long duration deployments and ongoing or repeated use of the indicator arrays [1-5]. Method Hardware. The prototype sensing hardware, our Array Based Environmental Air Monitor (ABEAM), is used with the paper supported porphyrin and porphyrin-antimicrobial peptide indicators. This device comprises a custom housing and control board with commercially available color sensors [4]. Communication with the device is via USB or wireless, with power supplied by DC barrel jack or batteries. Device output is as red, green, and blue (RGB) values versus time either stored in onboard flash memory or reported to a control computer via drip-feed [3-5]. Algorithm. An automated algorithm was developed to process ABEAM output for identification of chemical exposures [1, 3]. Processing by the algorithm is based on the angles between slopes in the device reported RGB color data for each of the color sensors. In general, two windows of data are compared for each color on each device: a baseline window populated by 120 data points and an active window populated by 20 points. The active window includes those points collected in the most recent 10 min with the baseline window including the previous 60 min. The use of these two sliding windows accounts for device drift as well as diurnal changes in environmental conditions. Results and Conclusions Initial work with the ABEAM prototype was completed without the housing or fans [2, 4, 6]. This effort largely focused on identification of indicator materials for use in an array that would allow for discrimination of alcohols (ethanol, methanol, and isopropanol) as model targets to be used in environmental evaluations. Under this work, a standard deviation based algorithm for event identification was developed. When the focus of the effort shifted from rapid on/off target exposures to those completed in an enclosure, to more effectively simulate expected environmental exposures, dramatic differences were noted in the performance of the standard deviation algorithm. The responses of the indicator materials did not occur within a short enough time duration to trigger the algorithm. The slope based algorithm (Section II) was developed as a result.This move from screening experiments to environmental exposures also served to identify shortfalls in instrument parameters. With the short on/off exposures, the use of a 5 s sampling interval with a 100 ms integration time produced data sufficient for characterization. For longer duration autonomous deployments, the 5 s sampling interval produced memory overrun in less than three days. The 30 s sampling interval allowed a run time of seven days in the original device and fourteen days in a follow-on iteration of the prototype. It was also necessary to extend the integration time for the devices under environmental exposure (to 400 ms) to provide sufficient signal to noise ratios for discrimination of events by the slope based algorithm [3-4].We have demonstrated that the ABEAM provides detection of the alcohol targets in either an indoor or outdoor environment. When parameters are optimized and a negative control is included in the array, a specificity of 0.97 with sensitivity 1.0 can be achieved [3]. Here, we also present the results of recent evaluations of a network of six devices used in an outdoor environment for the detection of methyl salicylate. This is the first demonstration of these devices used with ongoing drip-feed data reporting and real-time analysis via wireless communications and using battery power.