Chemicapacitive microsensors for detection of explosives and TICs

Abstract

Seacoast Science develops chemical sensors that use polymer-coated micromachined capacitors to measure the dielectric permittivity of an array of selectively absorbing materials. We present recent results demonstrating the sensor technology's capability to detect components in explosives and toxic industrial chemicals.These target chemicals are detected with functionalized polymers or network materials, chosen for their ability to adsorb chemicals. When exposed to vapors or gases, the permittivity of these sorbent materials changes depending on the strength of the vapor-sorbent interaction. Sensor arrays made of ten microcapacitors on a single chip have been previously shown to detect vapors of organic compounds (chemical warfare agents, industrial solvents, fuels) and inorganic gases (SO 2 , , CO 2 , NO 2 ).Two silicon microcapacitor structures were used, one with parallel electrode plates and the other with interdigitated "finger-like" electrodes. The parallel-plates were approximately 300 µm wide and separated by 750 nm. The interdigitated electrodes were approximately 400 µm long and were elevated above the substrate to provide faster vapor access. Eight to sixteen of these capacitors are fabricated on chips that are 5 x 2 mm and are packaged in less than 50 cm 3 with supporting electronics and batteries, all weighing less than 500 grams. The capacitors can be individually coated with different materials creating a small electronic nose that produces different selectivity patterns in response to different chemicals. The resulting system's compact size, low-power consumption and low manufacturing costs make the technology ideal for integration into various systems for numerous applications.

Miniature Nitro and Peroxide Vapor Sensors Using Nanoporous Thin Films

“…Similar sensors demonstrated by other research groups have used nonspecific, i.e. off-the-shelf, polymer films which show sensitivity to a target analyte, but also exhibit undesired and sizeable cross-sensitivity to a wide range of non-targeted compounds [17][18][19]. The sensors reported previously [16] demonstrated sensitivity to nitro-bearing compounds to a concentration of around 20 ppm.…”

High-Sensitivity Micro-Gas Chromatograph–Photoionization Detector for Trace Vapor Detection

“…A particular point of interest is sensing of trace VOCs for applications such as indoor air screening or detection of explosives and narcotics, which critically require highly sensitive vapor detectors which ultimately determine the sensitivity of μGC systems. Over the past few decades, a variety of miniaturized vapor detectors have been developed that can be used in μGC, including miniaturized thermal conductivity detectors, surface acoustic wave detectors, chemiresistors, chemicapacitors, and electron capture detectors. − In general, although these microdetectors can achieve extremely small footprint and low operating costs (power and gas consumption), their sensitivity is typically poor compared to the flame ionization detectors (FIDs) widely used in benchtop GC systems, which μGC systems aim to supplement or replace. While FIDs may possess detection limits on the order of sub-picogram or sub-parts per trillion concentrations (e.g., considering a mass of ∼1 pg in 1 L volume), most miniaturized detectors have detection limits around a few parts per million or at best tens of parts per billion. − This poor performance presents a problem for the aforementioned trace vapor analysis applications, which require near picogram detection limits, corresponding to parts per trillion to sub-parts per billion concentration levels (∼ng/L or sub ng/L).…”

Prototype of Nitro Compound Vapor and Trace Detector Based on a Capacitive MIS Sensor

“…The methods for increasing the sensitivity and selectivity of microelectronic gas sensors by using pre-concentrators [29] and pattern recognition algorithms in conjunction with a multiple-sensor array [30,31] are also known. However, for the efficient operation of the matrix of gas sensors included in a machine learning neural network, long-term stability of characteristics and high selectivity are required, which, to this day, are urgent tasks for research in the field of gas sensors.…”