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DMREF: Accelerating the deployment of toxic gas sensors

A schematic of a toxic gas sensor

Computations predicted (top left) that chlorine gas will displace liquid crystals on metallic gold surfaces to give an optical response. A thin gold film and liquid crystals (top right) were synthesized. Experiments confirmed a change in the optical output of the system upon exposure to chlorine gas (bottom). The technology has been deployed in a commercial sensor (center). See http://ls-staging.doit.wisc.edu/liquid-crystals/.

A collaborative team from Cornell University, Kent State University, and the University of Wisconsin-Madison, supported by the NSF Designing Materials to Revolutionize and Engineer our Future (DMREF) program, has employed the MGI philosophy to accelerate the design and deployment of metal alloy surfaces for chemoresponsive liquid crystals (LCs). Whereas the design of the first LC chemical sensor for hydrogen sulfide took almost ten years to complete, by iteratively developing over four generations of progressively more sophisticated computational chemistry models of competitive interactions of LCs and targeted chemical species with metal cation binding sites, the team has managed to shorten the timeline for design of chemoresponsive LCs to a few months per analyte.

The designs of liquid crystals that respond to chlorine gas, which emerged from cycles of feedback between computations and experiments, enable the sensing of concentrations of chlorine gas as low as 200 ppb within 15 minutes, which satisfies Occupational Safety and Health Administration (OSHA) personal exposure limits. The team has extended this work to address the design of nerve agent (NA) sensors based on the competitive binding of NAs and liquid crystalline compounds on metal salts.

NAs pose a great threat to society because they are easy to produce and are deadly in nature, which makes developing methods to detect, adsorb, and destroy them crucial. This team has collaborated with an industrial partner, ClearSenseTM, to develop wearable liquid crystalline sensors for monitoring human exposure to toxic gases. This research has also led to the discovery that machine learning techniques can uncover valuable feature information in the liquid crystal response that has not previously been recognized, and as a result, sensor accuracy increased from 60% to 99%.

Results of the project are disseminated at the Chemoresponsive Liquid Crystal Research Database, where a built-in analyte search feature enables the efficient identification of the most promising liquid crystal designs for a desired analyte.

Created September 2, 2021