The advancement could have big implications for the mil […]
The advancement could have big implications for the military, emergency service personnel and other workers that rely on "haz-mat" protection to perform their work and keep others safe.
In research published in the Journal of the American Chemical Society, the chemistry team of Katherine Mirica and Merry Smith describe the creation of new smart fabrics -- named SOFT, for Self-Organized Framework on Textiles -- in what is noted as the first demonstration of simultaneous detection, capture, pre-concentration and filtration of gases in a wearable that uses conductive, porous materials integrated into soft textiles.
According to the study, the SOFT devices have the potential for use in sensing applications ranging from real-time gas detection in wearable systems, to electronically accessible adsorbent layers in protective equipment like gas masks.
"By adding this fabric to a protective suit, sensors can alert the user if a chemical is penetrating the hazardous-material gear," said Katherine Mirica, an assistant professor of chemistry at Dartmouth College. "This is not just passive protection, the textile can actively alarm a user if there is a tear or defect in the fabric, or if functional performance is diminished in any other way."
Among other firsts described in the research are flexible, textile-supported electronic sensors based on materials known as metal-organic frameworks, or MOFs. In the study, the authors also describe a "simple" approach for integrating these conductive, porous materials into cotton and polyester fabrics to produce the e-textiles.
As part of the study, the Dartmouth team demonstrated that the new smart fabric can detect common toxic chemicals. Both the vehicle exhaust pollutant, nitric oxide, and the corrosive poison that reminds most of rotten eggs, hydrogen sulfide, were effectively identified by the SOFT system. In addition to sensing the chemicals, the electronic textiles are capable of capturing and filtering the dangerous toxins.