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Most research aimed at measuring biomarkers on the skin is only concernedwith sensing chemicals in sweat using electrical signals, but these methods arenot truly non-invasive nor non-intrusive because they require substantialamounts of sweat to get a reading. This project aims to create a trulynon-invasive wearable sensor that continuously detects the gaseous acetone (abiomarker related to metabolic disorders) that ambiently comes out of the skin.Composite films of polyaniline and cellulose acetate, exhibitingchemo-mechanical actuation upon exposure to gaseous acetone, were tested in theheadspaces above multiple solutions containing acetone, ethanol, and water togauge response sensitivity, selectivity, and repeatability. The bending of thefilms in response to exposures to these environments was tracked by anautomatic video processing code, which was found to out-perform anoff-the-shelf deep neural network-based tracker. Using principal componentanalysis, we showed that the film bending is low dimensional with over 90% ofthe shape changes being captured with just two parameters. We constructedforward models to predict shape changes from the known exposure history andfound that a linear model can explain 40% of the observed variance in film tipangle changes. We constructed inverse models, going from third order fits ofshape changes to acetone concentrations where about 45% of the acetonevariation and about 30% of ethanol variation are captured by linear models, andnon-linear models did not perform substantially better. This suggests there issufficient sensitivity and inherent selectivity of the films. These models,however, provide evidence for substantial hysteretic or long-time-scaleresponses of the PANI films, seemingly due to the presence of water. Furtherexperiments will allow more accurate discrimination of unknown exposureenvironments.

Towards skin-acetone monitors with selective sensitivity: dynamics of PANI-CA films