  Most research aimed at measuring biomarkers on the skin is only concerned
with sensing chemicals in sweat using electrical signals, but these methods are
not truly non-invasive nor non-intrusive because they require substantial
amounts of sweat to get a reading. This project aims to create a truly
non-invasive wearable sensor that continuously detects the gaseous acetone (a
biomarker related to metabolic disorders) that ambiently comes out of the skin.
Composite films of polyaniline and cellulose acetate, exhibiting
chemo-mechanical actuation upon exposure to gaseous acetone, were tested in the
headspaces above multiple solutions containing acetone, ethanol, and water to
gauge response sensitivity, selectivity, and repeatability. The bending of the
films in response to exposures to these environments was tracked by an
automatic video processing code, which was found to out-perform an
off-the-shelf deep neural network-based tracker. Using principal component
analysis, we showed that the film bending is low dimensional with over 90% of
the shape changes being captured with just two parameters. We constructed
forward models to predict shape changes from the known exposure history and
found that a linear model can explain 40% of the observed variance in film tip
angle changes. We constructed inverse models, going from third order fits of
shape changes to acetone concentrations where about 45% of the acetone
variation and about 30% of ethanol variation are captured by linear models, and
non-linear models did not perform substantially better. This suggests there is
sufficient sensitivity and inherent selectivity of the films. These models,
however, provide evidence for substantial hysteretic or long-time-scale
responses of the PANI films, seemingly due to the presence of water. Further
experiments will allow more accurate discrimination of unknown exposure
environments.
