Material Science and Engineering, The Ohio State University, Columbus, OH, United States of America.
Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH, United States of America.
PLoS One. 2022 Apr 27;17(4):e0267311. doi: 10.1371/journal.pone.0267311. eCollection 2022.
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. Nevertheless, the sensor will operate with high selectivity in low sweat body locations, like behind the ear or on the nails.
大多数旨在测量皮肤生物标志物的研究仅关注使用电信号感测汗液中的化学物质,但这些方法并不是真正的非侵入性或非侵入性的,因为它们需要大量的汗水才能获得读数。本项目旨在创建一种真正非侵入性的可穿戴传感器,能够连续检测从皮肤周围环境中散发出来的气态丙酮(一种与代谢紊乱相关的生物标志物)。在含有丙酮、乙醇和水的多种溶液的上方空间中,测试了聚苯胺和醋酸纤维素的复合膜,以衡量其对气态丙酮的响应灵敏度、选择性和可重复性。通过自动视频处理代码跟踪薄膜对这些环境暴露的弯曲,发现该代码比现成的基于深度神经网络的跟踪器表现更好。通过主成分分析,我们表明薄膜弯曲具有低维性,超过 90%的形状变化可以用仅两个参数来捕获。我们构建了正向模型,从已知的暴露历史预测形状变化,并发现线性模型可以解释薄膜尖端角度变化的 40%的观察到的方差。我们构建了反向模型,从形状变化的三阶拟合到丙酮浓度,其中约 45%的丙酮变化和约 30%的乙醇变化可以用线性模型来解释,而非线性模型的表现并没有明显更好。这表明薄膜具有足够的灵敏度和固有选择性。然而,这些模型提供了薄膜存在显著滞后或长时间尺度响应的证据,这似乎是由于水的存在。进一步的实验将允许对未知暴露环境进行更准确的区分。尽管如此,该传感器将在低汗液体部位(如耳后或指甲上)以高选择性运行。