Redox-Concatenated Aptamer Integrated Skin Mimicking Electrochemical Patch for Noninvasive Detection of Cortisol.

This research focuses on developing and validating a wearable electrochemical biosensor called the concatenated aptamer integrated skin patch, also known as the Captain Patch. The main objective is to detect cortisol levels in sweat, which can provide valuable insights into an individual's health. The biosensor utilizes a corrugated surface that mimics the skin, allowing for better attachment and an improved electrochemical performance. The study demonstrates the successful application of Captain Patch on the human body by using artificially spiked sweat samples. The results indicate good measurement accuracy and conformity when the patch is worn on the body. However, for long-term usage, the patch needs to be changed every 3-4 h or worn three times a day to enable monitoring of cortisol levels. Despite the need for frequent patch changes, the cost-effectiveness and ease of operation make these skin patches suitable for longitudinal cortisol monitoring and other sweat analytes. By customization of the biorecognition probe, the developed biowearable can be used to monitor a variety of vital biomarkers. Overall, Captain Patch, with its capability of detecting specific health markers such as cortisol, hints at the future potential of wearables to offer valuable data on various other biomarkers. Our approach presents the first step in integrating a cost-effective wearable electrochemical patch integrated with a redox-concatenated aptamer for noninvasive biomarker detection. This personalized approach to monitoring can lead to improved patient outcomes and increased patient engagement in managing their health.

Computational design and optimization of electro-physiological sensors.

Electro-physiological sensing devices are becoming increasingly common in diverse applications. However, designing such sensors in compact form factors and for high-quality signal acquisition is a challenging task even for experts, is typically done using heuristics, and requires extensive training. Our work proposes a computational approach for designing multi-modal electro-physiological sensors. By employing an optimization-based approach alongside an integrated predictive model for multiple modalities, compact sensors can be created which offer an optimal trade-off between high signal quality and small device size. The task is assisted by a graphical tool that allows to easily specify design preferences and to visually analyze the generated designs in real-time, enabling designer-in-the-loop optimization. Experimental results show high quantitative agreement between the prediction of the optimizer and experimentally collected physiological data. They demonstrate that generated designs can achieve an optimal balance between the size of the sensor and its signal acquisition capability, outperforming expert generated solutions.