The Effect of Encapsulation on Crack-Based Wrinkled Thin Film Soft Strain Sensors.

Practical wearable applications of soft strain sensors require sensors capable of not only detecting subtle physiological signals, but also of withstanding large scale deformation from body movement. Encapsulation is one technique to protect sensors from both environmental and mechanical stressors. We introduced an encapsulation layer to crack-based wrinkled metallic thin film soft strain sensors as an avenue to improve sensor stretchability, linear response, and robustness. We demonstrate that encapsulated sensors have increased mechanical robustness and stability, displaying a significantly larger linear dynamic range (~50%) and increased stretchability (260% elongation). Furthermore, we discovered that these sensors have post-fracture signal recovery. They maintained conductivity to the 50% strain with stable signal and demonstrated increased sensitivity. We studied the crack formation behind this phenomenon and found encapsulation to lead to higher crack density as the source for greater stretchability. As crack formation plays an important role in subsequent electrical resistance, understanding the crack evolution in our sensors will help us better address the trade-off between high stretchability and high sensitivity.

Machine learning plus optical flow: a simple and sensitive method to detect cardioactive drugs.

Current preclinical screening methods do not adequately detect cardiotoxicity. Using human induced pluripotent stem cell-derived cardiomyocytes (iPS-CMs), more physiologically relevant preclinical or patient-specific screening to detect potential cardiotoxic effects of drug candidates may be possible. However, one of the persistent challenges for developing a high-throughput drug screening platform using iPS-CMs is the need to develop a simple and reliable method to measure key electrophysiological and contractile parameters. To address this need, we have developed a platform that combines machine learning paired with brightfield optical flow as a simple and robust tool that can automate the detection of cardiomyocyte drug effects. Using three cardioactive drugs of different mechanisms, including those with primarily electrophysiological effects, we demonstrate the general applicability of this screening method to detect subtle changes in cardiomyocyte contraction. Requiring only brightfield images of cardiomyocyte contractions, we detect changes in cardiomyocyte contraction comparable to - and even superior to - fluorescence readouts. This automated method serves as a widely applicable screening tool to characterize the effects of drugs on cardiomyocyte function.