Highly Sensitive Flexible Tactile Sensors in Wide Sensing Range Enabled by Hierarchical Topography of Biaxially Strained and Capillary-Densified Carbon Nanotube Bundles.

Flexible tactile sensors with high sensitivity have received considerable attention for their use in wearable electronics, human-machine interfaces, and health-monitoring devices. Although various micro/nanostructured materials are introduced for high-performance tactile sensors, simultaneously obtaining high sensitivity and a wide sensing range remains challenging. Here, a resistive tactile sensor is presented based on the hierarchical topography of carbon nanotubes (CNTs) prepared by a low-cost and straightforward manufacturing process. The 3D hierarchical structure of the CNTs over large areas is formed by transferring vertically aligned CNT bundles to a prestrained elastomer substrate and subsequently densifying them through capillary forming, providing a monotonic increase in the contact area as applied pressure. The deformable and hierarchical structure of CNTs allows the sensor to exhibit a wide sensing range (0-100 kPa), high sensitivity (141.72 kPa-1 ), and low detection limit (10 Pa). Additionally, the capillary-formed CNT structure results in increased durability of the sensor over repeated pressures. Based on these advantages, meaningful applications of tactile sensors, such as object recognition gloves and multidirectional force perceptions, are successfully realized. Given the scalable fabrication method, 3D hierarchically structured CNTs provide an essential step toward next-generation wearable devices.

A novel patient-specific model to compute coronary fractional flow reserve.

The fractional flow reserve (FFR) is a widely used clinical index to evaluate the functional severity of coronary stenosis. A computer simulation method based on patients' computed tomography (CT) data is a plausible non-invasive approach for computing the FFR. This method can provide a detailed solution for the stenosed coronary hemodynamics by coupling computational fluid dynamics (CFD) with the lumped parameter model (LPM) of the cardiovascular system. In this work, we have implemented a simple computational method to compute the FFR. As this method uses only coronary arteries for the CFD model and includes only the LPM of the coronary vascular system, it provides simpler boundary conditions for the coronary geometry and is computationally more efficient than existing approaches. To test the efficacy of this method, we simulated a three-dimensional straight vessel using CFD coupled with the LPM. The computed results were compared with those of the LPM. To validate this method in terms of clinically realistic geometry, a patient-specific model of stenosed coronary arteries was constructed from CT images, and the computed FFR was compared with clinically measured results. We evaluated the effect of a model aorta on the computed FFR and compared this with a model without the aorta. Computationally, the model without the aorta was more efficient than that with the aorta, reducing the CPU time required for computing a cardiac cycle to 43.4%.