Flexible
pressure sensors that have high sensitivity, high linearity, and a
wide pressure-response range are highly desired in applications of
robotic sensation and human health monitoring. The challenge comes
from the incompressibility of soft materials and the stiffening of
microstructures in the device interfaces that lead to gradually saturated
response. Therefore, the signal is nonlinear and pressure-response
range is limited. Here, we show an iontronic flexible pressure sensor
that can achieve high sensitivity (49.1 kPa–1),
linear response (R
2 > 0.995) over a
broad pressure range (up to 485 kPa) enabled by graded interlocks
of an array of hemispheres with fine pillars in the ionic layer. The
high linearity comes from the fact that the pillar deformation can
compensate for the effect of structural stiffening. The response-relaxation
time of the sensor is <5 ms, allowing the device to detect vibration
signals with frequencies up to 200 Hz. Our sensor has been used to
recognize objects with different weights based on machine learning
during the gripper grasping tasks. This work provides a strategy to
make flexible pressure sensors that have combined performances of
high sensitivity, high linearity, and wide pressure-response range.
Shape memory hydrogel is a kind of hydrogels whose shape can transform between the temporary shape and initial shape when exposed on external stimuli, such as water, temperature, pH, etc. In the last decade, shape memory hydrogels have gained increasing interests due to their distinguished properties, while the constitutive models to illustrate their shape memory mechanism are still lacking. In this study, we propose a constitutive model for water-triggered shape memory hydrogels based on the transition between the sparse phase and dense phase. In the model, the shape memory process is identified by two internal variables, the frozen deformation gradient and the dense phase volume fraction. To validate the model on describing shape memory effects, the model is implemented into finite element method by using a user-defined element (UEL) subroutine in ABAQUS. To verify the accuracy of the proposed UEL, the water-triggered shape memory effects in different recovery processes under different uniaxial loads are simulated. Furthermore, we investigate the water-triggered shape memory behaviors of a self-bending bilayer structure and a four-arms gripper structure by both experiments and simulation. Good agreement can be found from the comparison between the simulation results and the experimental results.
Soft materials possess magnificent properties which could be harnessed for different potential applications. Compared to other soft materials, hydrogels have some unique advantages which can be used in the shape deformation or shape transformation of structures. This paper aims to investigate the deformation mechanisms of hydrogel-based bi-material beam structures and study the non-uniform geometric effects on the shape transformation including programmable scroll and helical deformations. With a sloped thickness design, the structures could be transformed from an initial quasi-2D beam configuration into some other 2D self-scroll and 3D self-helical configurations. From the hydrogel material model, a modified deformation formula for bi-material beam structures based on the framework of the classical beam theory has been developed to predict the shape morphing behaviors. The relationship between the curvature and the mismatch strain is derived in its explicit form and the theoretical results are verified through several numerical simulations. Furthermore, experiments are carried out to demonstrate the design principles for reconfigurable bi-material beam structures and the experiments show that the structures tend to deform similarly to that predicted by the analytical models. The presented work could provide guidance for future applications of responsive hydrogel-based bi-material beam structures such as in soft actuators and soft robots.
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