2.4. Piezoelectric self-powered ion skin (P-iskin)
A self-powered piezoelectric ionic skin (P-iskin) was developed based on
the ionic piezoelectric effect for human health monitoring. The ion
piezoelectric effect of the sensor is caused by the migration of cations
and anions in the ion film layer of the sensor when stimulated by
external pressure (Figure 5 a).[48,54,55]The piezoelectric properties were verified by piezoresponse force
microscopy (PFM) mode of atomic force microscope (AFM) (Figure
5b).[56,57] A strategically placed layer of copper
tape has been introduced between the sample and the probe tip for
two-fold purpose: i) it mitigates potential damage to the sample by
curtailing high current density; ii) the employment of copper tape acts
as an effective isolating barrier to avoid structural damage to flexible
gels caused by probe tip. Therefore, Figure 5c is an AFM image of the
copper strip surface and not of any other sample. The phase diagrams
after loading forward (-5 V) and reverse (+5 V) bias voltages to the
sample are given in the red curve (Figure 5d), clear hysteresis lines
can be noticed from it, and the 180° reversal of the phase signal also
reflects the existence of ferroelectric polarization in the ionogel. And
the obvious butterfly curve can be seen from its amplitude graph as the
blue curve (Figure 5d), after bias is added to the sample,
voltage-induced deformation of the sample leads to cyclic vibrations on
the sample surface, which are delivered to the probe tip and sensitively
read out with the help of a lock-in amplifier
(LIA).[56] This proves in reverse that the sample
can convert the pressure signal into an electrical signal and reflect it
in the form of a voltage signal under a certain pressure.
Next, we perform real-time detection and collection of force electrical
signals through a customized set of linear motors, force transducers,
high-precision digital source meters, and computer systems (the inset in
Figure 5e). With a pressure of approximately 5 N provided by a linear
stepper motor, the ion skin exhibits stable piezoelectric
responsiveness. The results show that a voltage of approximately 250 mV
can be output, with each waveform in the curve represents a loading /
unloading cycle (Figure 5e). It is worth noting that the electrokinetic
response curve exhibits an initial voltage in the absence of any
pressure stimulation, which can be attributed to the abundance of free
cations within the ionogel and the non-uniform distribution of positive
and negative ions, even if the overall ion balance within the gel is
relatively maintained.
Consequently, the ionogel demonstrates promising potential as a
self-powered piezoelectric pressure sensor, rendering it suitable for
monitoring human physiological signals. It effectively converts
mechanical energy generated during human activities into electrical
energy, which can be further translated into discernible electrical
signals. An advanced ion-piezoelectric self-drive wearable health
monitoring sensor (P-iskin) was developed, whose device structure
parallels that of C-iskin (see the Figure S16 for its corresponding
equivalent circuit diagram). The piezoelectric potential generated by
the piezoelectric ion effect is regarded as the internal power supply to
provide guarantee for the normal operation of the device.
Firstly, the P-iskin was employed to monitor the recovery motion
generated by the arm activity of a volunteer who had undergone surgery
for a fractured right arm three months ago. The volunteer executed a
series of successive flexion-extension-flexion movements, with the
corresponding output electrical signal curve displayed (Figure 5f). Each
elevated peak observed in the curve represents a complete cycle, wherein
a slight upward spike is noticeable, which can be attributed to muscle
tremors that occur due to incomplete recovery of the bones following the
surgical intervention. As a control measure, the P-iskin device was
utilized to monitor the activity of the healthy left elbow joint of the
volunteer. And it can be observed that the electrical signal curve for
each action cycle exhibits exceptional smoothness, devoid of any
spurious peaks (Figure 5g).
In addition, by placing P-iskin on the stationary elbow and performing
clenched and relaxed fist motions with the right and left hand
respectively (Figure S17a-b), it is used to monitor the recovery
progress of the injured arm. It is evident from the action cycle of the
right hand that muscle tremor persists in the injured hand and peaks are
observed, whereas the left hand presents a relatively smooth pattern
without any false peaks. P-iskin can be placed on skin scars and further
used to monitor the recovery of injured skin growth in human body, which
demonstrates the superior ability of P-iskin to monitor motion in normal
skin and scarred skin. The above research demonstrates that the device
can provide certain assistance for medical rehabilitation and guidance
for building a wearable health monitoring system and family
self-reconstruction, and avoid secondary harm to oneself during the
reconstruction process.
With the rapid advancements in technology, wearable robots are
undergoing remarkable progress and becoming increasingly prevalent in
our daily lives. Our research has revealed that P-iskin holds immense
promise in the realm of robotic action recognition (Figure S18). We
securely attached the P-iskin to the robot’s feet and proceeded to
execute a series of maneuvers, including front and back somersaults,
lateral sliding, and walking. Figure S18a presents the output signal
diagram capturing the robot’s somersaults. The illustration below
displays the action signal detected by the P-iskin throughout an action
cycle. Notably distinct, the variations in the output signal enable
accurate judgment of the corresponding actions based on these
discernible patterns. Figure S18b-c exhibits the step signals of the
robot’s lateral sliding and walking movements. Each peak corresponds to
a distinct walking step, and the consistent shape of these peaks
signifies the remarkable stability of P-iskin. This implies its
potential for reliable recognition of the robot’s walking states.