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.