Facilely prepared layer-by-layer graphene membrane-based pressure sensor with high sensitivity and stability for smart wearable devices

doi:10.1016/j.jmst.2019.11.014

Abstract

Abstract:

With the prosperous development of artificial intelligence, medical diagnosis and electronic skins, wearable electronic devices have drawn much attention in our daily life. Flexible pressure sensors based on carbon materials with ultrahigh sensitivity, especially in a large pressure range regime are highly required in wearable applications. In this work, graphene membrane with a layer-by-layer structure has been successfully fabricated via a facile self-assembly and air-drying (SAAD) method. In the SAAD process, air-drying the self-assembled graphene hydrogels contributes to the uniform and compact layer structure in the obtained membranes. Owing to the excellent mechanical and electrical properties of graphene, the pressure sensor constructed by several layers of membranes exhibits high sensitivity (52.36 kPa^-1) and repeatability (short response and recovery time) in the loading pressure range of 0-50 kPa. Compared with most reported graphene-related pressure sensors, our device shows better sensitivity and wider applied pressure range. What’s more, we demonstrate it shows desired results in wearable applications for pulse monitoring, breathing detection as well as different intense motion recording such as walk, run and squat. It’s hoped that the facilely prepared layer-by-layer graphene membrane-based pressure sensors will have more potential to be used for smart wearable devices in the future.

Key words: Graphene membrane, Self-assembly, High sensitivity, Wearable devices, Human motions

Tao Liu, Caizhen Zhu, Wei Wu, Kai-Ning Liao, Xianjing Gong, Qijun Sun, Robert K.Y. Li. Facilely prepared layer-by-layer graphene membrane-based pressure sensor with high sensitivity and stability for smart wearable devices[J]. J. Mater. Sci. Technol., 2020, 45: 241-247.

Figures/Tables 7

Fig. 1. (a) The schematic of fabricating GM. SEM of the cross-section of the layer-by-layer structure of rGOM (b) and GM (c and d).

Fig. 2. FT-IR spectrum (a), XRD curves (b) and XPS full scan spectra (c) of GM, rGOM and GO. C 1s XPS spectra of (d) GO and (e) rGOM and (f) GM.

Fig. 3. (a) Schematic illustration displaying the structure of the n-GM pressure sensor. (b) Digital pictures of 5-GM pressure sensor formed by five layers of GMs. (The size of the pressure sensor is about 5 mm × 5 mm, and the thickness is about 2 mm.) (c) Response sensitivity comparison of 5-GM and 5-rGOM pressure sensor. (d) Resistance changes with pressure for GM sensor with 1, 5 and 10 layers of graphene membranes.

Fig. 4. (a) Response time under different rate (0.2 mm/s, 1.0 mm/s and 2.0 mm/s) of a loading pressure of 1 kPa. (b) Response and Recovery time of 5-GM pressure sensor under a loading pressure of 1 kPa. (c) Test of repeatability characteristics of 5-GM in 6000 s under a pressure of 1.63 kPa. The inserted figure is the enlarged image of the characteristics from 3570-3590 s. (d) Response test of 5-GM at different pressure (300 Pa, 1 kPa, 10 kPa and 20 kPa, respectively).

Table 1 Comparations of the properties with graphene-related work.

Work	Materials	Sensitivity	Pressure	Stability	Applications
[23]	3DGF-PDMS	gauge factor ~98.66	-	200 cycles	finger bending
[26]	SWCNT/alginate	0.176 ΔR/R₀ /kPa	10 Pa	3000 cycles	wrist pulse, neck muscle
[20]	CNT/graphene	19.8 kPa^-1	0.3 kPa	3500 cycles	acoustic vibrations, bending, torsion
[14]	tissue paper/GO	17.2 kPa^-1	0-20 kPa	3000 s	breath, human motions
[32]	graphene monoliths	161 kPa^-1	0-10 kPa	-	finger movement
[12]	multilayer graphene nanoplatelets	0.23 kPa^-1	0-70 kPa	-	wearable health care systems
[13]	graphene arrays	5.53 kPa^-1	0-1400 Pa	250 s	information transmission
[21]	graphene foam	-	-	-	ultrasonic waves
This work	graphene membranes	52.36 kPa^-1	0-50 kPa	6000 s	pulse, breath, jumping, walking, squatting

Fig. 5. (a) Sensor application for detection of a person’s wrist pulse. (b) Measuring signal of the wrist pulse response before exercise. (c) Wrist pulse response of a person after exercise. (d) Detection curve of the breathing rate.

Fig. 6. (a) The tested signal of real-time finger movement of bending and stretching. The inserted image shows a pulse sensor to monitor the movement. Response curves for the person’s movements of running (b), walking (c), squatting (d).

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