Patterned flexible graphene sensor via printing and interface assembly†

Tangyue Xue ^ab, Huige Yang ^a, Bin Shen ^c, Fengyu Li *^c, Meng Su ^b, Xiaotian Hu ^b, Wentao Liu ^a and Yanlin Song *^b
aSchool of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
^bKey Laboratory of Green Printing, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences (ICCAS), Beijing Engineering Research Center of Nanomaterials for Green Printing Technology, National Laboratory for Molecular Sciences (BNLMS), Beijing 100190, P. R. China. E-mail: ylsong@iccas.ac.cn
^cCollege of Chemistry and Materials Science, Jinan University, Guangzhou 510632, China. E-mail: lifengyu@jnu.edu.cn

First published on 24th April 2019

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Introduction

Flexible electric sensors have attracted widespread interests due to their wide applications in human activity monitoring such as pulse,¹ body temperature,² respiration,³ voice,⁴ facial expression,⁵ and body motion.⁶ Multiple factor correlation and linkage result in complexity between the human system and ergonomics. Therefore, flexible sensors with large-area, multi-response, multi-location sensing and complicated data analysis are necessary for human activity evaluation and personal health monitoring devices.^7–10 The introduction of multi-analysis technology enables efficient discrimination of various and complex data. Multi-analytical detection of complex environments pursues sensor components with high sensitivity, fast response, low detection limits and other sensing characteristics for high-resolution signal reception and efficient transmission.^11,12 However, multi-material interfacial coalescence and mechanical matching critically challenge the advancement of flexible devices and multi-function integration. Thus, the two-dimensional planar nanomaterial graphene, which has high electron mobility,¹³ superior thermal conductivity,¹⁴ and excellent flexible mechanical properties,^15,16 has attracted increasing attention. In addition, graphene can be processed into multidimensional assembly forms such as graphene fibers,¹⁷ graphene ribbons,¹⁸ graphene films,¹⁹ graphene papers²⁰ and graphene aerogels²¹ to meet different performance or application requirements. Therefore, graphene materials with various structural forms provide an effective approach for the development of flexible sensor devices.^17,22 Moreover, simplified and efficient preparation technology promotes the rapid development of sensor devices. Direct writing printing technology is one of the most efficient pattern preparation methods due to its advantages of simple operation, precise control, large-area complex pattern preparation, and no restrictions on ink type.^23–26 Various of graphene devices can be fabricated by direct writing printing techniques, for example, flexible electronic circuits,²⁷ supercapacitors,²⁸ and wearable sensors.²⁹

The reasonable macroscopic patterned design can further enhance the sensing characteristics such as sensitivity, stretchability and detection range of a sensor.³⁰ Bahamon et al. designed kirigami structures in graphene, which significantly reduced the initial stiff regime and achieved high stretchability for graphene.³¹ Lee et al. reported network patterned graphene strain sensors with higher sensitivity than the nonpatterned graphene sensors, which ensured a wide strain detection range and the discrimination of human body subtle motion.³² Fan et al. enhanced the tensile properties of flexible sensor devices by conformal patterning design (Von Koch curve, Piano curve, Hilbert curve, Moore curve, etc.) and expanded the application range of flexible electronics.³³ Therefore, the combination of suitable sensing materials, simplified preparation techniques and rational patterned design with sensing preparation technology will promote the breakthrough development of flexible sensing devices.

In this work, we present an integrated flexible graphene sensor consisting of different graphene dense-packing and porous microstructures with different macroscopic patterns of wavy and serpentine. The porous serpentine pattern of the graphene aerogels result in a multi-dimensional deformation response, while the layered stacked dense-packing graphene ribbon provide good conductivity for efficient signal transmission. The sensors were fabricated via the synergistic combination of various macro-patterns and different microstructures with homo-graphene material, which reduced the interface coalescence defects and contact resistance. The designed high sensitivity, rapid response, and excellent durability multi-resolution graphene sensor could achieve multi-location recognition and complicated arthrosis motion monitoring, and thus have promising applications in diverse human motion monitoring and analysis.

Results and discussion

Graphene oxide (GO) was synthesized via a modified Hummers' method and the optimum concentration of graphene oxide ink was obtained based on a previous study (20 mg mL⁻¹) using the solvent evaporation method (see Experimental section for details).^34,35 The scanning electron microscopy (SEM) (Fig. S1, ESI†) and atomic force microscope (AFM) (Fig. S2, ESI†) characterization tests showed that graphene oxide had a size in the range of 20 to 40 μm and a thickness of approximately 0.98 nm. Using a direct writing printing technique, a symmetrical integrated pattern was printed with graphene oxide ink to produce a flexible graphene sensor. A schematic illustration of the fabrication of the graphene-based integrated patterned sensor is shown in Fig. 1. Firstly, graphene oxide ink was printed in a wavy line and dried at room temperature. Subsequently, serpentine patterns were printed by extruding the GO ink and they were processed by vacuum freeze-drying to form GO aerogels. Then, the printed pattern was reduced by chemical reduction by hydroiodic acid (HI). The Raman spectra (Fig. S3, ESI†) and X-ray diffraction (XRD) curves (Fig. S4, ESI†) before and after reduction indicated that the printed pattern was fully reduced. The electrical test results showed that the electrical conductivity of the graphene ribbon was 4.21 × 10⁴ S m⁻¹, while the electrical conductivity of the graphene aerogel was 3.43 × 10² S m⁻¹. This result indicates that the graphene device after reduction has excellent conductivity, and the conductivity of the graphene ribbon was higher than that of the graphene aerogel. Finally, the sensor was packaged with poly(di-methyl siloxane) (PDMS) and cured for 3 h at 85 °C.

As shown in Fig. 2a and b, the graphene ribbon has a flat surface with slight wrinkles due to shrinkage during the natural drying process. It can be seen from the cross-sectional image (Fig. 2c) that the graphene ribbon has a closely packed layered structure with a thickness about 16 μm. The direct writing printing method provided a shear force to induce the orientation of the graphene oxide, and subsequently the surface tension during the natural drying process promoted the formation of a layered close-packed structure.³⁵ As shown in Fig. 2d and e, the graphene aerogel has a disordered porous structure with a pore diameter ranging from tens of micrometers to eighty micrometers. The ice stencil manipulated the graphene sheets to assemble into a three-dimensional porous structure during the freeze-drying process.^34,36 It can be seen from the cross-section image (Fig. 2f) that the graphene aerogel has a dense porous skeleton, which gives it excellent in sensitivity to mechanical deformation.

The sensitivity of the graphene ribbons (linear and wavy lines) and graphene aerogels (linear, wavy, and serpentine lines) (Fig. S5, ESI†) under different mechanical tests was quantitatively investigated, which was calculated using the relative change in the rate of resistance (ΔR/R₀, where, ΔR and R₀ represent the amount of change in resistance and the initial resistance, respectively). As shown in Fig. 3a, when the pressure reached 39.7 kPa, the relative resistance change rate of the linear and wavy graphene ribbons was 0.35% and 0.42%, respectively. Simultaneously, wavy line and the serpentine line patterned graphene aerogels were also prepared and compared with the linear graphene aerogel. The resistance change sequence of the three patterned graphene aerogels showed that the serpentine line (2.79%) was larger than the wavy line (2.38%), and the straight line (1.92%) resistance variation was the smallest. Fig. 3b displays the bending testing when the bending angle increased from 0° to 180°. The relative resistance change rate of the three patterned graphene aerogels increased significantly, while the resistance of the graphene ribbon remained stable. As can be seen from Fig. 3c, when the twist angle reached 160 rad mm⁻¹, the maximum relative resistance change rate of the serpentine line patterned graphene aerogel reached up to 28.99%, and the minimum resistance change rate of the graphene ribbon was about 1.51%. Moreover, as presented in Fig. 3d, the maximum relative resistance change rate of the serpentine patterned graphene aerogel was up to 114.69% at a distance of 10 mm. Additionally, when the tensile strain increased from 0 to 100%, the relative resistance change of the serpentine graphene aerogel was about 889.91%, and that of the linear and wavy graphene ribbons was close to zero and it broke at 18% and 27% strain, respectively (Fig. 3e). The results indicated that the wavy graphene ribbon could bear larger tensile strain than the linear graphene ribbon. This is because the wavy graphene ribbon has more corrugated structure to cushion and withstand a certain degree of strain, which increased the reversibly stretchable property of the device.³⁷

To further assess the stability of the graphene ribbons and aerogels, they were tested for durability with a bending angle of 90° (Fig. 3f). After 1000 cycles of testing, the relative resistance of the graphene ribbons and graphene aerogels stabilized despite their different microstructures and macroscopic patterns, which proved they have good cycle stability. Compared with the densely layered graphene ribbons, the resistance change of the microporous graphene aerogels was significant. The sensitivity test results for the linear, wavy, and serpentine patterned graphene aerogels indicated that the resistance change rate of the serpentine line was the largest, followed by the wavy line, and the linear resistance change rate was the smallest. All of the mechanical test results indicated that they have excellent mechanical performances and cycle stability. Compared with previous reports (Table S1, ESI†), the electrical conductivity of the graphene ribbons and the strain range of the graphene aerogels via direct writing printing in this work are similar or better.

Based on the significant differences in the electrical sensitivity characteristics of the graphene ribbons and graphene aerogels with different microstructures and graphene aerogels with different macroscopic patterns, we designed a symmetrical integrated patterned sensor device using the wavy graphene ribbons with good electrical stability and serpentine graphene aerogels with excellent electrical sensitivity to achieve multi-directional tensile bending strain. From the perspective of ergonomics, the graphene aerogels and graphene ribbons were combined to design a symmetrical multi-direction stretchable and flexible sensitive strain device to realize the analysis of different positions of human joints. The integrated patterned sensor components have a clear division of labor, where the elastic graphene aerogel sensed the strain and transmitted the signal to the excellent conductivity graphene ribbon. The graphene ribbon further transmitted the strain information to ensure efficient signal transmission. To demonstrate the electrical response and signal transmission characteristics of the patterned graphene sensors, the resistance changes of the patterned graphene ribbon sensor, the patterned graphene aerogel sensor and the integrated patterned graphene ribbon and graphene aerogel sensor under strain were monitored in real time. The real-time resistance change of the patterned sensors caused by external stimuli was recorded by a multi-channel electronic data logger. The 4 channel electrodes of the multi-channel recorder were connected to the four pairs of electrodes of the integrated patterned graphene sensor with alligator clip wires, and the resistance changes of the 4 channels under force point were simultaneously monitored. As shown in Fig. 4a and Fig. S6a, c (ESI†), the force was intermittently applied to the P1 point, and the resistance changes of the 4 channels were observed simultaneously. Fig. 4b and Fig. S6b, d (ESI†) show the real-time relative resistance change rate of the 4 channels, which indicated that the patterned graphene sensors with different microstructures have different resistance responses. The resistance changes of the patterned graphene aerogel sensor and the integrated patterned graphene ribbon and graphene aerogel sensor in C1–C4 channels were obvious, while the resistance change of the patterned graphene ribbon sensor was almost zero, which indicated that the graphene aerogel has electrical sensitivity and the graphene ribbon has electrical stability. The real-time resistance changes of the patterned graphene aerogel sensor and the integrated patterned graphene ribbon and graphene aerogel sensor demonstrated that the resistance variation in the C1 channel was almost the same, while the resistance variation of the integrated patterned graphene ribbon and graphene aerogel sensor in the C2–C4 channels was bigger than the patterned graphene aerogel sensor, which showed that the electrical signal transmission capability due to the excellent electrical conductivity and stability of the graphene ribbon was better than that for the graphene aerogel. For the integrated patterned graphene ribbon and graphene aerogel sensor, the order of the resistance change rate of the 4 channels was as follows: C1 > C3 > C2 > C4, which is because the high-sensitivity aerogel at the C1 channel was subjected to a large resistance change after being pressed by the P1 point, and the highly conductive graphene ribbon transmitted the electrical signal at the C1 channel to the adjacent C2, C3, and C4 channels, which caused a corresponding change in resistance. In addition, the degree of resistance change is related to the distance of the force point, i.e., the channel near the force point has significant resistance changes, and that further away from the force point has relative smaller resistance changes.

Statistical principal component analysis (PCA), hierarchical clustering analysis (HCA) and linear discriminant analysis (LDA) were used to evaluate the spatial resolution of the integrated patterned graphene sensor.^5,38 As shown in Fig. 4a, four representative points of different orientations and distances (P1–P4) were selected for stimulation. From the 3D PCA chart in Fig. 4c, it can be seen that the test results of the same points were clearly clustered, and the classification between different points were clear (Fig. S7, ESI†). The results of the HCA data based on cluster similarity are shown in Fig. 4d, and the similarity of adjacent locations was obtained, such as P1, P2 and P4. Moreover, the LDA data showed that the Jackknifed classification matrix achieved a clear classification of P1–P4 up to 97% and the three factors of the canonical scores plot were effectively distinguished (Table S2 and Fig. S8, ESI†). Further selection of random points indicated that the multi-structure graphene sensors have different distance and position distribution recognition ability, which provides a new direction for the development of highly sensitive electronic skin.

From the above analysis, the obtained graphene sensor has good mechanical properties, flexibility and sensitivity, and can be widely used in wearable electronics. To further demonstrate the potential of the integrated graphene sensor with densely-packed layered and porous microstructures, the multi-channel motion recognition analysis of the patterned graphene sensors was explored. As shown in Fig. 5a, the sensor was attached to the wrist and the electronic data recorder performed multi-channel detection, which enabled real-time monitoring of human motion changes. The experiment selected seven different wrist movements, namely relax as control, fetch, throw, beckon, grasp, wave and knock. The 3D chart displayed the resistance changes of seven different motion gestures when the sensor was worn on the human wrist and monitored by an electronic data logger (Fig. S9, ESI†). The results indicated that the sensor responds differently with the movements of the wrist joint in every gesture.

Combining the resistance changes of the seven different gestures, 3D PCA and HCA were carries to assess the spatial resolution of the sensor. Fig. 5b shows the PCA data of the seven wrist movements. It can be seen that the seven movements were clearly classified (Fig. S10, ESI†). The general distribution of all the movements was in three directions: external wrist bending, inner wrist bending and side wrist bending. For example, movements involving the external wrist bending referred to fetch, throw, and beckon, in which the degree of wrist bending gradually increased. Movements involving the inner wrist bending are usually large muscle tension of certain gestures, such as grasp. Movements involving the side wrist bending is usually the muscles relaxed of certain gestures, such as wave and knock. In fact, the expression of each wrist movement of a person is accomplished by the interaction of the external wrist bending, the inner wrist bending, and the side wrist bending. Fig. 5c shows the HCA diagram of wrist movement changes based on different feature similarities, showing the trend of wrist movement changes, which verifies the analysis results of the PCA diagram. The results showed that the graphene flexible sensor could accurately test the movement of the wrist joint. In addition, the jackknifed classification matrix and the canonical scores plot of the LDA analysis results indicated that the integrated flexible sensors could completely and clearly distinguish seven different wrist movements. (Table S3 and Fig. S11, ESI†). By comprehensively analyzing the above results, the sensor could accurately detect the movement of the wrist. Thus, it can be used as a wearable device to monitor human body movements in real time, which greatly promotes the movement standard monitoring and personal health assessment of athletes.

Conclusions

In summary, considering the complexity of human motion systems and ergonomics, we integrated different graphene dense-packing and porous microstructures with different macroscopic wavy and serpentine patterns to achieve a multi-functional, multi-resolution flexible graphene sensor. The porous serpentine pattern of the graphene aerogels responded quickly to deformations (under 100% strain conditions, the aerogel relative resistance change was 889.91%). The layered stacked dense-packing graphene ribbon exhibited excellent conductivity (4.21 × 10⁴ S m⁻¹) for fast signal transmission, which enabled the integrated patterning of the graphene sensors for precise multi-location recognition and complicated arthrosis motion monitoring. Benefiting from the wearability of multi-functional sensing, the integrated patterned graphene sensor demonstrates broad application prospects in electronic skin, wearable devices, human-computer interaction and health monitoring equipment.

Experimental

Preparation of graphene oxide ink

3 g expanded graphite, 2.5 g phosphorus pentoxide (P₂O₅) and 2.5 g potassium peroxydisulfate (K₂S₂O₈) were added to 40 mL of concentrated sulfuric acid (98%, H₂SO₄), and the mixture was stirred at 80 °C for 5 h. Then, the mixture was cooled to room temperature and diluted in 400 mL of ice water. Next, it was filtered with 2 L of distilled water and dried at room temperature for 2 days to obtain pre-oxidized graphite. In an ice water bath, the pre-oxidized graphite was added to 120 mL H₂SO₄, and 9 g potassium permanganate (KMnO₄) was added slowly under stirring. Then the mixture was maintained at 35 °C for 2 h, and diluted in 1 L ice water after cooling to room temperature. 15 mL hydrogen peroxide (25%, H₂O₂) was added dropwise to obtain a bright yellow mixture. After the mixture was allowed to stand for 12 h, the supernatant was poured out, and then the remaining precipitate was washed three times with deionized water and hydrochloric acid solution (10%, HCl), respectively. Finally, the remaining precipitate was dialyzed until pH = 7. The graphene oxide dispersion was concentrated by a solvent evaporation method in a 60 °C water bath to obtain graphene oxide ink with a concentration of 20 mg mL⁻¹.

Sensor fabrication

The GO ink was placed in 3 cm³ syringe connected to a needle having an inner diameter of 200 μm and printed by a multi-axis dispensing system (2400, EFD). Wavy lines were printed on plasma-treated PET film in a pre-designed pattern and allowed to dry naturally at room temperature. Then, serpentine lines were printed on the dry patterns, frozen with liquid nitrogen and then further frozen in a refrigerator for 10 h. The pattern was placed in a freeze dryer and vacuum freeze dried for 3 h. Next, the dried pattern was immersed in a hydriodic acid (15%, HI) solution, and heated in an oven at 95 °C for 3 h. After cooling to room temperature, the sample was washed three times with deionized water and frozen again in a refrigerator for 10 h. Then, the sample was vacuum dried for another 3 h. Next a copper electrode was pasted on the end of the sample, and a small amount of silver conductive paste was added to strengthen the connection. Polydimethylsiloxane (PDMS) precursor and the cross-linking agent were mixed at a weight ratio of 10:1, stirred until homogeneously mixed, and the bubbles were removed by centrifugation at 5000 rpm for 10 min. Finally, the PDMS mixture was poured onto the printed pattern (except the electrodes), naturally leveled for 30 min and placed in an 85 °C oven for curing to obtain a packaged complete patterned sensor.

Characterization

The morphology of the samples was characterized by field emission scanning electron microscopy (FE-SEM, JEOL 7500 at 5 kV, 10 μA), atomic force microscope (AFM, Fastscan Bio in the tapping mode), Raman spectroscopy (LabRAM HR Evolution, 532 nm laser wavelength), and X-ray diffraction (XRD, Empyrean at 40 kV, λ = 1.5406 Å). The resistances of the samples were measured using the four-probe method (Keithley 4200 Semiconductor Characterization System) with a resistance meter (HIOKI RM3545). Strain was applied using home-made stretching equipment. At least 5 samples were tested for each data point and the average was used for plotting. Multi-analysis data was obtained by real-time monitoring with a multi-channel recorder (HIOKI, LR8400).

Data analysis

Principal component analysis (PCA), hierarchical clustering analysis (HCA) and linear discriminant analysis (LDA) data were analyzed using Minitab v16.1.1.0, which can comprehensively and correctly evaluate and analyze the response of the integrated patterned graphene sensors. When stimulating different positions and making different gestures, the multi-channel data logger could read five effective values such as the peak value, the intermediate point and the lowest point of the eigenvalues in the resistance curve in real time. Each point and movement was tested 9 times and 20 variables were obtained to describe the electrical response of the integrated patterned sensor.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

F. Li and Y. L. Song thank the National Nature Science Foundation of China (Grant No. 21874056, 51773206, 51473172, and 51473173), the National Key R&D Program of China (Grant No. 2018YFA0208501, 2016YFB0401603, 2016YFC1100502 and 2016YFB0401100), and Chinese Academy of Sciences and K. C. Wong Education Foundation.

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Footnote

† Electronic supplementary information (ESI) available. See DOI: 10.1039/c9tc00910h

This journal is © The Royal Society of Chemistry 2019