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epoxy flexible strain sensors
Accepted Manuscript Title: Health monitoring for composite materials with high linear and sensitivity GnPs/epoxy flexible Strain sensors Authors: Shaowei Lu, Caijiao Tian, Xiaoqiang Wang, Duo Chen, Keming Ma, Jingsong Leng, Lu Zhang PII: DOI: Reference:
S0924-4247(17)31267-0 https://doi.org/10.1016/j.sna.2017.10.047 SNA 10413
To appear in:
Sensors and Actuators A
Received date: Revised date: Accepted date:
11-7-2017 1-10-2017 17-10-2017
Please cite this article as: Shaowei Lu, Caijiao Tian, Xiaoqiang Wang, Duo Chen, Keming Ma, Jingsong Leng, Lu Zhang, Health monitoring for composite materials with high linear and sensitivity GnPs/epoxy flexible Strain sensors, Sensors and Actuators: A Physical https://doi.org/10.1016/j.sna.2017.10.047 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Health monitoring for composite materials with high linear and sensitivity GnPs/epoxy flexible Strain sensors Shaowei Lua, *, Caijiao Tian a, Xiaoqiang Wang, a Duo Chen a, Keming Ma a, Jingsong Leng b, Lu Zhang a a
Faculty of Aerospace Engineering, Shenyang Aerospace University, Shenyang
110136, China b
Institute of Composite Materials and Structures, School of Astronautics, Harbin
Institute of Technology, Harbin 10213, Chain The graphene platelets (GnPs)/epoxy flexible sensor with high sensitivity and linear can be used to monitor the deformation and damage in structural composites. The dispersion of GnPs in an epoxy matrix was better improved by optimizing ultrasonic time and using the ball mill mixing process. The GnPs/epoxy mixture exhibited relatively low percolation threshold of 0.76 vol. % and its model was analyzed. In this paper, GnPs/epoxy mixture with GnPs loading of 1.00vol.% was selected as damage detecting and strain sensor, and the characteristics of sensor was demonstrated via various mechanical tests. The monotonic tensile results suggested that there are two different linear change sensing stages, (05000 με ) and (500016700 με ), and it exhibits relatively high gauge factor (GF) of 6.5 and 11.6 in different sensing stages. The linear relationship and sensitivity were recoverable and stable upon loading and unloading tensile test. And the gauge factors upon loading and unloading test agree with the GF in monotonic tensile test. The GnPs/epoxy flexible sensor show promising applications for the damage monitoring of structural 1
composite in the field of aerospace. Keywords: graphene platelets(GnPs); percolation threshold; Sensor; Strain; Piezoresistive Corresponding author: Shaowei Lu E-mail: [email protected] Tel/Fax: +86-024-8972 3713
Graphical abstract
Coupon coated GnPs/epoxy flexible sensor
Analytical conductive model
2
Monotonic tensile and loading-unloading test
Highlights
The dispersion of GnPs in an epoxy matrix was better solved by optimizing ultrasonic time and using the ball mill mixing process. The GnPs/epoxy mixture exhibited relatively low percolation threshold of 0.76 vol. %.
Its analysis model was analyzed.
3.The monotonic tensile results suggested that there are two different linear change sensing stages, (05000 με ) and (500016700 με ), and it exhibits relatively high gauge factor (GF) of 6.5 and 11.6 in different sensing stages
The linear relationship and sensitivity were recoverable and stable upon loading and unloading tensile test. And the gauge factors upon loading and unloading test agree with the GF in monotonic tensile test.
1. Introduction The increased application of structural composites in aerospace, energy and 3
infrastructure, among other areas, has led to a concern about the reliability of these materials. [1-3]. These structures are often exposed to a variety of conditions, including impact, excessive loading, fatigue, material defects, environmental deterioration, manufacturing shortcomings and fluid penetration. The onset of local damage in structures, such as delamination, cracking and fastener loosening, can often be difficult to detect and has long term implications on the performance of the composite structure. Non-destructive techniques, such as X-ray, ultrasonic or eddy current inspection can offer the capability of periodical inspection on local damage, but the structures may require disassembly for inspection. Nevertheless, they cannot provide structural information in real-time conditions[4-7]. As a result, it is necessary to develop innovative techniques to monitor the state of damage in composite structures. Structural health monitoring (SHM) seeks to provide ongoing monitoring of a structure’s integrity, minimizing the need for programmed inspections and allowing maintenance to be need-driven, rather than usage-driven. Currently emerging SHM approaches include the use of strain gages and fiber optic sensors to measure strain, vibration, harmonic frequencies, or other parameters which can be used to assess the health of the structure by comparing these values to a known healthy dataset. Strain sensors are widely used in various fields of engineering for monitoring and damage detecting of critical infrastructure. But they provide sensing only near the sensor itself; therefore, they are limited by the requirement to detect damage only in designated directions and locations. When damage occur at other unanticipated 4
regions, it may be useless. Optical fibers have been used as embedded SHM sensors in an attempt to overcome the disadvantages of strain gauges, but their adoption has not been as wide spread due to a few reasons. The first is that expensive equipment is required for instrumentation. Second, optical fiber sensors are often insensitive to cracks propagating parallel to the optical fiber orientation. Embedded optical fibers sensors also may act as defects in the composite structure promoting crack initiation and damage to the composite parts due to the fact that the optical fiber diameter is larger than the diameter of the reinforcement fibers.[7-12] The discovery of carbon nanotubes (CNTs) and graphenes (Gns) has generated keen interest among researchers to develop CNT-based or Gn-based sensors for many applications. And the piezoresistive nature of CNT and Gns has been observed clearly over the past few years[13, 14]. Several studies have focused on piezoresistive polymers made by dispersing CNTs into a polymer to form a conductive matrix[1519]. This conductive matrix can then be molded into a thin film, a bulk composite shape, a ribbon or any other shape desired. Higher sensitivity has been observed in these novel sensors, at least at a macro-scale. compared with CNTs, Gns is in general easier to prepare and less costly for mass production[20]. From this perspective, the use of polymer/graphite composites featuring good process ability and low-cost materials is very promising. Shahin[21] et al. has investigated the effect of strain rate on mechanical behavior of epoxy reinforced with GnPs. R. Moriche et al. [22] carried out analyses the strain sensor capability of nanocomposites reinforced with different GnPs.
Detailed toughening mechanisms of the graphene in both graphene/epoxy and 5
carbon fiber/epoxy composites were studied by Xusheng Du et al.[23]. Some researchers reported Gns/epoxy composite-based strain gauges in the literature[13, 24-28]. These strain sensors were observed to have a higher gauge factor. But the major drawback of those Gns/polymer composite sensors was that the Graphene fractions they selected were high due to they did not discuss the percolation threshold in detail or the percolation threshold was high. Although Graphene content of Xusheng Du et al. [23] is relatively low, the linear fit of the graphene sensor given in the literature was not very satisfactory. Therefore, this writer seeks to build upon a prior study into the sensing ability of Gns/epoxy sensor by testing its response to mechanical loading. To further examine the behavior and sensitivity, determining the optimum number of Gns that may achieve the highest sensitivity and the analytical model are major process of this study. 2. Experimental section 2.1. Preparation and characterization of GnPs/epoxy conductive mixtures Graphene platelets (GnPs) were fabricated according to a brief “intercalationexpansion-stripping” procedure. This produced GnPs with average number of layers (5~6), an average thickness of (< 3 nm) and film conductivity (>700 s/cm). GnPs/epoxy conductive mixtures with the graphene platelet concentrations from 0.32 vol. % to 2.64 vol. % was prepared as follows: The GnPs were first dispersed in acetone (E-51, Yanshan Co., Ltd., China) by sonication under 30℃ for 2 h. The GnPs/acetone solutions with different GnPs weight fractions were then mixed with the epoxy by sonication for 1 h. The mixture was further processed using a planetary 6
ball mill (PM 400, Retsch) equipped with four steel containers (125 mL) and three different types of zirconia balls (3, 6, 11 mm in diameter and the corresponding 250, 50, 10 in number). The high shear stress applied by the milling impact can break up the agglomerates of GnPs and improve their exfoliations to generate the highly dispersed GnPs/epoxy dispersions. To avoid any intense shock stress which could destroy the GnPs structures, the rotating tray was controlled at a low speed of 200 rpm to ensure that the shear stress is dominant. After adding a little curing agent, the mixture was subjected to stir and shock to remove the acetone. Subsequently the conductive mixtures were coated on the grasses and then solidified to films in an oven under 120℃ for 1h. The electrical conductivity of the films was examined with a commercial four-probe resistance measuring apparatus (RTS-8,4 PROBES TECH Co., Ltd., China). The position is shown in Fig .1.
Fig. 1 Schematic view of the experiment system
2.2. Fabrication of composite surface coated with GnPs /epoxy flexible sensor In this section, a glass/epoxy unidirectional prepreg (6501/G15000/33%, Weihai Guangwei composites Co., Ltd., China) was used to fabricate a laminate specimen 7
with an area of 250mm*200mm. The specimen was stacked with [O16 ] prepregs on a steel moulid, which was previously treated with demouliding agent, then enclosed in a vacuum bag system and heated with the temperature first ramped from room temperature to 120℃ and then maintained at 120℃ for 2h in an oven. The products were cut into rectangular coupons (250mm×25mm). The thickness of all coupons was about 1mm. The 1.00 vol. % GnPs/epoxy conductive mixture was coated on the coupons solidified at 120℃ for 1h. Spacing and thickness between the conductive mixtures were kept as consistent as possible. The geometry and dimensions of composite coupon coated with GnPs/epoxy flexible sensor is shown in Fig.2. After GnPs/epoxy mixture was coated on the coupons surface and solidified, silver paste electrodes were placed on the edge of the films surface to minimize the contact resistance. Non-conducting tabs with a length of 30 mm were bonded to the coupon ends. This was done to reduce the stress concentration due to gripping during tensile testing. Finally, in order to get independent real-time and accurate strain measurements for comparison and GnPs/epoxy sensor calibration, an external commercial metal foil strain gauge (Vishay Micro-Measurements 250LW) was mounted on the outer surface of each composite coupon
8
Fig. 2 Experimental setup and specimen characteristics for piezoresistive characterization in tensile tests.
2.3. Characterization To evaluate the sensing performance of the GnPs/epoxy flexible sensor, different modes of tensile loading were performed using a INSTRON-8801 test system (Fig. 2) in a controlled laboratory environment. These tests included: (1) monotonic tensile tests and (2) loading-unloading tensile tests. The force data was recorded by the testing machine load cell while the strain was recorded via the metal foil strain gauge. The electrical resistance of the GnPs/epoxy mixture was recorded by an FLUKE 2638A mustimeter. It is essential to point out that both the strain gauge data acquisition software and the multimeter program used for recording the GnPs/epoxy film resistance change were started simultaneously with a data collection frequency to guarantee precise correlation of strain and resistance data. Monotonic tensile tests were performed with a fixed displacement speed of 2 mm/min. For the incremental tensile tests, the coupons were subjected to multiple loading-unloading cycles. The test fixture returned to the zero-loading state at the end of each cycle. Generally, three 9
samples of each type were tested monotonically while two samples were tested in progressive loading and cyclic modes. While the failure strains of the composites varied within an acceptable range (which is always the case for materials testing) the shape of the normalized resistance change curves were consistent among sample groups. 2.4 Analytical model When GnPs are thoroughly dispersed in epoxy materials, GnPs should have three categories of junctions: (i) complete contact by overlapping (ii) tunneling junction within a certain distance (below 3 nm), and (iii) disconnection (over 3 nm) caused by microcracks [14]. These GnPs connect with each other forming conductive networks, the resistance would be influenced by the contact resistance between GnPs. A hypothesis made herein was that upon mechanical loading, any deformation would change the contact resistance between GnPs and thus the film resistance, resulting in strong piezoresistivity. The authors think that the relative contribution of the inherent piezoresistive behavior, discrete damage accumulation is considered significant before the percolation threshold. The conductivity and the complex internal structure restricts to make them as sensors. While after the percolation threshold, the GnPs connect with each other by overlapping or/and tunneling, the electrical resistance increases mostly due to touching GnPs contact area change. This physical picture is simplified as a model for evaluating the resistance, as schematically shown in Fig. 3. It is hypothesized that GnPs/epoxy sensors can enable higher sensitivity and damage 10
detection originates from the disconnection and tunneling effect between GnPs, where slight strain causes microcracks and disconnection between GnPs, both of which dramatically increase the resistance. By contrast, stretchable conductors benefit from the densely stacked and overlapped GnPs. Near the percolation threshold, GnPs in epoxy would be able to arrange themselves to maintain connection with each other by reducing the overlap area. Therefore, the highest sensitivity in this sensor is obtained near the percolation threshold[13, 27]. Note that, there may be overlapping among GnPs to a certain extent in our numerical model. This overlapping may result in possible errors in the following stage where we evaluate the pizoresistivity of sensors, since the possibility of breakup of the GnPs network may be slightly underestimated for serious penetration states among GnPs. However, when the volume fraction of GnPs is low near the percolation threshold, GnPs in epoxy would be able to arrange themselves to maintain connection with each other by reducing the overlap area, the error caused by this overlapping is negligible if we explore this problem qualitatively. According to the transfer length method, the tunneling resistance between two adjacent GnPs can be determined by the Simmons' formula [29, 30] 𝑅𝑡 =
ℎ2 𝑤
4𝜋𝑤 exp ( √2𝑚𝜑) (𝐸𝑞. 1) ℎ 𝑎𝑒 2 √2𝑚𝜑
where h is the Planck' s constant, w is the tunnel width, a is the cross-sectional area of the tunnel, e is the single electron charge, m is the mass of electron, and 𝜑 is the height of potential barrier between adjacent platelets. The continuous conductive pathways were modeled as parallel conductive wires (effective conductive pathways) which were composed of the effect GnPs electrically 11
connected in series. The composite consists of 𝐿 number of the effective conductive pathways and each effective conductive pathway is formed by N number of effective GnPs. The composite resistance R can be expressed in terms of the electrical resistance of the effective GnP 𝑅𝑡 . 𝑅=
𝐿 𝐿 ℎ2 𝑤 4𝜋𝑤 𝑅𝑡 = exp ( √2𝑚𝜑) (𝐸𝑞. 2) 𝑁 𝑁 𝑎𝑒 2 √2𝑚𝜑 ℎ
As shown in both equations, the tunneling resistance mainly depends on the tunnel width.
Fig. 3 Modelling of distribution of graphene platelets in epoxy (a, before the percolation threshold, b after the percolation threshold)
3.Results and discussion 3.1 Characterization of GnPs/epoxy mixture The dispersion of GnPs in epoxy is most challenging problem to fabricate composite. The mechanical and electrical properties of the composite significantly depend on the dispersion process. The optimized dispersion process can also improve the percolation level[31]. The dispersion of GnPs in an epoxy matrix was better 12
solved by optimizing ultrasonic time and using the ball mill mixing process. The morphologies of GnPs/epoxy nanocomposites are evaluated by SEM observations. As shown in Fig. 4 for a fractured surface of a sample with 1.00 vol. % GnPs loading, some GnPs are partially embedded in the matrix, which indicates that the perfect interfacial interaction between the GnPs and epoxy matrix. The composite exhibits a relatively rough fracture surface with some river-like structures(Fig.4a). The GnPs rich region indicated in Fig. 4a is magnified and shown in Fig. 4b in order to verify existence of the GnPs (see the black arrows), there is no obvious aggregation in our random specimens. Therefore, aggregation of GnPs is neglected in this study. The dispersion of GnPs can be better solved by our dispersion process.
Fig.4. SEM image of fracture surface of the composite
3.2. The percolation threshold Monotonic tensile tests were conducted to examine the repeatability, sensitivity and linearity of the electrical resistance change of the composite samples with coated GnPs/epoxy flexible sensor. It is important to find the percolation threshold to determine the optimum number of GnPs concentration that would obtain the highest 13
possible strain sensitivity.
log
1 log Linear Fit of sheet1 log
2.0
0
C*=0.76vol%
1.5 1.0
log
log(electrical conductivity, S/m)
2
0.5 0.0
R-Square 0.93 Slop t 1.57
-0.5
-1
-1.0 -1.6
-1.2
-0.8
-0.4
0.0
0.4
log(C-C*)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
graphene content(vol.%) Fig. 5 Electrical conductivity of GnPs/epoxy mixtures films
The logarithm electrical conductivity change of different volume fraction of graphene is shown in Fig. 5. The logarithm electrical conductivity increases by orders of magnitude with the GnPs concentration, increasing from -1.26 to 1.87. And at low volume fraction (<0.7 vol. %) and high volume fraction (>1.58 vol. %), the logarithm electrical conductivity amplitude exhibited negligible changes, while for 0.76 vol. %1.58 vol. %, the logarithm electrical conductivity augment drastically. According to classical percolation theory, the conductivity of composite materials, which increasing with conductive filler content, can be described by a scaling law of the form 𝜎 = 𝜎0 (𝑐 − 𝑐 ∗ )𝑡 , where 𝑐 ∗ is the percolation threshold, and 𝜎0 and t are fitting parameters. This model provides an easy way to fit the experimental data[20, 32-35]. As the mass fraction increases beyond the percolation threshold, the conductivity increases. By fitting the experimental data into the law log𝜎 ∝ log(𝑐 − 𝑐 ∗ ), this fit was found by incrementally varying 𝑐 ∗ until the best 14
linear fit to the equation was found[9]. The inset of Fig. 5 shows the log–log plot of the equation, giving the percolation threshold of 0.76 vol. % and the coefficient of determination of 93%. These values are higher than the percolation threshold of conductive mixture with 3-nm thick graphene platelets and epoxy[9], but it is lower than the percolation threshold of 3wt.% for the GnPs/epoxy composites reported in[13], 2wt.% reported in [28] and 1.31 vol. % reported in[27]. The value of the exponent t (i.e. 1.57) of the percolation law, obtained as the slope of the linear interpolation in the inset of Fig. 5, is found to agree with universal values typically reported in literature. 3.3. Monotonic tensile test In this paper, the normalized resistance change ∆𝑅/𝑅0 is used to describe the sensitivity of the Gns/epoxy flexible sensors, where ∆𝑅 is the resistance change with strain, 𝑅0 is the resistance prior to straining (at room time). The ∆𝑅/𝑅0 was calculated and plotted against the mechanical strain of the tested coupon, as shown in Fig. 6. As presented in analytical modeling, the coupons were coated with GnPs/epoxy flexible sensor with the GnPs loading of 1.00 vol. %, near but after the percolation threshold, where most GnPs would be intimate connection by tunneling or/and overlapping to achieve better linearity and stability.
15
20
12
R/R()
2
1
1.67%
R-Square 0.99422 slope 6.50174
0 0.0
0.1
0.2
0.3 strain()
0.4
0.5 16
8
R/R() Linear Fit of sheet1 B R/R()
12
R/R()
R/R()
16
R/R()
R/R() Linear Fit of sheet1 B R/R()
3
4 =0.50%
0 0.0
0.5
8
R-Square 0.99939 slope 11.60162
4 0.6
0.9
1.0 1.5 strain()
1.2
strain()
1.5
2.0
1.8
2.5
Fig. 6 Normalized resistance change (∆𝑅/𝑅0 ) monotonic increase with strain increase for GNs/epoxy conductive mixtures with the graphene concentrations of 1.00vol. % and the fitting by linear regression.
As shown in Fig.6, the normalized electrical resistance curve starts to behave in positive piezoresistive fashion, i.e. ∆𝑅/𝑅0 increases from 0 to 18.58in response to the increasing axial tensile strain to 2.17%, which continues until fracture takes place. It is obvious that evident abrupt changes occurred around at the 0.50% and 1.67%. For strain range (00.50%) and (0.50%1.67%), as shown in the top left and bottom right corners of Fig. 6, curves were all close to a linear increase. The regressions are strong with a coefficient of determination of 0.994 with slop of 6.5 for range (00.50%) and 0.999 with slop of 11.6 for range (0.50%1.67%). They are almost linear growth. For higher strain levels (𝜀 >1.67%), the ∆𝑅/𝑅0 curve becomes nonlinear. Around at the 1.3% of strain, we observed a cracking that discontinues the electrical measurements 16
and leads to the specimen collapse. ∆𝑅/𝑅0 curve with an evident abrupt change around at the 0.50% most likely due to the difference of main working mechanism. For strain range (00.50%), the application of external tension results in a small expansion of tunneling pathways between adjacent platelets so the tunneling resistance increases, while for strain range (0.50%1.67%), it possibly is associated with discrete damage accumulation leading to disruption of the electrical conductive pathways caused by the further expansion of microcracks[18, 36]. The vary mostly due to some permanent and irreversible phenomena in electrical percolating network associated to morphological rearrangement of the GnPs dispersed in the epoxy caused by the further expansion of width between adjacent GnPs[27]. For higher strain levels (𝜀 >1.67%), ∆𝑅/𝑅0 curve becomes nonlinear. This change may be indications of load distribution in the composite structure due to a damage incident that the GnPs flexible sensor was capable of capturing. When this structure was strained monotonically, ∆𝑅/ 𝑅0 exhibited similar smooth trend with minimal oscillations and sensed the first damage incident related to fiber breakages which was corresponding to 1.67% strain. The sensor sensitivity was evaluated by a gauge factor (GF), which is usually defined as the ratio of relative change in electrical resistance ∆𝑅/𝑅0 to the mechanical strain, ε. Then, GF at the normal direction 𝐺𝐹 = (𝛥𝑅/𝑅0 )/𝜀. It is found that the relative change in resistance was proportional to the change in strain. The GF can be obtained from the slope of the curve. Hence, from the slope of the plot, the maximum gauge factor of GnPs/epoxy mixture sensors was 11.6 in tensile strain. Though this 17
value is less than the gauge factor of 45 for strain sensor based on functionalized graphene nanoplates, the percolation threshold is more lower than 1.31 vol. % reported in[27]. And the value is higher than many CNT/polymer composite strain sensors [6, 15], the 11.4 for piezoresistive nano smart hybrid material based on Gns[13] and 1.9 for graphene-based strain sensors[25], implying the sensor has a good reliability for practical application. 3.2. Loading-unloading tensile tests. In order to investigate the reversibility and stability of GnPs/epoxy flexible sensor, the composite specimens were subjected to tensile loading cycles based on increases/decreases of some selected level strains. Generally, load incremental tensile testing is useful for assessing a sensor’s potential to work under real damage inducing mechanical loads. This test aimed to monitor the electrical resistance response of GnPs/epoxy flexible sensor with 1.00 vol. % GnPs loading under progressive loading.
Fig. 7 Typical axial strain and corresponding normalized resistance change as function of the loading-unloading pattern: (a) 0.39%; (b) 1.1%
As shown in Fig. 7, increasing strain per cycle was applied (i.e. 0.39% and 18
1.1%), and the temporal behavior of the piezoresistive response was monitored. upon loading, ∆𝑅/𝑅0 immediately increased ranging from 02.5% in response to mechanical loading of 0.39%, while ranging from 0-9.6 % in response to loading of 1.1%. Upon unloading, ∆𝑅/𝑅0 decreased to 0 as loading to its original conformation. Through comparing the 5 cycles, the electrical resistance amplitude exhibited negligible changes after 5 loading–unloading cycles, further demonstrating the high sensitivity and durability of our sensor. The resistive behavior of the sensor is regular since, for the same value of the strain in each cycle, the variation of electrical resistance shows comparable values of the ∆𝑅/𝑅0 ratio. It is evident that the electrical resistance changes vary with the intensity of strain and that the maximum value achieved for each level of deformation is maintained for different cycles. Moreover, the resistance resumes its initial value under an applied strain up to 1.1%. This reproducible resistive response during the mechanical cycles indicates that no ruptures or permanent deformation has occurred in the composite structure.
Fig.8 Gauge factor and corresponding cycle number in the load and unload tensile test. (The cycles are identical to those in the test in Fig. 7; a 0.39%; b 1.1%)
The stability of the piezoresistive response was also analyzed for the variation of the gauge factor with the number of tensile cycles. As shown in Fig.8, GFs upon 19
loading and unloading all had only a slight change in the 5-cycle, and GF upon loading of 0.39% was very similar to GF upon unloading keeping mean value about 5.3. It may be inferred from this result that the sensor maintains reversibility and stability. What is interesting, the GF of the sensor upon loading of 0.39% was similar to the GF discussed in section 3.3(lower strain range (0-0.5%)). While for 1.1%, the first-time loading, the GF was 5.21 for lower strain range (00.5%), and GF was 11.2 for strain range (0.5%1.67%). These values were very similar to GF discussed in section 3.3. After that, GFs remained essentially unchanged but was significantly reduced from the first time. Consistent with the result in section 3.3, most likely due to the evident abrupt change around at the 0.5%, some permanent and irreversible phenomena in electrical percolating network associated to morphological rearrangement of the GnPs dispersed in the epoxy. 4. Conclusions Dispersing GnPs into an epoxy to serve as strain and damage flexible sensor was introduced in this study. The experimental results lead us to following conclusions: 1) By analytical model, the relative contribution of the inherent piezoresistive behavior, discrete damage accumulation is considered significant before the percolation threshold. The conductivity and the complex internal structure restricts to make them as sensors. While after the percolation threshold, the GnPs connect with each other by overlapping or/and tunneling, the electrical resistance increase mostly due to touching GnPs contact area change. 2) A better dispersion of GnPs in epoxy matrix was obtained by optimizing 20
ultrasonic time and using the ball mill mixing process. The GnPs/epoxy composite with lower percolation threshold, i.e. ∼0.76 vol. %. 3) The 1.00 vol. %, near but after the percolation threshold 0.76 vol. %, where most GnPs would be intimate connection by tunneling or/and overlapping to achieve better linearity and stability. GnPs/epoxy was selected as sensor via monotonic tensile test. Though the gauge factor of this sensor is different for different sensing stages, the relationship between resistance and strain is linear for each sensing stage, and GF are 6.5 for strain range (00.5%) and 11.6 for strain range (0.5%-1.67%). The results demonstrate that GnPs/epoxy mixture can be used as a strain sensor. 4) For loading of 0.39%, GFs upon loading and unloading all had only a slight change in the 5-cycle, and GF upon loading of 0.39% was very similar to GF upon unloading keeping mean value about 5.3. While for 1.1%, the first-time loading, the GF was 5.21 for lower strain range (00.5%), and GF was 11.2 for strain range (0.5%1.67%). These values were very similar to GF discussed in section 3.3. After that, GFs remained essentially unchanged but was significantly reduced from the first time. Loading and unloading tensile test results shows that the strain of the composites can be obtained at least five cycles later. Acknowledgements The financial contributions are gratefully acknowledged. This work was financially supported by the National Nature Science Foundation of China (11602150), the Aviation Science Fund (2016ZA54004), the Liaoning Provincial 21
Department of Education Fund (L201619), the Liaoning Province Talents Support Plan of China (LR2015048) and the Shenyang Science and Technology Bureau International Cooperation Fund of China (F16212600). The financial contributions are gratefully acknowledged. References [1] K. Diamanti, C. Soutis, Structural health monitoring techniques for aircraft composite structures, Progress in Aerospace Sciences, 46(2010) 342-52. [2] A. Ghoshal, M.J. Sundaresan, M.J. Schulz, P.F. Pai, Structural health monitoring techniques for wind turbine blades, Journal of Wind Engineering & Industrial Aerodynamics, 85(2000) 309-24. [3] L.C. Hollaway, A review of the present and future utilisation of FRP composites in the civil infrastructure with reference to their important in-service properties, Construction & Building Materials, 24(2010) 2419-45. [4] N.D. Alexopoulos, C. Jaillet, C. Zakri, P. Poulin, S.K. Kourkoulis, Improved strain sensing performance of glass fiber polymer composites with embedded pre-stretched polyvinyl alcohol–carbon nanotube fibers, Carbon, 59(2013) 65-75. [5] C.R. Farrar, K. Worden, An introduction to structural health monitoring, Philosophical Transactions, 365(2007) 303. [6] L. Vertuccio, L. Guadagno, G. Spinelli, P. Lamberti, V. Tucci, S. Russo, Piezoresistive properties of resin reinforced with carbon nanotubes for health-monitoring of aircraft primary structures, Composites Part B Engineering, 107(2016) 192-202. [7] J. Sebastian, N. Schehl, M. Bouchard, M. Boehle, L. Li, A. Lagounov, et al., Health monitoring of structural composites with embedded carbon nanotube coated glass fiber sensors, Carbon, 66(2014) 191–200. [8] editors-in-chief, ChristianBoller, Fu-KuoChang, YozoFujino, Encyclopedia of structural health monitoring: Wiley; 2009. [9] D. Lee, H.P. Hong, C.J. Lee, C.W. Park, N.K. Min, Microfabrication and characterization of spraycoated single-wall carbon nanotube film strain gauges, 22(2011) 455301. [10] K. Aly, A. Li, P.D. Bradford, Strain sensing in composites using aligned carbon nanotube sheets embedded in the interlaminar region, Composites Part A Applied Science & Manufacturing, 90(2016) 536-48. [11] B. Hofer, Fibre optic damage detection in composite structures, Composites, 1(1987) 309-16. [12] J.M. Park, S.I. Lee, O.Y. Kwon, H.S. Choi, J.H. Lee, Comparison of nondestructive microfailure evaluation of fiber-optic Bragg grating and acoustic emission piezoelectric sensors using fragmentation test, Composites Part A, 34(2003) 203-16. [13] Y.J. Kim, Y.C. Ju, H. Ham, H. Huh, D.S. So, I. Kang, Preparation of piezoresistive nano smart hybrid material based on graphene, Current Applied Physics, 11(2011) S350-S2. [14] G. Shi, Z. Zhao, J.H. Pai, I. Lee, L. Zhang, C. Stevenson, et al., Highly Sensitive, Wearable, Durable Strain Sensors and Stretchable Conductors Using Graphene/Silicon Rubber Composites, Advanced Functional Materials, 26(2016) 7614-25. 22
[15] W. Obitayo, T. Liu, A Review: Carbon Nanotube-Based Piezoresistive Strain Sensors, Journal of Sensors, 2012(2012) 276-83. [16] E.T. Thostenson, T.W. Chou, Carbon Nanotube Networks: Sensing of Distributed Strain and Damage for Life Prediction and Self Healing, Advanced Materials, 18(2006) 2837-41. [17] N. Hu, Y. Karube, M. Arai, T. Watanabe, C. Yan, Y. Li, et al., Investigation on sensitivity of a polymer/carbon nanotube composite strain sensor, Carbon, 48(2009) 680-7. [18] A. Sanli, A. Benchirouf, C. Müller, O. Kanoun, Piezoresistive performance characterization of strain sensitive multi-walled carbon nanotube-epoxy nanocomposites, Sensors and Actuators A: Physical, 254(2017) 61-8. [19] X. Cao, X. Wei, G. Li, C. Hu, K. Dai, J. Guo, et al., Strain sensing behaviors of epoxy nanocomposites with carbon nanotubes under cyclic deformation, Polymer, 112(2017) 1-9. [20] Y. Wang, J.W. Shan, G.J. Weng, Percolation threshold and electrical conductivity of graphenebased nanocomposites with filler agglomeration and interfacial tunneling, Journal of Applied Physics, 118(2015) 065101. [21] S. Shadlou, B. Ahmadi-Moghadam, F. Taheri, The effect of strain-rate on the tensile and compressive behavior of graphene reinforced epoxy/nanocomposites, Materials & Design, 59(2014) 439-47. [22] R. Moriche, M. Sánchez, A. Jiménez-Suárez, S.G. Prolongo, A. Ureña, Strain monitoring mechanisms of sensors based on the addition of graphene nanoplatelets into an epoxy matrix, Composites Science and Technology, 123(2016) 65-70. [23] X. Du, H. Zhou, W. Sun, H.-Y. Liu, G. Zhou, H. Zhou, et al., Graphene/epoxy interleaves for delamination toughening and monitoring of crack damage in carbon fibre/epoxy composite laminates, Composites Science and Technology, 140(2017) 123-33. [24] X.-W. Fu, Z.-M. Liao, J.-X. Zhou, Y.-B. Zhou, H.-C. Wu, R. Zhang, et al., Strain dependent resistance in chemical vapor deposition grown graphene, Applied Physics Letters, 99(2011) 213107. [25] J. Zhao, G.-Y. Zhang, D.-X. Shi, Review of graphene-based strain sensors, Chinese Physics B, 22(2013) 057701. [26] J. Muñoz, L.J. Brennan, F. Céspedes, Y.K. Gun'ko, M. Baeza, Characterization protocol to improve the electroanalytical response of graphene–polymer nanocomposite sensors, Composites Science and Technology, 125(2016) 71-9. [27] J.W. Zha, B. Zhang, R.K.Y. Li, Z.M. Dang, High-performance strain sensors based on functionalized graphene nanoplates for damage monitoring, Composites Science & Technology, 123(2016) 32-8. [28] L.M. Chiacchiarelli, M. Rallini, M. Monti, D. Puglia, J.M. Kenny, L. Torre, The role of irreversible and reversible phenomena in the piezoresistive behavior of graphene epoxy nanocomposites applied to structural health monitoring, Composites Science and Technology, 80(2013) 73-9. [29] N. Hu, Y. Karube, C. Yan, Z. Masuda, H. Fukunaga, Tunneling effect in a polymer/carbon nanotube nanocomposite strain sensor, Acta Materialia, 56(2008) 2929-36. [30] Y. Kuronuma, T. Takeda, Y. Shindo, Electrical resistance-based strain sensing in carbon nanotube/polymer composites under tension: Analytical modeling and experiments, Composites Science & Technology, 72(2012) 1678–82. [31] L. Yue, G. Pircheraghi, S.A. Monemian, I. Manas-Zloczower, Epoxy composites with carbon nanotubes and graphene nanoplatelets – Dispersion and synergy effects, Carbon, 78(2014) 268-78. [32] B.E. Kilbride, J.N. Coleman, J. Fraysse, P. Fournet, M. Cadek, A. Drury, et al., Experimental observation of scaling laws for alternating current and direct current conductivity in polymer-carbon 23
nanotube composite thin films, Journal of Applied Physics, 92(2002) 4024-30. [33] W. Bauhofer, J.Z. Kovacs, A review and analysis of electrical percolation in carbon nanotube polymer composites, Composites Science and Technology, 69(2009) 1486-98. [34] Q. Meng, J. Jin, R. Wang, H.C. Kuan, J. Ma, N. Kawashima, et al., Processable 3-nm thick graphene platelets of high electrical conductivity and their epoxy composites, Nanotechnology, 25(2014) 125707. [35] N. Yousefi, M.M. Gudarzi, Q. Zheng, S.H. Aboutalebi, F. Sharif, J.-K. Kim, Self-alignment and high electrical conductivity of ultralarge graphene oxide–polyurethane nanocomposites, Journal of Materials Chemistry, 22(2012) 12709. [36] A. Nakamura, T. Hamanishi, S. Kawakami, M. Takeda, A piezo-resistive graphene strain sensor with a hollow cylindrical geometry, Materials Science and Engineering: B, 219(2017) 20-7.
Figure captions Fig. 1. Schematic view of the experiment system Fig. 2. Experimental setup and specimen characteristics for piezoresistive characterization in tensile tests. Fig. 3. Modelling of distribution of graphene platelets in epoxy (a, before the percolation threshold, b after the percolation threshold) Fig. 4. SEM image of fracture surface of the nanocomposite Fig. 5. Electrical conductivity of GnPs/epoxy mixtures films Fig. 6. Normalized resistance change (∇R/𝑅0 ) monotonic increase with strain increase for GNs/epoxy conductive mixtures with the graphene concentrations of 1.00vol. % and the fitting by linear regression. Fig. 7. Typical axial strain and corresponding normalized resistance change as function of the loading-unloading pattern: (a) 0.39%; (b) 1.1% Fig. 8. Gauge factor and corresponding cycle number in the load and unload tensile test. (The cycles are identical to those in the test in Fig. 7; a 0.39%; b 1.1%)
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Author Biography Shaowei Lu is currently Professor in the Faculty of Aerospace Engineering at Shenyang Aerospace University (SAU), Liao ning, China. His current areas of research are health monitoring of polymer composites and functional composites. Shaowei Lu put forward the idea and direction,then designed the experiments; Caijiao tian, postgraduate student, performed the experiments and processed the exprimental data,then written the paper with the help of Xiaoqiang wang and Duo chen; Keming Ma, Jingsong Leng and Lu Zhang help modified the paper.
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S0924-4247(17)31267-0 https://doi.org/10.1016/j.sna.2017.10.047 SNA 10413
To appear in:
Sensors and Actuators A
Received date: Revised date: Accepted date:
11-7-2017 1-10-2017 17-10-2017
Please cite this article as: Shaowei Lu, Caijiao Tian, Xiaoqiang Wang, Duo Chen, Keming Ma, Jingsong Leng, Lu Zhang, Health monitoring for composite materials with high linear and sensitivity GnPs/epoxy flexible Strain sensors, Sensors and Actuators: A Physical https://doi.org/10.1016/j.sna.2017.10.047 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Health monitoring for composite materials with high linear and sensitivity GnPs/epoxy flexible Strain sensors Shaowei Lua, *, Caijiao Tian a, Xiaoqiang Wang, a Duo Chen a, Keming Ma a, Jingsong Leng b, Lu Zhang a a
Faculty of Aerospace Engineering, Shenyang Aerospace University, Shenyang
110136, China b
Institute of Composite Materials and Structures, School of Astronautics, Harbin
Institute of Technology, Harbin 10213, Chain The graphene platelets (GnPs)/epoxy flexible sensor with high sensitivity and linear can be used to monitor the deformation and damage in structural composites. The dispersion of GnPs in an epoxy matrix was better improved by optimizing ultrasonic time and using the ball mill mixing process. The GnPs/epoxy mixture exhibited relatively low percolation threshold of 0.76 vol. % and its model was analyzed. In this paper, GnPs/epoxy mixture with GnPs loading of 1.00vol.% was selected as damage detecting and strain sensor, and the characteristics of sensor was demonstrated via various mechanical tests. The monotonic tensile results suggested that there are two different linear change sensing stages, (05000 με ) and (500016700 με ), and it exhibits relatively high gauge factor (GF) of 6.5 and 11.6 in different sensing stages. The linear relationship and sensitivity were recoverable and stable upon loading and unloading tensile test. And the gauge factors upon loading and unloading test agree with the GF in monotonic tensile test. The GnPs/epoxy flexible sensor show promising applications for the damage monitoring of structural 1
composite in the field of aerospace. Keywords: graphene platelets(GnPs); percolation threshold; Sensor; Strain; Piezoresistive Corresponding author: Shaowei Lu E-mail: [email protected] Tel/Fax: +86-024-8972 3713
Graphical abstract
Coupon coated GnPs/epoxy flexible sensor
Analytical conductive model
2
Monotonic tensile and loading-unloading test
Highlights
The dispersion of GnPs in an epoxy matrix was better solved by optimizing ultrasonic time and using the ball mill mixing process. The GnPs/epoxy mixture exhibited relatively low percolation threshold of 0.76 vol. %.
Its analysis model was analyzed.
3.The monotonic tensile results suggested that there are two different linear change sensing stages, (05000 με ) and (500016700 με ), and it exhibits relatively high gauge factor (GF) of 6.5 and 11.6 in different sensing stages
The linear relationship and sensitivity were recoverable and stable upon loading and unloading tensile test. And the gauge factors upon loading and unloading test agree with the GF in monotonic tensile test.
1. Introduction The increased application of structural composites in aerospace, energy and 3
infrastructure, among other areas, has led to a concern about the reliability of these materials. [1-3]. These structures are often exposed to a variety of conditions, including impact, excessive loading, fatigue, material defects, environmental deterioration, manufacturing shortcomings and fluid penetration. The onset of local damage in structures, such as delamination, cracking and fastener loosening, can often be difficult to detect and has long term implications on the performance of the composite structure. Non-destructive techniques, such as X-ray, ultrasonic or eddy current inspection can offer the capability of periodical inspection on local damage, but the structures may require disassembly for inspection. Nevertheless, they cannot provide structural information in real-time conditions[4-7]. As a result, it is necessary to develop innovative techniques to monitor the state of damage in composite structures. Structural health monitoring (SHM) seeks to provide ongoing monitoring of a structure’s integrity, minimizing the need for programmed inspections and allowing maintenance to be need-driven, rather than usage-driven. Currently emerging SHM approaches include the use of strain gages and fiber optic sensors to measure strain, vibration, harmonic frequencies, or other parameters which can be used to assess the health of the structure by comparing these values to a known healthy dataset. Strain sensors are widely used in various fields of engineering for monitoring and damage detecting of critical infrastructure. But they provide sensing only near the sensor itself; therefore, they are limited by the requirement to detect damage only in designated directions and locations. When damage occur at other unanticipated 4
regions, it may be useless. Optical fibers have been used as embedded SHM sensors in an attempt to overcome the disadvantages of strain gauges, but their adoption has not been as wide spread due to a few reasons. The first is that expensive equipment is required for instrumentation. Second, optical fiber sensors are often insensitive to cracks propagating parallel to the optical fiber orientation. Embedded optical fibers sensors also may act as defects in the composite structure promoting crack initiation and damage to the composite parts due to the fact that the optical fiber diameter is larger than the diameter of the reinforcement fibers.[7-12] The discovery of carbon nanotubes (CNTs) and graphenes (Gns) has generated keen interest among researchers to develop CNT-based or Gn-based sensors for many applications. And the piezoresistive nature of CNT and Gns has been observed clearly over the past few years[13, 14]. Several studies have focused on piezoresistive polymers made by dispersing CNTs into a polymer to form a conductive matrix[1519]. This conductive matrix can then be molded into a thin film, a bulk composite shape, a ribbon or any other shape desired. Higher sensitivity has been observed in these novel sensors, at least at a macro-scale. compared with CNTs, Gns is in general easier to prepare and less costly for mass production[20]. From this perspective, the use of polymer/graphite composites featuring good process ability and low-cost materials is very promising. Shahin[21] et al. has investigated the effect of strain rate on mechanical behavior of epoxy reinforced with GnPs. R. Moriche et al. [22] carried out analyses the strain sensor capability of nanocomposites reinforced with different GnPs.
Detailed toughening mechanisms of the graphene in both graphene/epoxy and 5
carbon fiber/epoxy composites were studied by Xusheng Du et al.[23]. Some researchers reported Gns/epoxy composite-based strain gauges in the literature[13, 24-28]. These strain sensors were observed to have a higher gauge factor. But the major drawback of those Gns/polymer composite sensors was that the Graphene fractions they selected were high due to they did not discuss the percolation threshold in detail or the percolation threshold was high. Although Graphene content of Xusheng Du et al. [23] is relatively low, the linear fit of the graphene sensor given in the literature was not very satisfactory. Therefore, this writer seeks to build upon a prior study into the sensing ability of Gns/epoxy sensor by testing its response to mechanical loading. To further examine the behavior and sensitivity, determining the optimum number of Gns that may achieve the highest sensitivity and the analytical model are major process of this study. 2. Experimental section 2.1. Preparation and characterization of GnPs/epoxy conductive mixtures Graphene platelets (GnPs) were fabricated according to a brief “intercalationexpansion-stripping” procedure. This produced GnPs with average number of layers (5~6), an average thickness of (< 3 nm) and film conductivity (>700 s/cm). GnPs/epoxy conductive mixtures with the graphene platelet concentrations from 0.32 vol. % to 2.64 vol. % was prepared as follows: The GnPs were first dispersed in acetone (E-51, Yanshan Co., Ltd., China) by sonication under 30℃ for 2 h. The GnPs/acetone solutions with different GnPs weight fractions were then mixed with the epoxy by sonication for 1 h. The mixture was further processed using a planetary 6
ball mill (PM 400, Retsch) equipped with four steel containers (125 mL) and three different types of zirconia balls (3, 6, 11 mm in diameter and the corresponding 250, 50, 10 in number). The high shear stress applied by the milling impact can break up the agglomerates of GnPs and improve their exfoliations to generate the highly dispersed GnPs/epoxy dispersions. To avoid any intense shock stress which could destroy the GnPs structures, the rotating tray was controlled at a low speed of 200 rpm to ensure that the shear stress is dominant. After adding a little curing agent, the mixture was subjected to stir and shock to remove the acetone. Subsequently the conductive mixtures were coated on the grasses and then solidified to films in an oven under 120℃ for 1h. The electrical conductivity of the films was examined with a commercial four-probe resistance measuring apparatus (RTS-8,4 PROBES TECH Co., Ltd., China). The position is shown in Fig .1.
Fig. 1 Schematic view of the experiment system
2.2. Fabrication of composite surface coated with GnPs /epoxy flexible sensor In this section, a glass/epoxy unidirectional prepreg (6501/G15000/33%, Weihai Guangwei composites Co., Ltd., China) was used to fabricate a laminate specimen 7
with an area of 250mm*200mm. The specimen was stacked with [O16 ] prepregs on a steel moulid, which was previously treated with demouliding agent, then enclosed in a vacuum bag system and heated with the temperature first ramped from room temperature to 120℃ and then maintained at 120℃ for 2h in an oven. The products were cut into rectangular coupons (250mm×25mm). The thickness of all coupons was about 1mm. The 1.00 vol. % GnPs/epoxy conductive mixture was coated on the coupons solidified at 120℃ for 1h. Spacing and thickness between the conductive mixtures were kept as consistent as possible. The geometry and dimensions of composite coupon coated with GnPs/epoxy flexible sensor is shown in Fig.2. After GnPs/epoxy mixture was coated on the coupons surface and solidified, silver paste electrodes were placed on the edge of the films surface to minimize the contact resistance. Non-conducting tabs with a length of 30 mm were bonded to the coupon ends. This was done to reduce the stress concentration due to gripping during tensile testing. Finally, in order to get independent real-time and accurate strain measurements for comparison and GnPs/epoxy sensor calibration, an external commercial metal foil strain gauge (Vishay Micro-Measurements 250LW) was mounted on the outer surface of each composite coupon
8
Fig. 2 Experimental setup and specimen characteristics for piezoresistive characterization in tensile tests.
2.3. Characterization To evaluate the sensing performance of the GnPs/epoxy flexible sensor, different modes of tensile loading were performed using a INSTRON-8801 test system (Fig. 2) in a controlled laboratory environment. These tests included: (1) monotonic tensile tests and (2) loading-unloading tensile tests. The force data was recorded by the testing machine load cell while the strain was recorded via the metal foil strain gauge. The electrical resistance of the GnPs/epoxy mixture was recorded by an FLUKE 2638A mustimeter. It is essential to point out that both the strain gauge data acquisition software and the multimeter program used for recording the GnPs/epoxy film resistance change were started simultaneously with a data collection frequency to guarantee precise correlation of strain and resistance data. Monotonic tensile tests were performed with a fixed displacement speed of 2 mm/min. For the incremental tensile tests, the coupons were subjected to multiple loading-unloading cycles. The test fixture returned to the zero-loading state at the end of each cycle. Generally, three 9
samples of each type were tested monotonically while two samples were tested in progressive loading and cyclic modes. While the failure strains of the composites varied within an acceptable range (which is always the case for materials testing) the shape of the normalized resistance change curves were consistent among sample groups. 2.4 Analytical model When GnPs are thoroughly dispersed in epoxy materials, GnPs should have three categories of junctions: (i) complete contact by overlapping (ii) tunneling junction within a certain distance (below 3 nm), and (iii) disconnection (over 3 nm) caused by microcracks [14]. These GnPs connect with each other forming conductive networks, the resistance would be influenced by the contact resistance between GnPs. A hypothesis made herein was that upon mechanical loading, any deformation would change the contact resistance between GnPs and thus the film resistance, resulting in strong piezoresistivity. The authors think that the relative contribution of the inherent piezoresistive behavior, discrete damage accumulation is considered significant before the percolation threshold. The conductivity and the complex internal structure restricts to make them as sensors. While after the percolation threshold, the GnPs connect with each other by overlapping or/and tunneling, the electrical resistance increases mostly due to touching GnPs contact area change. This physical picture is simplified as a model for evaluating the resistance, as schematically shown in Fig. 3. It is hypothesized that GnPs/epoxy sensors can enable higher sensitivity and damage 10
detection originates from the disconnection and tunneling effect between GnPs, where slight strain causes microcracks and disconnection between GnPs, both of which dramatically increase the resistance. By contrast, stretchable conductors benefit from the densely stacked and overlapped GnPs. Near the percolation threshold, GnPs in epoxy would be able to arrange themselves to maintain connection with each other by reducing the overlap area. Therefore, the highest sensitivity in this sensor is obtained near the percolation threshold[13, 27]. Note that, there may be overlapping among GnPs to a certain extent in our numerical model. This overlapping may result in possible errors in the following stage where we evaluate the pizoresistivity of sensors, since the possibility of breakup of the GnPs network may be slightly underestimated for serious penetration states among GnPs. However, when the volume fraction of GnPs is low near the percolation threshold, GnPs in epoxy would be able to arrange themselves to maintain connection with each other by reducing the overlap area, the error caused by this overlapping is negligible if we explore this problem qualitatively. According to the transfer length method, the tunneling resistance between two adjacent GnPs can be determined by the Simmons' formula [29, 30] 𝑅𝑡 =
ℎ2 𝑤
4𝜋𝑤 exp ( √2𝑚𝜑) (𝐸𝑞. 1) ℎ 𝑎𝑒 2 √2𝑚𝜑
where h is the Planck' s constant, w is the tunnel width, a is the cross-sectional area of the tunnel, e is the single electron charge, m is the mass of electron, and 𝜑 is the height of potential barrier between adjacent platelets. The continuous conductive pathways were modeled as parallel conductive wires (effective conductive pathways) which were composed of the effect GnPs electrically 11
connected in series. The composite consists of 𝐿 number of the effective conductive pathways and each effective conductive pathway is formed by N number of effective GnPs. The composite resistance R can be expressed in terms of the electrical resistance of the effective GnP 𝑅𝑡 . 𝑅=
𝐿 𝐿 ℎ2 𝑤 4𝜋𝑤 𝑅𝑡 = exp ( √2𝑚𝜑) (𝐸𝑞. 2) 𝑁 𝑁 𝑎𝑒 2 √2𝑚𝜑 ℎ
As shown in both equations, the tunneling resistance mainly depends on the tunnel width.
Fig. 3 Modelling of distribution of graphene platelets in epoxy (a, before the percolation threshold, b after the percolation threshold)
3.Results and discussion 3.1 Characterization of GnPs/epoxy mixture The dispersion of GnPs in epoxy is most challenging problem to fabricate composite. The mechanical and electrical properties of the composite significantly depend on the dispersion process. The optimized dispersion process can also improve the percolation level[31]. The dispersion of GnPs in an epoxy matrix was better 12
solved by optimizing ultrasonic time and using the ball mill mixing process. The morphologies of GnPs/epoxy nanocomposites are evaluated by SEM observations. As shown in Fig. 4 for a fractured surface of a sample with 1.00 vol. % GnPs loading, some GnPs are partially embedded in the matrix, which indicates that the perfect interfacial interaction between the GnPs and epoxy matrix. The composite exhibits a relatively rough fracture surface with some river-like structures(Fig.4a). The GnPs rich region indicated in Fig. 4a is magnified and shown in Fig. 4b in order to verify existence of the GnPs (see the black arrows), there is no obvious aggregation in our random specimens. Therefore, aggregation of GnPs is neglected in this study. The dispersion of GnPs can be better solved by our dispersion process.
Fig.4. SEM image of fracture surface of the composite
3.2. The percolation threshold Monotonic tensile tests were conducted to examine the repeatability, sensitivity and linearity of the electrical resistance change of the composite samples with coated GnPs/epoxy flexible sensor. It is important to find the percolation threshold to determine the optimum number of GnPs concentration that would obtain the highest 13
possible strain sensitivity.
log
1 log Linear Fit of sheet1 log
2.0
0
C*=0.76vol%
1.5 1.0
log
log(electrical conductivity, S/m)
2
0.5 0.0
R-Square 0.93 Slop t 1.57
-0.5
-1
-1.0 -1.6
-1.2
-0.8
-0.4
0.0
0.4
log(C-C*)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
graphene content(vol.%) Fig. 5 Electrical conductivity of GnPs/epoxy mixtures films
The logarithm electrical conductivity change of different volume fraction of graphene is shown in Fig. 5. The logarithm electrical conductivity increases by orders of magnitude with the GnPs concentration, increasing from -1.26 to 1.87. And at low volume fraction (<0.7 vol. %) and high volume fraction (>1.58 vol. %), the logarithm electrical conductivity amplitude exhibited negligible changes, while for 0.76 vol. %1.58 vol. %, the logarithm electrical conductivity augment drastically. According to classical percolation theory, the conductivity of composite materials, which increasing with conductive filler content, can be described by a scaling law of the form 𝜎 = 𝜎0 (𝑐 − 𝑐 ∗ )𝑡 , where 𝑐 ∗ is the percolation threshold, and 𝜎0 and t are fitting parameters. This model provides an easy way to fit the experimental data[20, 32-35]. As the mass fraction increases beyond the percolation threshold, the conductivity increases. By fitting the experimental data into the law log𝜎 ∝ log(𝑐 − 𝑐 ∗ ), this fit was found by incrementally varying 𝑐 ∗ until the best 14
linear fit to the equation was found[9]. The inset of Fig. 5 shows the log–log plot of the equation, giving the percolation threshold of 0.76 vol. % and the coefficient of determination of 93%. These values are higher than the percolation threshold of conductive mixture with 3-nm thick graphene platelets and epoxy[9], but it is lower than the percolation threshold of 3wt.% for the GnPs/epoxy composites reported in[13], 2wt.% reported in [28] and 1.31 vol. % reported in[27]. The value of the exponent t (i.e. 1.57) of the percolation law, obtained as the slope of the linear interpolation in the inset of Fig. 5, is found to agree with universal values typically reported in literature. 3.3. Monotonic tensile test In this paper, the normalized resistance change ∆𝑅/𝑅0 is used to describe the sensitivity of the Gns/epoxy flexible sensors, where ∆𝑅 is the resistance change with strain, 𝑅0 is the resistance prior to straining (at room time). The ∆𝑅/𝑅0 was calculated and plotted against the mechanical strain of the tested coupon, as shown in Fig. 6. As presented in analytical modeling, the coupons were coated with GnPs/epoxy flexible sensor with the GnPs loading of 1.00 vol. %, near but after the percolation threshold, where most GnPs would be intimate connection by tunneling or/and overlapping to achieve better linearity and stability.
15
20
12
R/R()
2
1
1.67%
R-Square 0.99422 slope 6.50174
0 0.0
0.1
0.2
0.3 strain()
0.4
0.5 16
8
R/R() Linear Fit of sheet1 B R/R()
12
R/R()
R/R()
16
R/R()
R/R() Linear Fit of sheet1 B R/R()
3
4 =0.50%
0 0.0
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8
R-Square 0.99939 slope 11.60162
4 0.6
0.9
1.0 1.5 strain()
1.2
strain()
1.5
2.0
1.8
2.5
Fig. 6 Normalized resistance change (∆𝑅/𝑅0 ) monotonic increase with strain increase for GNs/epoxy conductive mixtures with the graphene concentrations of 1.00vol. % and the fitting by linear regression.
As shown in Fig.6, the normalized electrical resistance curve starts to behave in positive piezoresistive fashion, i.e. ∆𝑅/𝑅0 increases from 0 to 18.58in response to the increasing axial tensile strain to 2.17%, which continues until fracture takes place. It is obvious that evident abrupt changes occurred around at the 0.50% and 1.67%. For strain range (00.50%) and (0.50%1.67%), as shown in the top left and bottom right corners of Fig. 6, curves were all close to a linear increase. The regressions are strong with a coefficient of determination of 0.994 with slop of 6.5 for range (00.50%) and 0.999 with slop of 11.6 for range (0.50%1.67%). They are almost linear growth. For higher strain levels (𝜀 >1.67%), the ∆𝑅/𝑅0 curve becomes nonlinear. Around at the 1.3% of strain, we observed a cracking that discontinues the electrical measurements 16
and leads to the specimen collapse. ∆𝑅/𝑅0 curve with an evident abrupt change around at the 0.50% most likely due to the difference of main working mechanism. For strain range (00.50%), the application of external tension results in a small expansion of tunneling pathways between adjacent platelets so the tunneling resistance increases, while for strain range (0.50%1.67%), it possibly is associated with discrete damage accumulation leading to disruption of the electrical conductive pathways caused by the further expansion of microcracks[18, 36]. The vary mostly due to some permanent and irreversible phenomena in electrical percolating network associated to morphological rearrangement of the GnPs dispersed in the epoxy caused by the further expansion of width between adjacent GnPs[27]. For higher strain levels (𝜀 >1.67%), ∆𝑅/𝑅0 curve becomes nonlinear. This change may be indications of load distribution in the composite structure due to a damage incident that the GnPs flexible sensor was capable of capturing. When this structure was strained monotonically, ∆𝑅/ 𝑅0 exhibited similar smooth trend with minimal oscillations and sensed the first damage incident related to fiber breakages which was corresponding to 1.67% strain. The sensor sensitivity was evaluated by a gauge factor (GF), which is usually defined as the ratio of relative change in electrical resistance ∆𝑅/𝑅0 to the mechanical strain, ε. Then, GF at the normal direction 𝐺𝐹 = (𝛥𝑅/𝑅0 )/𝜀. It is found that the relative change in resistance was proportional to the change in strain. The GF can be obtained from the slope of the curve. Hence, from the slope of the plot, the maximum gauge factor of GnPs/epoxy mixture sensors was 11.6 in tensile strain. Though this 17
value is less than the gauge factor of 45 for strain sensor based on functionalized graphene nanoplates, the percolation threshold is more lower than 1.31 vol. % reported in[27]. And the value is higher than many CNT/polymer composite strain sensors [6, 15], the 11.4 for piezoresistive nano smart hybrid material based on Gns[13] and 1.9 for graphene-based strain sensors[25], implying the sensor has a good reliability for practical application. 3.2. Loading-unloading tensile tests. In order to investigate the reversibility and stability of GnPs/epoxy flexible sensor, the composite specimens were subjected to tensile loading cycles based on increases/decreases of some selected level strains. Generally, load incremental tensile testing is useful for assessing a sensor’s potential to work under real damage inducing mechanical loads. This test aimed to monitor the electrical resistance response of GnPs/epoxy flexible sensor with 1.00 vol. % GnPs loading under progressive loading.
Fig. 7 Typical axial strain and corresponding normalized resistance change as function of the loading-unloading pattern: (a) 0.39%; (b) 1.1%
As shown in Fig. 7, increasing strain per cycle was applied (i.e. 0.39% and 18
1.1%), and the temporal behavior of the piezoresistive response was monitored. upon loading, ∆𝑅/𝑅0 immediately increased ranging from 02.5% in response to mechanical loading of 0.39%, while ranging from 0-9.6 % in response to loading of 1.1%. Upon unloading, ∆𝑅/𝑅0 decreased to 0 as loading to its original conformation. Through comparing the 5 cycles, the electrical resistance amplitude exhibited negligible changes after 5 loading–unloading cycles, further demonstrating the high sensitivity and durability of our sensor. The resistive behavior of the sensor is regular since, for the same value of the strain in each cycle, the variation of electrical resistance shows comparable values of the ∆𝑅/𝑅0 ratio. It is evident that the electrical resistance changes vary with the intensity of strain and that the maximum value achieved for each level of deformation is maintained for different cycles. Moreover, the resistance resumes its initial value under an applied strain up to 1.1%. This reproducible resistive response during the mechanical cycles indicates that no ruptures or permanent deformation has occurred in the composite structure.
Fig.8 Gauge factor and corresponding cycle number in the load and unload tensile test. (The cycles are identical to those in the test in Fig. 7; a 0.39%; b 1.1%)
The stability of the piezoresistive response was also analyzed for the variation of the gauge factor with the number of tensile cycles. As shown in Fig.8, GFs upon 19
loading and unloading all had only a slight change in the 5-cycle, and GF upon loading of 0.39% was very similar to GF upon unloading keeping mean value about 5.3. It may be inferred from this result that the sensor maintains reversibility and stability. What is interesting, the GF of the sensor upon loading of 0.39% was similar to the GF discussed in section 3.3(lower strain range (0-0.5%)). While for 1.1%, the first-time loading, the GF was 5.21 for lower strain range (00.5%), and GF was 11.2 for strain range (0.5%1.67%). These values were very similar to GF discussed in section 3.3. After that, GFs remained essentially unchanged but was significantly reduced from the first time. Consistent with the result in section 3.3, most likely due to the evident abrupt change around at the 0.5%, some permanent and irreversible phenomena in electrical percolating network associated to morphological rearrangement of the GnPs dispersed in the epoxy. 4. Conclusions Dispersing GnPs into an epoxy to serve as strain and damage flexible sensor was introduced in this study. The experimental results lead us to following conclusions: 1) By analytical model, the relative contribution of the inherent piezoresistive behavior, discrete damage accumulation is considered significant before the percolation threshold. The conductivity and the complex internal structure restricts to make them as sensors. While after the percolation threshold, the GnPs connect with each other by overlapping or/and tunneling, the electrical resistance increase mostly due to touching GnPs contact area change. 2) A better dispersion of GnPs in epoxy matrix was obtained by optimizing 20
ultrasonic time and using the ball mill mixing process. The GnPs/epoxy composite with lower percolation threshold, i.e. ∼0.76 vol. %. 3) The 1.00 vol. %, near but after the percolation threshold 0.76 vol. %, where most GnPs would be intimate connection by tunneling or/and overlapping to achieve better linearity and stability. GnPs/epoxy was selected as sensor via monotonic tensile test. Though the gauge factor of this sensor is different for different sensing stages, the relationship between resistance and strain is linear for each sensing stage, and GF are 6.5 for strain range (00.5%) and 11.6 for strain range (0.5%-1.67%). The results demonstrate that GnPs/epoxy mixture can be used as a strain sensor. 4) For loading of 0.39%, GFs upon loading and unloading all had only a slight change in the 5-cycle, and GF upon loading of 0.39% was very similar to GF upon unloading keeping mean value about 5.3. While for 1.1%, the first-time loading, the GF was 5.21 for lower strain range (00.5%), and GF was 11.2 for strain range (0.5%1.67%). These values were very similar to GF discussed in section 3.3. After that, GFs remained essentially unchanged but was significantly reduced from the first time. Loading and unloading tensile test results shows that the strain of the composites can be obtained at least five cycles later. Acknowledgements The financial contributions are gratefully acknowledged. This work was financially supported by the National Nature Science Foundation of China (11602150), the Aviation Science Fund (2016ZA54004), the Liaoning Provincial 21
Department of Education Fund (L201619), the Liaoning Province Talents Support Plan of China (LR2015048) and the Shenyang Science and Technology Bureau International Cooperation Fund of China (F16212600). The financial contributions are gratefully acknowledged. References [1] K. Diamanti, C. Soutis, Structural health monitoring techniques for aircraft composite structures, Progress in Aerospace Sciences, 46(2010) 342-52. [2] A. Ghoshal, M.J. Sundaresan, M.J. Schulz, P.F. Pai, Structural health monitoring techniques for wind turbine blades, Journal of Wind Engineering & Industrial Aerodynamics, 85(2000) 309-24. [3] L.C. Hollaway, A review of the present and future utilisation of FRP composites in the civil infrastructure with reference to their important in-service properties, Construction & Building Materials, 24(2010) 2419-45. [4] N.D. Alexopoulos, C. Jaillet, C. Zakri, P. Poulin, S.K. Kourkoulis, Improved strain sensing performance of glass fiber polymer composites with embedded pre-stretched polyvinyl alcohol–carbon nanotube fibers, Carbon, 59(2013) 65-75. [5] C.R. Farrar, K. Worden, An introduction to structural health monitoring, Philosophical Transactions, 365(2007) 303. [6] L. Vertuccio, L. Guadagno, G. Spinelli, P. Lamberti, V. Tucci, S. Russo, Piezoresistive properties of resin reinforced with carbon nanotubes for health-monitoring of aircraft primary structures, Composites Part B Engineering, 107(2016) 192-202. [7] J. Sebastian, N. Schehl, M. Bouchard, M. Boehle, L. Li, A. Lagounov, et al., Health monitoring of structural composites with embedded carbon nanotube coated glass fiber sensors, Carbon, 66(2014) 191–200. [8] editors-in-chief, ChristianBoller, Fu-KuoChang, YozoFujino, Encyclopedia of structural health monitoring: Wiley; 2009. [9] D. Lee, H.P. Hong, C.J. Lee, C.W. Park, N.K. Min, Microfabrication and characterization of spraycoated single-wall carbon nanotube film strain gauges, 22(2011) 455301. [10] K. Aly, A. Li, P.D. Bradford, Strain sensing in composites using aligned carbon nanotube sheets embedded in the interlaminar region, Composites Part A Applied Science & Manufacturing, 90(2016) 536-48. [11] B. Hofer, Fibre optic damage detection in composite structures, Composites, 1(1987) 309-16. [12] J.M. Park, S.I. Lee, O.Y. Kwon, H.S. Choi, J.H. Lee, Comparison of nondestructive microfailure evaluation of fiber-optic Bragg grating and acoustic emission piezoelectric sensors using fragmentation test, Composites Part A, 34(2003) 203-16. [13] Y.J. Kim, Y.C. Ju, H. Ham, H. Huh, D.S. So, I. Kang, Preparation of piezoresistive nano smart hybrid material based on graphene, Current Applied Physics, 11(2011) S350-S2. [14] G. Shi, Z. Zhao, J.H. Pai, I. Lee, L. Zhang, C. Stevenson, et al., Highly Sensitive, Wearable, Durable Strain Sensors and Stretchable Conductors Using Graphene/Silicon Rubber Composites, Advanced Functional Materials, 26(2016) 7614-25. 22
[15] W. Obitayo, T. Liu, A Review: Carbon Nanotube-Based Piezoresistive Strain Sensors, Journal of Sensors, 2012(2012) 276-83. [16] E.T. Thostenson, T.W. Chou, Carbon Nanotube Networks: Sensing of Distributed Strain and Damage for Life Prediction and Self Healing, Advanced Materials, 18(2006) 2837-41. [17] N. Hu, Y. Karube, M. Arai, T. Watanabe, C. Yan, Y. Li, et al., Investigation on sensitivity of a polymer/carbon nanotube composite strain sensor, Carbon, 48(2009) 680-7. [18] A. Sanli, A. Benchirouf, C. Müller, O. Kanoun, Piezoresistive performance characterization of strain sensitive multi-walled carbon nanotube-epoxy nanocomposites, Sensors and Actuators A: Physical, 254(2017) 61-8. [19] X. Cao, X. Wei, G. Li, C. Hu, K. Dai, J. Guo, et al., Strain sensing behaviors of epoxy nanocomposites with carbon nanotubes under cyclic deformation, Polymer, 112(2017) 1-9. [20] Y. Wang, J.W. Shan, G.J. Weng, Percolation threshold and electrical conductivity of graphenebased nanocomposites with filler agglomeration and interfacial tunneling, Journal of Applied Physics, 118(2015) 065101. [21] S. Shadlou, B. Ahmadi-Moghadam, F. Taheri, The effect of strain-rate on the tensile and compressive behavior of graphene reinforced epoxy/nanocomposites, Materials & Design, 59(2014) 439-47. [22] R. Moriche, M. Sánchez, A. Jiménez-Suárez, S.G. Prolongo, A. Ureña, Strain monitoring mechanisms of sensors based on the addition of graphene nanoplatelets into an epoxy matrix, Composites Science and Technology, 123(2016) 65-70. [23] X. Du, H. Zhou, W. Sun, H.-Y. Liu, G. Zhou, H. Zhou, et al., Graphene/epoxy interleaves for delamination toughening and monitoring of crack damage in carbon fibre/epoxy composite laminates, Composites Science and Technology, 140(2017) 123-33. [24] X.-W. Fu, Z.-M. Liao, J.-X. Zhou, Y.-B. Zhou, H.-C. Wu, R. Zhang, et al., Strain dependent resistance in chemical vapor deposition grown graphene, Applied Physics Letters, 99(2011) 213107. [25] J. Zhao, G.-Y. Zhang, D.-X. Shi, Review of graphene-based strain sensors, Chinese Physics B, 22(2013) 057701. [26] J. Muñoz, L.J. Brennan, F. Céspedes, Y.K. Gun'ko, M. Baeza, Characterization protocol to improve the electroanalytical response of graphene–polymer nanocomposite sensors, Composites Science and Technology, 125(2016) 71-9. [27] J.W. Zha, B. Zhang, R.K.Y. Li, Z.M. Dang, High-performance strain sensors based on functionalized graphene nanoplates for damage monitoring, Composites Science & Technology, 123(2016) 32-8. [28] L.M. Chiacchiarelli, M. Rallini, M. Monti, D. Puglia, J.M. Kenny, L. Torre, The role of irreversible and reversible phenomena in the piezoresistive behavior of graphene epoxy nanocomposites applied to structural health monitoring, Composites Science and Technology, 80(2013) 73-9. [29] N. Hu, Y. Karube, C. Yan, Z. Masuda, H. Fukunaga, Tunneling effect in a polymer/carbon nanotube nanocomposite strain sensor, Acta Materialia, 56(2008) 2929-36. [30] Y. Kuronuma, T. Takeda, Y. Shindo, Electrical resistance-based strain sensing in carbon nanotube/polymer composites under tension: Analytical modeling and experiments, Composites Science & Technology, 72(2012) 1678–82. [31] L. Yue, G. Pircheraghi, S.A. Monemian, I. Manas-Zloczower, Epoxy composites with carbon nanotubes and graphene nanoplatelets – Dispersion and synergy effects, Carbon, 78(2014) 268-78. [32] B.E. Kilbride, J.N. Coleman, J. Fraysse, P. Fournet, M. Cadek, A. Drury, et al., Experimental observation of scaling laws for alternating current and direct current conductivity in polymer-carbon 23
nanotube composite thin films, Journal of Applied Physics, 92(2002) 4024-30. [33] W. Bauhofer, J.Z. Kovacs, A review and analysis of electrical percolation in carbon nanotube polymer composites, Composites Science and Technology, 69(2009) 1486-98. [34] Q. Meng, J. Jin, R. Wang, H.C. Kuan, J. Ma, N. Kawashima, et al., Processable 3-nm thick graphene platelets of high electrical conductivity and their epoxy composites, Nanotechnology, 25(2014) 125707. [35] N. Yousefi, M.M. Gudarzi, Q. Zheng, S.H. Aboutalebi, F. Sharif, J.-K. Kim, Self-alignment and high electrical conductivity of ultralarge graphene oxide–polyurethane nanocomposites, Journal of Materials Chemistry, 22(2012) 12709. [36] A. Nakamura, T. Hamanishi, S. Kawakami, M. Takeda, A piezo-resistive graphene strain sensor with a hollow cylindrical geometry, Materials Science and Engineering: B, 219(2017) 20-7.
Figure captions Fig. 1. Schematic view of the experiment system Fig. 2. Experimental setup and specimen characteristics for piezoresistive characterization in tensile tests. Fig. 3. Modelling of distribution of graphene platelets in epoxy (a, before the percolation threshold, b after the percolation threshold) Fig. 4. SEM image of fracture surface of the nanocomposite Fig. 5. Electrical conductivity of GnPs/epoxy mixtures films Fig. 6. Normalized resistance change (∇R/𝑅0 ) monotonic increase with strain increase for GNs/epoxy conductive mixtures with the graphene concentrations of 1.00vol. % and the fitting by linear regression. Fig. 7. Typical axial strain and corresponding normalized resistance change as function of the loading-unloading pattern: (a) 0.39%; (b) 1.1% Fig. 8. Gauge factor and corresponding cycle number in the load and unload tensile test. (The cycles are identical to those in the test in Fig. 7; a 0.39%; b 1.1%)
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Author Biography Shaowei Lu is currently Professor in the Faculty of Aerospace Engineering at Shenyang Aerospace University (SAU), Liao ning, China. His current areas of research are health monitoring of polymer composites and functional composites. Shaowei Lu put forward the idea and direction,then designed the experiments; Caijiao tian, postgraduate student, performed the experiments and processed the exprimental data,then written the paper with the help of Xiaoqiang wang and Duo chen; Keming Ma, Jingsong Leng and Lu Zhang help modified the paper.
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