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Applications of Polymer, Composite, and Coating Materials

Interface Design Strategy for the Fabrication of Highly Stretchable Strain Sensors Zhen Sang, Kai Ke, and Ica Manas-Zloczower ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.8b14573 • Publication Date (Web): 03 Oct 2018 Downloaded from http://pubs.acs.org on October 8, 2018

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ACS Applied Materials & Interfaces

Interface Design Strategy for the Fabrication of Highly Stretchable Strain Sensors Zhen Sang, Kai Ke*, Ica Manas-Zloczower* Department of Macromolecular Science and Engineering, Case Western Reserve University, 2100 Adelbert Road, Cleveland, OH 44106-7202, United States Keywords: Thermoplastic polyurethane (TPU), electrical conductivity, strain sensor, segregated structure, carbon nanostructures (CNS). ABSTRACT: Simultaneously achieving high piezoresistive sensitivity, stretchability and good electrical conductivity in conductive elastomer composites (CECs) with carbon nanofillers are crucial for stretchable strain sensor and electrode applications. Here, we report a facile and environmentally-friendly strategy to realize these three goals at once by using branched carbon nanotubes, also known as carbon nanostructure (CNS). Inspired by the brick-wall structure, a robust segregated conductive network of CNS is formed in the thermoplastic polyurethane (TPU) matrix at very low filler fraction, which renders the composite very good electrical, mechanical and piezoresistive properties. An extremely low percolation threshold of 0.06 wt. %, currently the lowest for TPUbased CECs, is achieved via this strategy. Meanwhile, the electrical conductivity is up to 1 and 40 S/m for the composites with 0.7 and 4 wt. % CNS, respectively. Tunable piezoresistive sensitivity dependent on CNS content is obtained and the composite with 0.7 wt. % filler has a gauge factor up to 6861 at strain ε=660% (elongation at break is 950%). In addition, this strategy also renders the composites attractive tensile modulus. The composite with 3 wt. % CNS shows 450% improvement in Young’s modulus versus neat TPU. This work introduces a facile strategy to fabricate highly stretchable strain sensors by designing CNS network structures, advancing understanding the effects of polymer-filler interface on the mechanical and electrical property enhancements for polymer nanocomposites.

1. Introduction Elastic conductive nanomaterials enable a wide spectrum of applications in wearable electronic devices, such as stretchable displays1-2, electromagnic wave attenuation3-4, field-effect transistors5, actuators6-7 and strain sensors8-13. Recently stretchable strain sensors are getting increasingly attractive regarding their potential application for personal health monitoring, therapeutics8, 14-16 and large-scale and complicated human body motion detection8-13, as the joint movements usually generate strain more than 50%8-9, 17-18. As such, conductive elastomer composites (CECs), which can retain good electrical conductivity under large strains, are highly attractive for those applications.12, 19-39 As piezoresistive strain sensors rely on percolated networks in the conductive nanomaterials, combination of elastic polymers with conductive polymers23, metal nanoparticles/nanowires10, 12, 31, 38 or carbon nanofillers9, 19-22, 24-28, 31-37, 40 are frequently reported to fabricate stretchable strain sensors. Generally, two routes are mainly used for the preparation of these conductive nanomaterials: deposition of conductive filler suspension on the particular elastomer surface10, 12, 19, 21, 29, 36-38, 41 and incorporation of them into elastomers via either solution22, 24-25, 27, 31-33, 35, 39 or melt mixing26, 34, 42. The former, i.e. coating filler suspension on the wavy elastomer substrates, depends on the micro/nanofabrication technology. Since conductive fillers have very poor stretchability, it is imperative to design proper surface structure on elastic substrates (e.g. silicone rubber) and make an effective combination of conductive nanofillers with substrates, which both determine the sensing properties of the resultant sensors. Nevertheless, this route is sophisticated, time-consuming and high-cost. Other attempts

regarding the latter strategy, such as using natural rubber latex and graphene25, 32 or carbon nanotubes (CNTs)30 or graphitic nanocarbon43, elastomers and silver nanowires31, etc., were also reported. However, high stretchability and sensitivity seems to be mutually exclusive for both methods. High sensitivity is a premise for effective signal detection and data acquisition for stretchable sensor-based human-machine interface devices. The limitations are probably from the deformation ability of conductive filler networks formed in the elastomers. Specifically, stable piezoresistive sensing requires good conductivity of CECs, namely large amounts of conductive carbon fillers are frequently needed for the formation of relatively dense spatial conductive networks, while this generally brings about the loss of elasticity of elastomers. Meanwhile, this results in a decrease of strain sensitivity and an increase of manufacture cost. Although a trade-off could be found by using functionalized carbon nanofillers in a solutionbased route for the fabrication of these sensors, it requires complex procedures and causes environmental issues due to the consumption of organic solvents. Besides, these strain sensors are capable of detecting tensile strain up to 400% or even higher, though they generally have relatively low sensitivity (gauge factor, GF200%) strain sensor applications. In this work a combination of CNS and segregated structure is proposed to make highly sensitive and stretchable strain sensors at low filler content, as schematically shown in Fig. 2. Firstly, ethanol wetted CNS (yellow lines) were grinded manually with TPU powder (grey spheres) (see Fig. 2(a)) to force CNS to stick to the surface of TPU particles (see Fig. (b)). Afterwards, the surface treated powders were compressionmolded, as shown in Fig. 2(c). Finally, TPU composites with a brick-wall structure, namely TPU powders deformed into multi-facets with CNS conductive layers sandwiched, were obtained, as seen in Fig. 2 (d). Noteworthy, the processing temperature was selected at 230 °C to ensure good bonding between CNS and TPU by means of the melting of TPU soft and hard segments. Thus the TPU chains will penetrate into the CNS meshes making them act as “the mortar” to strongly unite TPU particles (“the bricks”). Worth mentioning, the TPU used here has very high viscosity which hinders the full diffusion of TPU chains into CNS to form a homogeneous structure as observed in melt-mixed TPU composites. Thus, the CNS will locate only at the boundary regions of TPU particles where the penetrated TPU chains provide a good bonding effect. This procedure demonstrates a facile and environmentally-friendly route to fabricate strain sensors.71

Fig. 1 Current design challenges for CEC-based strain sensor.

During the past decades, various kinds of carbon nanofillers, such as carbon black (CB)57-58, carbon nanotubes (CNTs)54, 59 and graphene60-61, have been widely used for fabricating CECs with low ΦC and high conductivity. However, ΦCs are generally above 1 wt. % for melt- and solution-mixed TPU composites48, 62-64, limited by the low efficiency of these carbon nanofillers to form robust spatial interconnected conductive networks throughout the elastomer matrix. To overcome this problem, branched carbon nanotubes may offer a solution to

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Fig. 2 Schematic of the fabrication of TPU/CNS composites with segregated network structure: (a) CNS and TPU powders; (b) TPU/CNS mixture after hand mixing; (c) compression molding of the TPU/CNS mixture; (d) final TPU/CNS composite.

3.2 Morphology of TPU/CNS composites To verify the segregated network structure of CNS in the TPU composites, optical microscopy images of the TPU/CNS composites with different filler contents and the corresponding illustration schematics are provided in Fig. 3. As seen in Fig. 3(a, d), at 0.1 wt. % CNS loading, parts of CNS are trapped between TPU powder, and the connections between CNS agglomerates (ca. tens of micrometers) look loose. In this case, the TPU powders are more coalesced due to insufficient CNS cover of the particles at such low filler content. However, with increasing CNS content, more CNS or CNS bundles are trapped by the TPU powders, as revealed by the dark platelike areas in Fig. 3(b). In addition, the coalescence of TPU powders diminishes and clear continuous conductive paths are formed in the TPU boundary regions, as seen in Fig. 3(b, e), rendering the resultant composites higher electrical conductivity. For 2 wt. % CNS, the coalescence of TPU powders is almost suppressed and a much thicker CNS interfacial layer can be seen, as illustrated by Fig. 3(c, f).

Fig. 3 Optical microscope images and corresponding schematics of TPU/CNS composites with (a, d) 0.1, (b, e) 0.7 and (c, f) 2 wt. % CNS. The dark polygon, purple column, red dash line and green circle stand for TPU powder, CNS particles, conductive path and coalescent boundary between TPU powders, respectively.

To further understand the micro-morphology and TPU-CNS interactions, SEM images of the fracture surfaces of 3 TPU/CNS are provided in Fig. 4. Small-size TPU powder is easily distinguished (green circle), but TPU-TPU interface coalescence areas also exist, as directed by the triangle symbol. Moreover, as seen in Fig. 4 (b), hair-like CNS network structures located at the interface of TPU powders and bridging with each other are observed. As confirmed by the insert in Fig. 4(b) at higher magnification, some CNS can be found penetrating into the surface of TPU powder although not deep inside. These results are highly consistent with optical microscopy findings. Furthermore, this morphology enables the formation of flexible CNS networks and will influence the composite mechanical properties.

Fig. 4 (a) FE-SEM image of 3 TPU/CNS composite. (b) An amplification of the red rectangle region in (a).

3.3 Electrical conductivity Generally, the electrical conductivity (σ) of CECs can be described by the classical percolation theory using the following equation


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σ σ ɸ ɸ

(1) where σ and σ0 are the electrical conductivity of composite and filler, respectively. The power-law constant t is related to the intrinsic conductivity of fillers, Φ is the volume fraction of the fillers and ΦC is the percolation threshold.33, 72-73 Equation (1) can be also used to fit the experimental data in terms of filler weight percentage.74 Fig. 5 (a) shows the plot of electrical conductivity vs. filler content for TPU nanocomposites. A rapid increase of conductivity from 1.5 × 10-9 S/m to 4 × 10-1 S/m was observed when the CNS content increased from 0 to 0.3 wt. %. Further increase of CNS content leads to a slow augment of conductivity and a plateau is observed for CNS contents above 3 wt. %. More interestingly, the conductivity of 4 TPU/CNS composite reaches 40 S/m, which is comparable with CEC-based stretchable conductors75-76. Fig. 5 (b) shows that TPU/CNS nanocomposites have a percolation threshold ΦC of 0.06 wt. %, which is currently the lowest for polyurethane-based conductive composites.48, 55, 62-64 Highly-entangled and wall-sharing multiwall carbon nanotubes of CNS with high aspect ratio contribute to the formation of conductive paths, enabling good charge transfer. By forming a segregated network structure, CNS create dense conducting layers around the TPU powders, which render TPU composites high electrical conductivity at low filler contents.

Fig. 5a (a) Electrical conductivity plotted with filler content. (b) The fitting results of experimental data for TPU/CNS composites using percolation law.

3.4 Mechanical Properties As discussed above, the amount of CNS trapped at the interface influences the compactness of the conductive CNS network, leading to different conductivity and will also govern the mechanical properties of the composites, as shown in Fig. 6. At low filler loadings, TPU-TPU coalescence is occurring and less CNS particles are located at the TPU interface (see Fig. 3). Therefore, by comparison with neat TPU, relatively mild decrease of elongation at break, limited change of fracture strength and slight increase of normalized Young’s modulus, were observed in Fig. 6(a), (b) and (c), respectively. Fig. 6(a) and (b) show that high CNS loadings lead to severe decrease of elongation at break and fracture strength, due to the local stress concentration caused by large amounts of CNS in the interface, as supported by optical microscopy images in Fig. 3(c). Meanwhile, upon increasing CNS loading, TPU/CNS composites have much higher normalized Young’s modulus, as observed in Fig. 6(c), as a result of the reinforcement effect of CNS network and CNS-TPU interfacial interactions due to the penetration of CNS into the TPU, as seen in Fig. 4(b). In addition, by comparison with neat TPU, Fig. 6 (d)

shows that the tensile stress of the composite 0.1 TPU/CNS at 100%, 300% and 500% increases by 22%, 40% and 48%, respectively. Further increasing CNS loading (e.g. 3 wt. %) leads to an improvement in tensile stress up to 54%, 60% and 43%, respectively.

Fig. 6 (a) Elongation at break, (b) fracture strength, (c) normalized Young’s modulus and (d) tensile stress at 100%, 300% and 500% strain for TPU and its composites.

3.5 Strain sensing behavior In general, electrically conductive polymer composites with filler contents slightly above the percolation threshold will have relatively high piezoresistive sensitivity due to the loose filler-filler contacts.77-79 However, such composites have poor reproducibility and unstable sensing properties since the conductive network is very loose. To understand the effects of filler content on piezoresistive sensitivity of TPU composites, Fig. 7(a) and (b) show the piezoresistive response to strain for the TPU composites under single and cyclic loading conditions. As seen in Fig. 7(a), higher filler loadings give rise to higher tensile stress for the TPU composites, consistent with the results shown in Fig. 6(d). Besides, as revealed by the ∆R/R0-strain curves in Fig. 7(a), larger piezoresistive sensitivity appears at higher level of strain during the uniaxial extension. Interestingly, the composites with high filler content can sustain extended experimental time before the resistance exceeds the range (110 MΩ) of the digital multimeter used here. Robust CNS network systems, e.g. 2 TPU/CNS composite, show relatively mild piezoresistive sensitivity. Additionally, the composite 0.7 TPU/CNS shows good stability under small strains and significant ∆R/R0 after the strain exceeds 400%. By contrast, the 0.1 TPU/CNS composite exhibits steeper relative resistance increase at strain ε≤120% by comparison with the other two, which results from the looser CNS network in the TPU matrix.



Fig. 7 (a) Stress and ∆R/R0 plotted with strain for 0.1, 0.7 and 2 TPU/CNS composites. (b) ∆R/R0 plotted with cycle number under strain ε= 85% for 15 test cycles.

To evaluate the strain sensing behavior of these composites, the gauge factor GF, namely the ratio of ∆R/R0 to the instant strain, was employed to quantify composite sensitivity for strain sensor applications.54, 80-81 High sensitivity (a large ∆R/R0 under the same strain), namely a larger GF value, is favorable for the strain sensing applications of CECs. Conventional strain sensors, relying on a geometrical change of conduction path, have a GF in the range of 2-5.82-83 GF for the 0.1 TPU/CNS at strain ε=100% is 20 and for the 2 TPU/CNS composite is 199 at strain ε=780%. The composite 0.7 TPU/CNS has a GF up to 2200 at ε=600% which increases to 6861 at strain ε=660%. Noteworthy, the 0.7 TPU/CNS composite has an elongation at break of ca. 950% (see Fig. 6(a)), which may allow for even larger GF values as desirable for reliable strain sensing applications. Based on our knowledge, the GF value of 6861 at such high strain is currently the highest for TPU-based strain sensors (Table 1). Besides, the processing method used here is environmentally friendly, facile and low-cost (much less filler consumption) in contrast to those used in literature, e.g. solution mixing and melt compounding. All of these reveal the efficiency and significance of this strategy for the fabrication of stretchable strain sensors by TPU-based CECs.

Table 1 Gauge factor comparison between the composite (0.7 TPU/CNS) in this work and those reported in literature for TPU-based CECs. Filler

Polymer

Processing

GF @ strain

6% MWCNTa 0.8% ACNTc 2% MWCNT

Segmented PU

SMb (CHCl3) SM (DMFd)

TPU

SM (THFe)

4.3% MWCNT

TPU granules

SM (CHCl3)

1.25 (24%) 152.93 (30%) 17 (100%) 10 (1500% )

TPU (selfsynthesis)

SM (DMF)

5.4% MWCNT

TPU

1.07 (300%)

Ref.

52

33

54

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8% MWCNT

TPU

MCf (160 º C, 5 min)

10 (300%)

6% MWCNT

TPU & POEg blend (1:1)

MC (160 º C, 5 min)

533 (250%)

3% MWCNT + 1% CB

TPU

MC (240 º C, 4 min)

128 (50%)

0.7% CNS

TPU

CMh

6861 (660%)

53

84

85

This work

MWCNTa: muti-walled carbon nanotubes. SMb: solution mixing. ACNTc: amino carbon nanotubes. DMFd: N-Ne Dimethylformamide. THF : tetrahydrofuran. MCf: melt compounding. POEg: ethylene-octene copolymer polyolefin elastomer. CMh: compression molding. Ref.: Reference. All percentages are weight percentages.

Besides, results in Fig. 7(b) are helpful for evaluating the reversibility and reproducibility of those piezoresistive composites. All the curves show similar changes of ∆R/R0 for each sample, namely the resistance decreases with increasing strain first and then increases. During unloading, the resistancestrain dependence behavior follows a similar graphic pattern with a small shoulder peak, which was also observed in other piezoresistive TPU composites due to the competition of network breaking and reconstruction.45, 86 Interestingly, the composite with 0.1 wt. % CNS has a decay trend in ∆R/R0 upon loading and unloading, suggesting a weak stability due to the creep of the elastomeric composite and loose conductive CNS network. By contrast, the strain sensing behavior for the TPU/CNS composites with 0.7 and 2 wt. % CNS exhibit mostly stable amplitude in each loop, showing much better reversibility and reproducibility. This is due to the more robust network of CNS formed in the TPU matrix, as supported by the electrical conductivity results shown in Fig. 5(a) and macroscale morphology in Fig. 3(b) and (c). 3.6 Potential application for human motion detection Fig. 8 gives an example to demonstrate the potential application for human motion detection of the TPU/CNS composite. A strain sensor made by the 0.7 TPU/CNS composite was selected for imitating elbow bending movement and commercial tape was used to attach it on a flexible tube. As seen in Fig. 8(b), the relative resistance change clearly indicates the bending-straightening cycles corresponding to different bending angles drawn in Fig. 8(a). Interestingly, the resistance signal increases under higher bending degree, as supported by the amplitude of resistance change. Also the device could easily detect, and also discriminate among various human motions involving the extension and flexion of the knee, such as walking, jumping, seating and squatting (Fig. 8(c)). These results point out to promising applications for wearable sensors for human-machine interface devices.87-89

55

9


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Notes The authors declare no competing financial interest.

ACKNOWLEDGMENT The authors acknowledge the financial support from ParkerHannifin Corporation. We also appreciate Applied Nanostructured Solutions LLC for providing CNS for our research. The authors also thank Mrs. Nan Avishai (from the Swagelok Center for Surface Analysis of Materials of Case Western Reserve University) for SEM analysis and Vahab Solouki Bonab for dialog and suggestions.

REFERENCES

Fig. 8 (a) Schematic of 0.7 TPU/CNS test sample under bending with different bending degrees; (b) the corresponding electrical signal of ∆R/R0 plotted as a function of cycle number under 30, 50, 80 degrees bending. (c) Response of a sensor to different human motion patterns (e.g., walking, jumping, seating and squatting) when the sensor was mounted on the knee joint.

4. Conclusion Inspired by the brick-wall structure, we fabricated highly stretchable and piezoresistive TPU/CNS composites with low filler loadings in an environmentally-friendly way by creating the CNS network structure in the TPU. Using an appropriate processing method, CNS were successfully trapped at the interfaces of TPU powders forming an interconnected conductive network throughout the composite. This strategy renders the TPU composites an extremely low percolation threshold (0.06 wt. %), which is currently the lowest one for TPU-based CECs. Moreover, the electrical conductivity of the composites is up to 1 and 40 S/m for 0.7 and 4 wt. % CNS, respectively, which is very promising for stretchable sensor and conductor applications. For sensor application, the piezoresistive sensitivity of these composites is tunable by changing the CNS content. For instance, the composite with 0.7 wt. % filler has a gauge factor up to 6861 at strain ε=660%, indicating the potential applications for highly piezoresistive and stretchable strain sensors. A bending test example, i.e. an investigation of resistance change under various bending degrees, was provided to demonstrate this. In addition, the segregated network structure of CNS in the TPU matrix also influences the mechanical properties of TPU composites. For instance, a 450% improvement of Young’s modulus at only 3 wt. % filler by comparison with that of neat TPU was achieved. Interestingly, these CECs are very elastic in spite of the addition of CNS. 0.7 TPU/CNS and 3 TPU/CNS composites have elongation at break ca. 950 and 550%, respectively, which is very promising for fabrication of stretchable electronics. This strategy is also applicable for other elastomers for the fabrication of electrically conductive and multi-functional CECs.

AUTHOR INFORMATION Corresponding Author * E-mail: [email protected] (K.K.); [email protected] (I.M.).

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