Conductive PolyamideâGraphene Composite Fabric via Interface

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Interface Components: Nanoparticles, Colloids, Emulsions, Surfactants, Proteins, Polymers

Conductive Polyamide–Graphene Composite Fabric via Interface Engineering Ehsan Barjasteh, Christie Sutanto, and Dhriti Nepal Langmuir, Just Accepted Manuscript • DOI: 10.1021/acs.langmuir.8b03543 • Publication Date (Web): 16 Jan 2019 Downloaded from http://pubs.acs.org on January 20, 2019

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Conductive Polyamide–Graphene Composite Fabric via Interface Engineering Ehsan Barjasteh1,2*, Christie Sutanto1, Dhriti Nepal3 1

Chemical Engineering Department, California State University, Long Beach, CA 90840, United States

2

Mechanical and Aerospace Department, California State University, Long Beach, CA 90840, United States

Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, OH 454337702, United States 3

ABSTRACT Conductive fabrics have received significant attention due to their widespread applications from smart textiles to energy storage devices. Conductive colloidal materials are preferred as a coating on the fabric to achieve desirable electronic conductivity; however, obtaining a uniform coverage with a simple and effective route is a challenge. Herein, we report exfoliating graphene nanoplatelets (GNP) in low boiling point solvents, and its subsequent coating onto a polyamide fabric surface. Few-layered (average < 7 layers) GNPs were obtained by optimizing solubility parameters of solvent mixtures and sonication time. Raman spectroscopy showed the ID/IG ratio changed from 0.33 to 0.38 of GNPs solution before and after the sonication confirming an insignificant increase in defects on the basal plane of graphene after sonication treatment. Uniform coating of GNPs was obtained by optimizing concentration and sonication times. Scanning electron microscope (SEM) showed uniform coverage of GNP, and the surface resistivity of polyamide fabric decreased from infinity to ~40 k after 4 hours of the coating. X-ray diffraction (XRD) analysis confirmed the minimal effect on the fabric crystallinity during processing. This interface engineering approach is simple and scalable, and it is applicable for coating of different polymeric fabrics with a great promise in electronic textiles.

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INTRODUCTION Electrically conductive fabrics have expanded their usage into numerous applications including textile construction, aerospace, wearable electronics, electromagnetic shielding, 1–6.

packaging sector, and industrial/residential heating elements

Fabrics are attractive for their

strength, toughness, flexibility, and low density, where retaining similar properties into conductive fabrics is of significant interest 7,8. Graphene is established as one of the most attractive materials for producing conductive fabrics 9, due to its extraordinary electron-transport properties 10–12 and thermal conductivity

13.

Its high aspect ratio, atomic layer thickness, and planar structure are

instrumental for versatile chemical modifications 14. Single to few layered sheets are flexible that can fold and wrap around the surface of the coating fabric by physical interlocking 15, which is critical for improving interfacial properties and fixation of graphene on the fabric. Its mechanical strength is comparable to in-plane values for graphite and its fracture strength akin to that of carbon nanotubes for similar types of defects 16,17. Graphene and its derivatives, e.g. graphene oxide (GO), are shown to improve the toughness of nanocomposites

8,18,19.

Graphene-coated fibers are

promising for improving the interfacial shear strength of the fiber-reinforced composites 20. The fabrication of conductive fabrics generally involves either direct surface coating or blending of polymers with graphene-based nanoparticles. The blending approach is suitable for industrial applications because of its faster production rate and low-temperature fabrication

21.

However, this approach often results in a non-uniform distribution of particles. Adding nanoparticles into the polymer blend increase the viscosity of the slurry, making it difficult to disperse the nanoparticles 22. These nanoparticles tend to aggregate in the polymer slurry due to their noncovalent interactions (van der Waals, -)

23.

Similarly, the adhesion to polymer is


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another issue, which is related to the weak interaction of nanoparticles with the fiber surface 24, leading to incomplete surface coverage and poor conductivity. The products thereof also suffer from brittleness, dimensional instability upon fabrication 25. The direct surface coating, as an alternative approach, can avoid these difficulties. Among various coating methods, chemical vapor deposition (CVD)-based approach is not practical, as the process requires rather high temperature ( ~1000 C or higher) 26,27, which is much higher than the degradation temperature of typical fabrics (~ 400 C)

9,28.

fabric coating include kinetic trapping 9, brush coating

29,30,

Other methods for polymeric-based and vacuum filtration

31,32.

Kinetic

trapping method results in the coating of both graphite and graphene on the fabric. The brush coating and vacuum filtration methods have been investigated by using graphene oxide (GO) and reduced graphene oxide (r-GO). The GO is not conductive and the electrical conductivity of r-GO is lower than that of graphene 33. Another widely used method is the deep coating of the fabric into a solution that contains the graphene-based materials

34,35.

In order to produce a stable suspension of graphene for a

successful dip coating, several methods can be implemented. The commonly used organic solvents such as N-methyl-2-pyrrolidone (NMP) often require high processing temperature to remove solvents at the end of the process that could adversely affect the mechanical integrity of the polymeric fabric and increase the manufacturing cost. Alternatively, surfactants can be employed to produce a stable suspension. However, its removal requires a harsh chemical treatment on the coated fabric 9. The surface functionalization with organic moieties including pyridyl

36,

azomethine ylides 37, nitrophenyl 38, aryl 39, and phenyl 40 are often not straightforward, costly, and could affect the conductivity. Polar graphene-based particles like GO are often used for the dip


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coating process, which is followed by a thermal, chemical, or electrochemical reductions

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41–44.

The fibers are highly sensitive to the processing condition, e.g. heat, and chemical treatment 45, often leads to undesirable physical and chemical changes of the fabric. Given the aforementioned challenges of using the GO or reducing to r-GO and the choice of an appropriate solvent for a successful dip coating process, here we report on a novel approach to avoid the drawback. In this study, we adapted a liquid-based coating method for fabricating conductive fabrics using a mixture of two low-boiling-point solvents along with graphite. A solvent mixture composition was optimized based on tailoring the Hildebrand and Hansen Solubility Parameter (HSP). The exfoliated graphite, graphene nanoplatelets (GNPs), had excellent colloidal stability in the solvent. These GNPs with the assistant of a mild sonication created a homogeneous coating on the fabric with a significantly reduced resistivity. It should be noted that the sonication step did not result in significant increase in defects on the basal plane of graphene. Accordingly, here we report for the first time a successful conductive fabric processing with a low-temperature and straightforward method. EXPERIMENTAL The graphene dispersion samples were prepared by adding 20 mg of graphite (particle size < 300 meshes) from Asbury Carbons with various ratios of acetone and water in a 20-ml vial. The mixtures were bath sonicated (Branson 3510, 40 kHz, 130 W) for different times (0-5 hours). During the sonication, vial caps were covered and the temperature was maintained between 20 C to 40 C to minimize the acetone evaporation. All vials were placed in the same position in the sonication tank and the temperature was closely monitored. The sonicated products were centrifuged for 30 minutes at 1000 rpm and the top 50% of the supernatant was isolated for further characterization and coating process.


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The polyamide 12 fabric (melting point > 185 C) from Spunfab was dried in an oven at 100 C and inserted into the vial containing the supernatant solution. In order to activate the adhesion site on the fabric and to maintain the homogenous dispersion of GNP, the entire process was done under mild sonication. Time for sonication was optimized by varying the times between 0-9 hours. To ensure complete removal of the solvents, the fabrics were dried in a vacuum oven at 100 C for one hour. The concentration of graphene in the solution was evaluated using Varian Cary 300 Bio UV-vis spectrophotometer (quartz cuvette with 1 cm path length) utilizing the Beer-Lambert law. A standard curve was prepared from a known concentration of graphene and the absorbance at 660 nm was used to create a plot of concentration vs absorbance 46. From the slope of this curve, a molar absorption coefficient of graphene was calculated to be 199 L mol-1 cm-1. For atomic force microscopy (AFM) measurement, a cleaned silicon wafer was used (piranha treated, caution: piranha solution is highly reactive and corrosive). 1.0 l of the graphene supernatant solution was drop-casted on a clean silicon wafer and air dried. Bruker Dimension ICON AFM and Quanta 650 FEG scanning electron microscopy (FESEM) were used for surface morphology characterization. Sheet resistance measurement of the fabric was carried out using four-point probes (Keithley 2400) at ambient temperature according to ASTM D257. Two copper plates with an area of 16.1 cm2 and a spacing of 1.1 cm were pressed against the surface of the fabric and measurements were taken after 60 seconds elapsed. The surface resistivity was then calculated using 𝜌𝑠 =R (D/L), where 𝜌𝑠 is the surface resistivity, R is the surface resistance, and D and L are the spacing and length of the electrodes, respectively. Raman Renishaw microscope (Argon laser at 514 nm) was used for Raman characterization of the graphite, exfoliated graphite, and graphene-coated fabric at ambient temperature. The graphene, neat Polyamide 12 (PA12) fabric, and graphene-coated fabric were


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analyzed by Fourier Transform infrared spectroscopy using Thermo Nicolet 6700 FTIR. The Rigaku Ultrax 18FB X-ray diffractometer, equipped with 18kW rotating anode generator to generate the X-ray diffraction patterns of neat PA fabric and graphene-coated fabric, were performed at ambient temperature in the range of 0 < 2 < 50. RESULTS & DISCUSSION Preparation and optimization of a dispersion of GNP in a solvent mixture A solution-based coating method was proposed to coat GNPs onto the polyamide (PA) fabric. The uniformity, adhesion, and long-term stability of the GNP coated PA fabric depend on the dispersion state and quality of the GNPs in solution. A theoretical-based approach was applied to determine compatible solvent mixtures for GNP. The ability of a solvent to dissolve a non-polar solute was shown to depend on the similarity of their Hildebrand solubility parameters (T)

47.

When a solute is polar, the values of the Hansen solubility parameters (HSPs) (dispersive (D), polar (P), and hydrogen-bonding (H)) of the solvent should be similar to those for the solute 48. Mixture of acetone and water was used to disperse graphite

49

and grephene

50.

In this study,

acetone and water were chosen as the two miscible solvents due to their low-boiling points and the environmental aspects of disposing the solvent residues. The Hildebrand and Hansen solubility parameters for the mixture of solvents were theoretically examined and their relationship with respect to mixture composition is shown in Figure 1. The Ts of the mixtures of acetone and water with different compositions were calculated and compared with that of graphene. The T of a mixture was obtained using its relationship with the Hansen parameters, expressed as 𝛿2𝑇 = 𝛿2𝐷 + 𝛿2𝑃 + 𝛿2𝐻 51. The individual Hansen parameters of a solvent mixture were calculated using i,mix =

h i,h + w i,w, where i denotes D, P and H, h and w denote the volume fraction of acetone


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and water, respectively. The HSP values for acetone and water were published in the literature 48. Therefore, the calculated T of aqueous mixtures varied from 19.9 for 0 wt. % acetone to 30.0 for 100 wt. % acetone, as is illustrated by a straight line in Figure 1 (a). As for the T of graphene, Hernandez et al. demonstrated the value was about 23 MPa1/2 based on the surface-tension analysis of graphene 51. By a comparison, this value was similar to the one for the aqueous solution of 60 wt. % acetone, ~ 23.1 MPa1/2. The relationship between Hansen solubility parameters of solvent mixtures and graphene was also analyzed to determine further guidelines for defining a range of mixture compositions. Based on the HSP theory, a maximum solubility of a solute could be achieved by minimizing the distance between two molecules in Hansen space (Ra) 47,48. In the case of a solvent mixture here, Figure 1 (a) illustrates the theoretical Ra curve as a function of acetone weight fraction in aqueous mixtures. The theoretical data were derived from the HSP values of graphene, D 18 MPa1/2, P 17 MPa1/2, and H 17 MPa1/2 51, and acetone (A)/water (W) mixture, D,W 18 MPa1/2, P,W 10 MPa1/2, and H,W 7 MPa1/2 and D,A 16 MPa1/2, P,A 10 MPa1/2, and H,A 7 MPa1/2 48, following the equation Ra2 = 4 (D1 - D2)2 + (P1- P2)2 + (H1- H2)2 48, where 1 and 2 denotes the graphene and solvent mixture, respectively. As is shown in Figure 1 (a), the theoretical-based calculation provided a broad minimum range of Ra between 50 wt. % to 90 wt. % of acetone, with a minimum value occurred at 80 wt. % of acetone. Considering both Hildebrand and Hansen solubility parameters of a mixture for dissolving graphene, a range of composition between 60 wt. % to 90 wt. % of acetone could be used for creating a mixture with the greatest concentration of solute. With guidance from the theoretical calculations, the first step of the proposed method was to prepare a stable solution of exfoliated GNP. A low-boiling-point solvent without surfactant or stabilizer could not produce a concentrated graphene dispersion 9. Hence, the strategy was to blend


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two mediocre solvents to produce a concentrated GNP dispersion to facilitate the coating process. The optimum solvent mixture composition was experimentally identified by varying the ratio of acetone and water and monitoring the absorbance by UV-Vis spectrophotometer. The concentration of graphene in the dispersion, C, is directly related to the absorbance per unit path length, A/l, based on the Beer-Lambert law, A/l = C, where  is the molar absorption coefficient. A calibration curve was prepared from a known concentration (supplemental information), and  was estimated to be 830 ml mg-1m-1. Because the values of  did not change by varying the mixture composition

50,52,

the absorbance values were used here as an indicator of a change in the

concentration of graphene in the dispersions. Figure 1 (b) shows a typical graphene featureless absorbance in the 400-700 nm region for the supernatants of GNP after sonication and 30 minutes of centrifugation. To make sure the absorbance is only from the GNP, a baseline spectrum was taken from the same solvent mixture. The UV-Vis spectra of aqueous mixtures at 660 nm revealed an increase in the absorbance with increasing the acetone from 40 wt. % to 60 wt. %. While further increasing the acetone content up to 90 wt. % resulted in a decrease in the absorbance values, as is illustrated in Figure 1 (b) (topright figure). This result was also evident by observing the glass vials of the aqueous GNP mixtures at different weight percentages of acetone, as is illustrated in Figure 1 (c). The aqueous solution with 60 wt. % acetone showed the darkest color in comparison to the others containing different weight percentages of acetone, corresponding to the highest concentration of GNP in the aqueous mixture. UV-vis spectra confirmed that the 60% acetone mixture has the highest concentration of GNP (0.1 mg ml-1). As expected, the weight fraction of acetone in solution was within the range derived from the theoretical-based calculations (between 60 wt. % to 80 wt. %), indicating that the composition of the mixture could be theoretically identified prior to experiments by implementing


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the procedure based on the solubility-based approach. Sonication time was found to be as another important variable for yielding high concentration GNP dispersion. Optimal sonication time was determined by taking five samples, which were subjected to different sonication times ranging from 1 to 5 hours followed by 30 minutes of centrifugation. Figure 1 (d) shows the featureless absorbance of dispersions with respect to increasing the time of sonication. The absorbance values (as an indicator of the concentration of GNPs in solvents) increased insignificantly with increasing the sonication time up to 2 hours, while further increasing the time to 3 hours of sonication increased the absorbance value. Also expanding the sonication time up to 5 hours of sonication resulted in the absorbance values to remain nearly unchanged. Therefore, a sonication for 3 hours was required to achieve the highest concentration of graphene in the mixture. At this processing condition, the GNP concentration was equal to ~ 0.1 mg ml-1, which was calculated based on measuring the absorbance of diluted solutions at 660 nm and an absorbance coefficient of 830 ml mg-1m-1. Note that the absorbance value of a dispersion kept at room temperature for four weeks was identical to that measured immediately after sonication, confirming the excellent stability of the dispersion. The quality of the dispersion was comparable to that of NMP, which is known to be one the best solvents for graphene 51.


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Figure 1. A) Theoretical calculations of Hildebrand solubility parameters and distance profile (Ra) in the Hansen space of acetone-water mixtures with respect to acetone mass fraction. The minimum Ra corresponds to maximum solubility of a solute in solvents. B) UV visible spectra of graphene dispersions in various acetone weight fraction. C) Absorbance as a function of acetone mass fraction at the wavelength of 660 nm. Also, glass vials containing dispersions at different acetone weight fractions are illustrated. D) The effect of time of sonication on the graphene dispersion measured for UV absorbance at 660nm. Quality of GNPs in dispersions Considering the effect of the layer thickness of graphene platelets on its electrical properties

53,54

and subsequently the quality of coating, the thickness of GNPs in the dispersion

was determined by AFM and Raman Spectrum. Figure 2 (a) shows a micrograph of dispersed GNPs on a silicon wafer. Figure 2 (b) shows a line-profile of the AFM specimen. Based on the


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height, the thickness of the GNPs are below 10 nm. A histogram [Figure 2 (c)] shows that over 96 % of the GNPs were equal to or below 8-nm thick (estimated from ~ 200 particles), indicating the process resulted in a homogeneous GNP dispersion. The GNP thickness measured by AFM was not directly related to the number of graphene layers. Nevertheless, it qualitatively confirmed the presence of multilayer graphene sheets in the mixture, given the thickness of a single layer of graphene was reported to be between 0.4-1.7 nm 55. This result was further supported by the Raman spectra of the similar specimen, as illustrated in Figure 2 (d). Samples of the neat graphite and the exfoliated graphite after 9 hours of sonication were analyzed. The number of layers of graphene in nano-sheets was determined by the peak position of the 2D band and the relative intensity ratio of 2D to G bands (I2D/IG) 56,57. The broad and less intense peak of the 2D band appeared as multiple merged peaks, where the highest peak occurred at 2702 cm-1. The 2D band of single-layer graphene was often observed to be a single symmetric peak in ~2600 cm-1 58. As the number of layers increased, the peak in the ~2700 cm-1 evolved in size and the peak in the ~2600 cm-1 disappeared. The fact that the 2D band of GNPs was not symmetrical and occurred in 2700 cm-1 region implied that the solute particles were composed of a few layers of graphene. The multilayer formation was also evident by the I2D/IG ratio that was equal to 0.58 59. The defect quantity introduced by the sonication process was also investigated by the Raman spectrum. It was characterized by the relative D- and G-band intensity ratio (ID/IG). As expected, the ID/IG ratio of the exfoliated graphite increased due to the edges defect caused by the shock wave of the bath sonication

50.

It is worth to note that the increase in ID/IG ratio was

insignificant, decreased from 0.33 to 0.38, indicating the sonication treatment and the acetone/water mixture as the solvents system to exfoliate the graphite had a negligible effect on the GNPs.


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Figure 2. A) Micrograph of an AFM sample. B) An example of the thickness profile of GNPs over a 2-m line for a typical AFM specimen. C) A histogram of GNPs frequency after sonicated in an aqueous solution with 60 wt.% of acetone with respect to the thickness of GNPs. D) Raman spectra of neat graphite (top) and GNPs after 9 hours of sonication time (bottom)

Fabrication and characterization of GNP coated fabric The aqueous mixture with 60 wt. % acetone and GNPs was used for fabricating the PA conductive fabric. As illustrated in Figure 3, the intact PA fabric (Figure 3 (a) (left)) was placed


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and sonicated for different times in the scintillation vial to produce a GNP coated fabric. Figure 3 (a) (right) shows an example of a coated fabric that resulted from sonicating for 4 hours and drying in a vacuum oven for 1 hour. The fabric changed its color uniformly from white to black, indicating a successful coating of GNPs on the polymer fabric. Besides, the integrity and feel of the coated fabric did not change as a result of the coating. This coating method is scalable and simple to perform. In the next sections, the coating optimization and characterization are discussed to provide further insights into the quality and the fabrication mechanism of conductive fabric.

Figure 3. A) PA fabric (left) and GNP coated PA fabric (right). The coating was performed in a scintillation vial containing an aqueous solution with 60 wt. % acetone. B) Surface resistivity profile of GNPs coated fabric as a function of sonication time. C) The individual points represent the resistivity of fabrics coated in mixtures of acetone/water/GNP with different acetone weight


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ratio. The solid line represents the distance between graphene and solution mixture with various compositions in Hansen space based on the HSP theory.

Fabric Coating and Process Optimization The coating process of PA fabric was optimized with respect to sonication time and mixture compositions. A dispersion containing an aqueous mixture with 60 wt. % acetone was used for conducting the coatings at different sonication time. For every 1-hour increment, the fabric was removed from the vial and dried in the vacuum oven at 70 C for 1 hour. The surface resistivity of the fabric was examined with respect to an increase in the sonication time, as is illustrated in Figure 3 (b). The surface resistivity of the fabric decreased to its smallest value after 5 hours of sonication and remained relatively unchanged up to 9 hours of sonication. It was concluded that the percolation threshold was achieved at 5 hours. As the time of sonication increased, a higher possibility existed for the GNPs to be exposed and adhered to the surface of PA fibers thereby a decrease in surface resistivity was observed. Furthermore, it has been well demonstrated in the literature that the process of graphene exfoliation could induce defects, mainly in the form of edge defects 60. The existence of greater sonication-induced defects could assist the fixation of GNPs on the PA fabric through the secondary interactions. Further increase in sonication time over 5 hours did not decrease the fabric resistivity due to unavailability of intact surfaces of PA for the GNP to reside as well as the spatial hindrance by the stacks of GNP layers existed on the surface.


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The surface resistivity of treated polyamide fabric using the proposed methodology and its comparison with the published data are shown in Table 1. The surface resistivity value was 29 k/sq for the fabric coated in a dispersion of acetone/water mixture. This value was in the same order of magnitude as those of the fabrics coated by interfacial trapping method 6, doping 22, and physical mixing

61.

Here, the exfoliated sheets, uniform distribution of GNP, and the inherent

nature of reduced graphene were the keys to achieving low resistivity. On the contrary, poor coverage, large flake sizes, and oxidized form of graphite particulates were common issues with the dip coated method. The resistivity of the fabric dip-coated by rGO thus was significantly higher than the other methods 62. Table 1. The comparison of surface resistivity of conductive fabrics manufactured by various methods 6. Conductive Polymer Graphene/Graphite-PA12 MWCNT-PA6 PANI-PAA-MWCNT-PA6 rGO- PA6 Graphene-PA12

Processing Method

Surface Resistivity (k/sq)

Ref.

Interface trapping

4

6

In-situ/doping

31

22

Physical mixing

14

61

Dip-coating

1000

62

Solvent Mixture

29

This Paper

Figure 3 (c) shows the resistivity of the GNP coated fabrics produced in mixtures with different acetone wt. %. The resistivity decreased with increasing the acetone weight fraction in the mixtures up to 50 wt. % and remained constant with minimal change by increasing the acetone content up to 90 wt.%. The range of solvent compositions to achieve the minimum resistivity was identical to the one with maximum absorbance (Figure 1 (b)), indicating the higher concentration of GNP in the mixture resulted in the coated fabric with greater conductivity. The trend in


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resistivity with respect to acetone mass fraction was similar to the one obtained from theoretical Ra values, as such data was also shown in Figure 3 (c) for a side-by-side comparison. The range of solvent compositions from 50-90 wt. % of acetone was corresponded to the broad minimal range of Ra, indicating the HSP theory could provide a theoretical range for solvent compositions without performing an extensive experimental study. Note that the approach using Hildebrand solubility theory resulted in accurately predicting the solvent composition with the highest GNP concentration, while the approach using HSP theory could provide a range for solvents to perform the coating process. The polarity of the solute was the primary factor in determining the appropriate solubility method to calculate the solvent composition. Thus, the two solvent compositions based on the Hildebrand and Hansen solubility parameters to define a range for coating can be derived from the equations below.

{

𝑅𝑎 =

𝐶𝑜𝑚𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛 1 2 2 2 232 = (ℎ.16 + (1 ― ℎ).18)𝐷 + (ℎ.10 + (1 ― ℎ).10)𝑃 + (ℎ.7 + (1 ― ℎ).7)𝐻 ― 𝐶𝑜𝑚𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛 2 𝑑 𝑅𝑎 = 0 𝑤ℎ𝑒𝑟𝑒 𝑑 ℎ 2 2 2 4 (18 ― (ℎ.16 + (1 ― ℎ).18)) + (17 ― (ℎ.10 + (1 ― ℎ).10)) + (17 ― (ℎ.7 + (1 ― ℎ).7))

3.3.2 Characterization of GNP Coated Fabric The coated fabric was examined by SEM to demonstrate the presence of GNPs coated on the PA12 fabric. The fabric showed nearly minimal charging effect during the SEM evaluation, indicating the presence of distributed GNPs on the surface of the fabric. In contrast, as one strand of fiber is illustrated in Figure 3 (a), the control fabric, which was non-conductive, experienced accumulated static electric charges on the surface, thereby deteriorated the image information. Figures 3 (b) to (d) show the SEM micrographs of coated GNPs on the surface of the fabric with different magnifications. The surface morphology of PA fibers is shown in Figure 3 (a), where the


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considerable amount of surface roughness was presented that could lead to physical interlocking of GNP on the surface. The GNPs adhered to the surface, often stacked up and distributed throughout the surface of the fiber. The morphology of the GNPs was observed to have a flaky texture and, the larger flake tended to fold creating a 3D structure (the individual graphene layer was transparent and thus not detectable by SEM). This morphology contributed to the stacking of each graphene flake creating a multilayer of graphene and also the physical bonding between the GNPs and the PA fabric. Graphene naturally tended to form aggregates because of the weak Van der Waals interaction between the individual graphene layers. This interaction, as well as the polar groups present on the surface of graphene, could result in the rebinding of the nearby C dangling bonds that led from the broken C-C bonds upon sonication. The crumpled texture of the graphene provided a larger surface area for graphene to physically bond onto the PA fabric.


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Figure 4. (A) A SEM micrograph of strands of fiber with no GNP coating. (B)-(D) SEM Micrographs of GNP-coated PA fabric at various magnifications. This attachment of GNP on the surface of PA fabric was examined by Raman spectra, as illustrated in Figure 5 (a). Three characteristic peaks were observed on the GNPs coated fabric, which were the D band at ~1363 cm-1, the G band at ~1583 cm-1, and the 2D band at ~2724 cm-1. Similar to the Raman spectra of GNPs in the solution, the Raman spectra of GNP coated fabric indicated the deposition of multilayer graphene. The 2D peak appeared to have at least two merged peaks, where the first peak occurred at 2670 cm-1 and the second peak occurred at 2702 cm-1. Comparing to the literature data, the data fitting using Lorentzian functions indicated the presence


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of GNP on the PA fabric of no more than 7 layers 57, as shown in Figure 5 (b). The ID/IG ratio of GNP coated PA fabric was measured to be 0.72. The increase in ID/IG ratio of GNPs coated PA fabric suggested that the process altered the sp2 structure of the GNPs through more of the sp3 carbon formation. As discussed in the previous section, an increase in the sonication time resulted in the deterioration of the graphene thereby increasing the edge defect density containing oxygenated groups 63. In addition to the physical interlocking of GNP due to the PA surface roughness and noncovalent - interactions among GNPs, the sonication-induced edge defects contributed to the surface bonding between fiber and GNP. Figure 5 (c) shows the FTIR spectra of GNP, PA fabric, and GNP-coated PA fabric (PA-GNP). As illustrated in Figure 5 (c), it was evident that the GNPs contained oxygenated groups. A strong and broad peak in the ~3000 cm-1 region, as well as the missing peak in the ~1700 cm-1 region, indicated the presence of hydroxyl groups of an alcohol, which was the major oxygen-containing group of GNPs. The PA fabric contained an amideterminated group which was evidenced by the presence of strong and sharp C=O stretch at 1640 cm-1 and medium N-H stretch at 3300 cm-1. Furthermore, the neat PA fabric did not show any broad peak at 2500-3200 cm-1 as a result of carboxylic O-H stretch. Considering the aforementioned functional groups on the PA fabric, it was speculated that the fixation of GNP onto the PA fabric occurred likely due to the hydrogen bonding between the amide group of PA fabric and an oxygenated group of GNPs. This was evidenced by the shift of –OH stretch in GNP spectra (~3430 cm-1) to the higher region (~3456 cm-1) in GNP coated fabric , indicating the existence of hydrogen bonding 64. As the crystalline structure greatly affect the mechanical properties of the neat PA fabric, the effect of the coating process on the crystallinity of the PA fabric was examined. The X-ray


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diffractograms of the neat PA fabric and GNPs coated PA fabric was illustrated in Figure 5 (d). There were two peaks centered at 2 = 5.8 and 21, corresponding to the (020) and (100) planes of -phase of the semi-crystalline neat PA12

65.

It was evident that the GNP coated PA fabric

presented the same crystalline structure as the 2 = 5.8 of the neat PA fabric was maintained. Likewise, the crystalline peak at 2 = 21 remained constant (no change intensity), indicating the coating process preserved the crystalline structure of the PA fabric.

Figure 5. A) XRD spectra of untreated PA fabric (bottom) and treated PA fabric (top). B) FTIR spectra of untreated PA fabric and treated PA fabric with GNPs. C) Raman spectra evidence of the successful GNP deposition on the surface of PA fabric. D) Data fitting using Lorentzian functions for the peak at 2702 cm-1.


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CONCLUSION In summary, a liquid-based, facile, and low temperature coating method was proposed to produce a conductive polyamide fabric. By tailoring the solubility parameters of a mixture of two low-boiling-point solvents and with mild sonication, a highly exfoliated, stable, and concentrated dispersion of GNPs was produced. The stable and exfoliated GNPs dispersion was used for a surface coating of fabric. Continuous bath sonication of the dispersion with the nylon fabric stabilized the colloidal dispersion and induced new anchoring sites on the fabric, resulting in a uniform coverage of GNPs on the surface. The treated fabric was highly conductive with minimal defects. This nanoparticle deposition was due to the secondary interaction between the GNPs and PA fabric as well as physical interlocking without affecting the chemical and physical properties of polyamide fabric. Choice of solvents and its compositions should be carefully considered, theoretical guideline based on Hildebrand and HSP theories can reduce the experimental work. Desirable solvents should produce a stable suspension with a high concentration of GNP and should not change the characteristics of the polymeric fabric. ACKNOWLEDGEMENTS The authors sincerely thank Dr. Hilmar Koerner from Wright-Patterson Air Force Research Laboratory (AFRL) for numerous technical discussions and AFRL for summer faculty fellowship. This work is partially supported by Faculty Startup Fund from California State University, Long Beach. REFERENCES (1) (2)

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