A method of improving the dispersion of graphene nanoplatelets in cellulose acetate based composite

Abstract

The application of cellulose acetate (CA) in various industrial areas is strongly influenced by thermal stability. Graphene nanoplatelets (GNPs) as a highly thermal conductive filler is a promising choice to modify the stability of CA, while the practical effect falls short of the expectations associated with the promise of individually dispersed graphene owing to poor dispersion. Supercritical carbon dioxide (SC-CO₂) is favorable to nanoparticles dispersion. Thus, the method, introducing SC-CO₂ into the CA/GNPs system during extrusion process, was proposed to modify GNPs dispersion in CA matrix. As three main process parameters, it was researched that the effects of injection flowrate, temperature and screw rotation-rate on GNPs dispersion in CA matrix. The dispersion quality of GNPs was enhanced with the rising of injection flowrate, and the average visible GNPs count of sample G_1.25C_0.5T₃₇R₉ decreased 74.79%, compared with sample G_1.25C_0.1T₃₇R₉. The dispersion quality of GNPs was enhanced with the rising of temperature, and the average visible GNPs count of sample G_1.25C_0.3T₄₁R₉ decreased 63.63%, compared with sample G_1.25C_0.3T₃₃R₉. The dispersion quality of GNPs decreased with the rising of screw rotation-rate, and the average visible GNPs count of sample G_1.25C_0.3T₃₇R₅ decreased 77.71%, compared with sample G_1.25C_0.3T₃₇R₁₃. Thermogravimetric analysis revealed that a better GNPs dispersion is beneficial to CA stability even at an extremely low level. The maximum-weight-loss rate of the first weight-loss-stage of samples G_1.25C_0.5T₃₇R₉, G_1.25C_0.3T₄₁R₉ and G_1.25C_0.3T₃₇R₅ appeared at 212, 224 and 225°C, while that of samples G_1.25C_0.1T₃₇R₉, G_1.25C_0.3T₃₃R₉ and G_1.25C_0.3T₃₇R₁₃ appeared at 207, 219 and 219°C, with an increment of 5, 5 and 6°C, when the content of introduced GNPs is merely 1.25‰.

Keywords

Cellulose acetate graphene nanoplatelets supercritical carbon dioxide dispersion extrusion

Introduction

Early 20th century, cellulose acetate (CA) was primarily developed as a safe alternative to highly flammable cellulose nitrate.¹Nowadays, it has been widely used in membranes,² fibers,³ plastics,⁴ filters,⁵ etc.⁶ The decomposition and stability of CA considerably affect its application in several industrial areas.⁷ Introducing excellent thermal conductivity fillers is an effective routine to restrain decomposition and enhance stability of polymer materials.^8,9

For traditional high thermal conductivity fillers, great improvement can only be achieved when the weight loads is large enough, which will greatly affect the physical properties of composites.^10,11 In recent years, carbon based materials^12-16 have been gradually employed in thermal systems due to their low density. Whereas there are some factors (material cost and inherent thermal properties, etc.) limited such carbon materials using in composites. Graphene nanoplatelets (GNPs), composed of several layers of graphene, offered an excellent balance between properties and cost given the abundance of naturally existing graphite as the source material for GNPs.¹⁷

Graphene’s unusual thermal properties, originating from its unique two-dimensional structure, provide a solid platform for new findings of novel thermal management applications, with thermal stability modifying included.¹⁸ However, the composite properties fail to meet the expectations associated with the promise of individually dispersed graphene. There exists condition where it is found that thick graphite sheets or platelets perform better in thermal stability modifying than graphene.¹⁹ Therefore, a benign method for dispersion modification of GNPs in composites matrices is to be proposed.

Conventional methods for achieving graphene dispersion in polymer matrices usually include solvent using,²⁰ whereas the use of conventional solvents causes serious health and environmental problems, preventing large-scale use of these solvents. Compared to solvent dispersion, melt blending, that is the direct dispersion of graphene nanophases into molten polymer, is a cleaner process that can be readily scaled up for industrial applications.²¹ However, the processing temperature of melt blending is around 200°C in common condition, which consumes large amount of energy. Besides, new techniques are being tried to improve graphene dispersion, such as supercritical carbon dioxide (SC-CO₂) technique.

Indeed, many researchers are devoting their efforts to modifying graphene dispersion in composites by SC-CO₂ assisted processing. Considering its wetting characteristic,²² promoting phase inversion effect^23,24 and intercalation effect²⁵ of SC-CO₂, introducing it into the processing of material is favorable to improve GNPs dispersion. Generally, high-pressure autoclaves were employed to reach the supercritical state of CO₂, belonging to batch approach and restricting the productivity. To satisfy the ever-expanding demand for CA composites with well dispersed GNPs, it is necessary to develop a continuous manufacturing method. Extrusion is a common continuous manufacturing process, in which single screw extruder is frequently adopted given its simple and cost-effective operation.^26,27 However, it is rarely reported to introduce SC-CO₂ into the screw extrusion process to modify GNPs dispersion in the CA matrix.

In this work, we introduced SC-CO₂ into the CA/GNPs system to enhance GNPs’ dispersion during the extrusion process. Raman spectroscopy, scanning electron microscope (SEM), Fourier-transform infrared (FTIR) spectroscopy, slit-die rheometer and Thermogravimetric (TG) analysis were adopted to characterize the relative properties of CA/GNPs composite. As three main process parameters, it was researched that the influences of injection flowrate, temperature and screw rotation-rate on GNPs dispersion in CA matrix.

Experimental

Materials and devices

Table S1-2 in supplementary information provided the detailed information of materials (CA, acetone, ethanol, GNPs, CO₂ and N₂) and devices.

Sample preparation

Preliminary kneading of GNPs/CA composite

Firstly, CA, ethanol and acetone were measured according to the proportion of 2 g: 1.15 mL: 1.15 mL. Secondly, 0.27‰, 0.64‰, 1.25‰, 2.16‰, 3.43‰ GNPs were respectively weighed according to the weight of CA. Thirdly, the weighted ethanol, acetone and GNPs were added into a beaker for a 15 min sonication treatment with the ultrasonic cleaner. Fourthly, ethanol, acetone, GNPs and CA were added into the kneader for a 15 min kneading at 35°C condition. Finally, the CA/GNPs was stored with sealing bag to reduce the volatilization of ethanol and acetone.

Preparation of CA/GNPs composite samples

Twenty-four types of samples, (content, flowrate, temperature and rotation-rate group), were prepared by SC-CO₂ assisted extrusion device shown in Figure S1 of supplementary information. Table 1 provided exact preparation conditions of CA/GNPs composite samples. The preliminarily kneaded materials was firstly released into the mandatory feeder when the temperature of barrel and slit-die rheometer remain steady. Secondly, all valves in the gas pipeline of SC-CO₂ injection system except the injection valve were turned on. Thirdly, the syringe pump’ pressure was adjusted 0.5 MPa higher than that of second barrel when pressure get stable. Fourthly, the syringe pump was adjusted into specific flowrate. Fifthly, the injection valve was open to inject SC-CO₂ into barrel. Then, the samples were collected when the pressure of rheometer become stable (and measure the volumetric flowrate for rotation-rate group).

Table 1.

Preparation conditions of CA/GNPs samples.

Code	GNPs content (wt‰)	CO₂ flowrate (mL/min)	Temperature (°C)	Rotation-rate (r/min)
G₀C₀T₃₇R₉	0	0	37	9
G₀C_0.3T₃₇R₉	0	0.3	37	9
G_0.27C_0.3T₃₇R₉	0.27
G_0.64C_0.3T₃₇R₉	0.64
G_1.25C_0.3T₃₇R₉	1.25
G_2.16C_0.3T₃₇R₉	2.16
G_3.43C_0.3T₃₇R₉	3.43
G_1.25C₀T₃₇R₉	1.25	0	37	9
G_1.25C_0.1T₃₇R₉		0.1
G_1.25C_0.2T₃₇R₉		0.2
G_1.25C_0.3T₃₇R₉		0.3
G_1.25C_0.4T₃₇R₉		0.4
G_1.25C_0.5T₃₇R₉		0.5
G_1.25C_0.3T₃₁R₉	1.25	0.3	31	9
G_1.25C_0.3T₃₃R₉			33
G_1.25C_0.3T₃₅R₉			35
G_1.25C_0.3T₃₇R₉			37
G_1.25C_0.3T₃₉R₉			39
G_1.25C_0.3T₄₁R₉			41
G_1.25C_0.3T₃₇R₅	1.25	0.3	37	5
G_1.25C_0.3T₃₇R₇				7
G_1.25C_0.3T₃₇R₉				9
G_1.25C_0.3T₃₇R₁₁				11
G_1.25C_0.3T₃₇R₁₃				13

Sample characterization

Dispersion characterization

Raman spectroscopy investigation

In 100-4000 cm⁻¹ range, the Raman spectra of samples were collected by the confocal Raman microscope. In 1480-1680 cm⁻¹ range, the map-image-acquisition function in live track mode was adopted to investigate the distribution of GNPs on samples’ surface.

Optical microscopy images processing

Figure S2 illustrated nine optical microscopy (OM) images were exported from nine random locations on the surface of every sample. Image-Pro Plus 6.0 was applied to process the images and measure the parameters (counts, diameter) of GNPs on samples’ surface in OM images.

Quantification of GNPs’ dispersion degree

Morishita index (I_δ)²⁸ was introduced to evaluate the aggregation degree of dispersed phase

I_{δ} = \frac{δ}{π}

(1)

δ is the probability that two arbitrary GNPs in nine images appear simultaneously in one image

δ = \frac{\sum_{i = 1}^{N} t_{i} (t_{i} - 1)}{T_{i} (T_{i} - 1)}

(2)

N (N=9) is the number of exported images of every sample; t_i is the total counts of GNPs in every exported image; T_i is the total counts of GNPs in all exported images.

π = \frac{1}{N}

(3)

π is the average probability of a GNP appearing in certain image of sample’s images. Practically, the aggregation degree of dispersed phase increase with the increasing of I_δ value.

Scanning electron microscope investigation

The fracture surfaces of samples were investigated by SEM. The fracture surface of samples was gold coated before imaging.

Fourier-transform infrared spectroscopy investigation

In 4000-580 cm⁻¹ range, the FTIR spectrometer with ATR accessories was used to analyze samples.

In-line shear viscosity measurement

The slit-die rheometer (shown in Figure S3) can measure the in-line rheological properties of CA/GNPs solution.²⁹The shear viscosity ( $η$ ) of CA/GNPs solution can be determined through equations (4-7)

η = \frac{τ_{w}}{\dot{γ}}

(4)

τ_{w}

and

\dot{γ}

are wall shear stress and rate of the slit-die rheometer.

τ_{w}

can be calculated by equation (5)

τ_{w} = \frac{H Δ P}{2 L}

(5)

Δ P

is the pressure differential value between two P-T sensors. H (0.003m) is the thickness of flow channel, L (0.065m) is the distance between two adjacent P-T sensors.

\dot{γ} = \frac{6 Q_{V}}{W H^{2}} (\frac{b + 2}{3})

(6)

Q_V is the volumetric flowrate of CA/GNPs solution, which can be calculated with the dimensions of sample extruded in certain time interval, W (0.024m) is the width of flow channel, and

(b + 2) / 3

is the Rabinowitsch correction factor. b is determined according to equation (7)

b \equiv \frac{d [lg (\frac{6 Q}{W H^{2}})]}{d [lg (τ_{w})]}

(7)

Thermogravimetric analysis

Thermogravimetric analyzer was adopted to investigate the thermal property of CA/GNPs samples in 50–600°C range. The measurements were performed in a Al₂O₃ pan, pierced lid in the N₂ atmosphere at 10 °C/min heating rate.

Result and discussion

Raman spectroscopy analysis

Figure S4 illustrated the position where Raman spectra were collected. According to Figure 1(a), the most prominent features in Raman spectrum of GNPs are G and G^′ bands at 1580 and 2709 cm⁻¹, corresponding to in-plane vibration of sp² carbon atoms and layer stacking of carbon atoms.³⁰ While the bands at 1375 cm⁻¹, 1434 cm⁻¹, 1737 cm⁻¹ and 2941 cm⁻¹ were most noticeable in the Raman spectra of sample G₀C₀T₃₇R₉ and G_1.25C₀T₃₇R₉ (point Ⅱ).³¹ G band at 1580 cm⁻¹ is the most outstanding among all the characteristic bands of the spectrum of sample G_1.25C₀T₃₇R₉ (point Ⅰ), not for sample G_1.25C₀T₃₇R₉ (point Ⅱ). Therefore, the G band is suitable to distinguish GNPs and CA in map-image-acquisition process.

Figure 1.

(a): Raman spectra of GNPs, sample G₀C₀T₃₇R₉ and G_1.25C₀T₃₇R₉ (point Ⅰ and Ⅱ); (b): OM image of sample G_1.25C₀T₃₇R₉; (c): Raman map image in the 1480-1680 cm⁻¹ ranges of sample G_1.25C₀T₃₇R₉.

Figure 1(b-c) presented the OM and Raman map-image of sample G_1.25C₀T₃₇R₉ in 1480-1680 cm⁻¹ range acquired from the same area. Figure 1(c) illustrated these areas marked with circles possess relatively more vigorous peak intensity, although not the strongest but correspond to the white plates in Figure 1(b) one-to-one. Which further confirmed the white plates randomly dispersed in Figure 1(b) are GNPs. Comprehensively, the OM images are more acceptable for dispersion characterizing due to sharper GNPs outline and less image acquisition time. As Figures S5-8 illustrated, the OM images were processed with Image-Pro Plus 6.0 software to be counted more accurately.

Average count and diameter(mean) distribution of GNPs in samples’ OM images

Figure 2(a) reflected the tendency of average count of GNPs, approximately classified as linear increment within the 0.27-3.43‰ content range. The standard deviation of GNPs counts increased with the increasing of content. Figure 2(b) showed the average count rose suddenly within 0-0.1 mL/min, then it gradually decreased within the 0.1 -0.5 mL/min range. The standard deviation of GNPs counts maintained a similar tendency with the average count curve. Figure 2(c) revealed the average count increased at first (31–33°C) and then decreased gradually with the temperature enhancing (33-41°C). Figure 2(d) presented the average count climbed with the increasing of rotation-rate. Furthermore, the inset plot of dispersion index I_δ fluctuated within minimal range, indicating the GNPs counts of all group samples varied based on a similar dispersion extent foundation.

Figure 2.

Average count with a I_δ inset graph of (a): content group (G_0.27-3.43C_0.3T₃₇R₉), (b): flowrate group (G_1.25C_0-0.5T₃₇R₉), (c) temperature group (G_1.25C_0.3T_31-41R₉), (d): rotation-rate group (G_1.25C_0.3T₃₇R_5-13) samples’ OM images.

Figure 3(a-d) presented the diameter (mean) distribution of GNPs in four group samples’ OM images, illustrating the diameter tended to distribute on the minor side for all group samples. Table S3 provided the detailed diameter (mean) distribution of GNPs, indicating it maintained relatively stable.

Figure 3.

Diameter(mean) distribution diagram of (a): content group (G_0.27-3.43C_0.3T₃₇R₉); (b): flowrate group (G_1.25C_0-0.5T₃₇R₉); (c) temperature group (G_1.25C_0.3T_31-41R₉); (d): rotation-rate group (G_1.25C_0.3T₃₇R_5-13).

Optical microscopy and SEM micrographs analysis

A contradiction was exposed that sample’s average GNPs count with different process parameters drastically varied, while the dispersion index I_δ and diameter distribution maintained stable. It can be explained by the transparency variety of GNPs dispersed in CA matrix treated by SC-CO₂. Almost no transparent GNPs can be seen in Figure 4(a), (c), and (f), while Figure 4(b), (d), and (e) contained transparent and semi-transparent GNPs. Relevant research revealed that the optical reflection contrast enhances, and the transmittance declines with the increasing of graphene layer number.³² Accordingly, SC-CO₂ assisted extrusion process affected the layer number of GNPs dispersed in CA matrix.

Figure 4.

OM image of samples (a): G_1.25C_0.1T₃₇R₉; (b): G_1.25C_0.5T₃₇R₉; (c): G_1.25C_0.3T₃₃R₉; (d): G_1.25C_0.3T₄₁R₉; (e): G_1.25C_0.3T₃₇R₅; (f): G_1.25C_0.3T₃₇R₁₃.

Scanning electron microscope technique is not enough to visualize GNPs. However, the exfoliation process is very complex and with a high probability of not obtaining the whole range of particles. The not exfoliated platelets remain as stacks which can be dispersed, broken, etc. This effect can be detected by analyzing SEM micrographs, which sample preparation is quite simple among the electron microscope techniques. Figure 5(a-h) showed the SEM images of fracture surfaces of samples G_0.27C_0.3T₃₇R₉ G_3.43C_0.3T₃₇R_9, G_1.25C_0.1T₃₇R₉, G_1.25C_0.5T₃₇R₉, G_1.25C_0.3T₃₃R₉, G_1.25C_0.3T₄₁R₉, G_1.25C_0.3T₃₇R₅ and G_1.25C_0.3T₃₇R₁₃, from which we can figured out that the GNPs dispersed on the fracture surface of sample G_3.43C_0.3T₃₇R₉ were obviously more than that of sample G_0.27C_0.3T₃₇R₉. Besides, the stacking phenomenon of GNPs dispersed on the fracture surface of sample G_3.43C_0.3T₃₇R₉, G_1.25C_0.1T₃₇R₉, G_1.25C_0.3T₃₃R₉, and G_1.25C_0.3T₃₇R₁₃ was severer compared with that of sample G_0.27C_0.3T₃₇R₉, G_1.25C_0.5T₃₇R₉, G_1.25C_0.3T₄₁R₉ and G_1.25C_0.3T₃₇R₅. SEM micrographs furtherly verified the supposal that the SC-CO₂ assisted extrusion process affected the layer number of GNPs dispersed in CA matrix.

Figure 5.

Scanning electron microscope images of samples (a): G_0.27C_0.3T₃₇R₉; (b): G_3.43C_0.3T₃₇R₉; (c): G_1.25C_0.1T₃₇R₉; (d): G_1.25C_0.5T₃₇R₉; (e): G_1.25C_0.3T₃₃R₉; (f): G_1.25C_0.3T₄₁R₉; (g): G_1.25C_0.3T₃₇R₅; (h): G_1.25C_0.3T₃₇R₁₃.

Fourier-transform infrared spectroscopy investigation

Graphene nanoplatelets’ FTIR spectra in Figure 6(a) presented no noticeable characteristic peaks, except the 2330.12 cm⁻¹ band due to asymmetric tensile vibration of atmospheric CO₂. It indicated that there were few oxygen-containing functional groups on GNPs’ surface, corresponding to the fact that the commercial GNPs were fabricated by liquid-phase exfoliation method.^33-35 As for the remaining curves in Figure 6(a-d), similar characteristic peaks were observed around 1030.63, 1216.19, 1368.08 and 1733.30 cm⁻¹, except the broad band around 3460 cm⁻¹ due to atmospheric moisture.³⁶ It can be inferred the SC-CO₂ assisted extrusion process and the introducing of GNPs have limited effect on samples’ characteristic peak wavenumbers. Accordingly, there is no solid chemical interaction between GNPs and CA matrix.

Figure 6.

Fourier-transform infrared spectra of samples (a): (G_0-3.43C_0.3T₃₇R₉) and pure GNPs; (b): (G_1.25C_0-0.5T₃₇R₉); (c): (G_1.25C_0.3T_31-41R₉); (d): (G_1.25C_0.3T₃₇R_5-13).

Influences of process parameters

Figure 7 illustrated the process was divided into two states based on the quantity of injected SC-CO₂. Figure 6(b) revealed there was no strong chemical interaction between GNPs and matrix. At 0.1-0.3 mL/min state, limited SC-CO₂ dissolved in CA matrix, leading to CO₂ molecules preferentially aggregating at the interface of GNPs and CA matrix. The aggregation of CO₂ molecules further weakened the shear stress that matrix implemented to GNPs’ outer layer, making it harder for GNPs to be exfoliated than without SC-CO₂ assistance state. At 0.3-0.5 mL/min state, enough SC-CO₂ dissolved in CA matrix, leading to CO₂ molecules not only aggregating at the interface of GNPs and CA matrix but also inter the GNPs layers. Which simultaneously weakens the shear stress that matrix implements to GNPs’ outer layer and the interaction of GNPs’ interlayers. Meanwhile, CO₂ molecules enhanced CA chains’ motion ability due to the disentanglement, interaction weakening and lubrication effects. Therefore, CA chains moved more easily into the interval of GNPs layers with the rise of CO₂ injection flowrate.³⁷ And the average visible GNPs count of samples G_1.25C_0.5T₃₇R₉ decreased by 74.79% compared with the samples G_1.25C_0.1T₃₇R₉.

Figure 7.

Exfoliation schematic diagram of GNPs with SC-CO₂ assistance.

For temperature group, injected CO₂ was beneath the critical point under 31°C condition, leading to few CO₂ penetrated in CA matrix. Under 33°C condition, CO₂’s dissolubility underwent a sudden rise, causing more CO₂ dissolving in CA matrix. However, the diffusion coefficient of SC-CO₂ was not large enough to support its diffusing into GNPs’ interlayer. Figure 6(c) showed there was no intense chemical interaction between GNPs and CA matrix, so CO₂ molecules were likely to aggregate at the interface of GNPs and CA matrix, which further weakened the interaction of GNPs and CA matrix. Accordingly, the average GNPs count abruptly increased at 33°C. As the temperature rose continuously, the aggregation of CO₂ molecules within the interval of GNPs layers increased owing to dissolubility enhancing, which further weakened GNPs’ interlayer interaction. Macroscopically, the average count of visible GNPs decreased with the temperature climbing, that of samples G_1.25C_0.3T₄₁R₉ decreased 63.63% compared with the samples G_1.25C_0.3T₃₃R₉. The result implied a high temperature of the SC-CO₂ during extrusion process is favorable to reduce layer numbers of GNPs dispersed in CA matrix.

Figure 8 provided the negative correlation between $\dot{γ}$ and $η$ of CA/GNPs solution, while the inset graph of Figure 8 illustrated the logarithmic relation of $\dot{γ}$ and $η$ with linear relation. Therefore, the power model can be applied to describe the in-line rheological behaviors of CA/GNPs solution.²⁹ Equation (8) can be obtained by linear fitting of lg $(\dot{γ})$ and lg $(η)$ .

η = K \times {\dot{γ}}^{n - 1} = 39.084 \times {\dot{γ}}^{- 0.741}

(8)

K (kPa·sⁿ) is the viscous coefficient, and n is the flow index equals 0.259, indicating that CA/GNPs solution is a pseudoplastic fluid. It means that the shear viscosity of the CA/GNPs solution decrease with shear rate rising. Shear rate is positively correlated to screw rotation-rate.³⁸ Thus, the shear viscosity or inter friction of CA/GNPs solution decreased with the increasing of rotation-rate.³⁹ Although SC-CO₂ can reduce the interaction of GNPs interlayers, the final exfoliation of GNPs relies on the shear stress that CA matrix implement on GNPs’ outer layer. The enhanced screw rotation-rate reduced the inter friction of the CA/GNPs solution and further reduced the shear stress that CA matrix implement to GNPs’ outer layer. Besides, the residence time of CA/GNPs solution in the extruder’s barrel shortened at high screw rotation-rate, which is not conducive to the process of CA/GNPs solution’s absorbing SC-CO₂.⁴⁰ And this will further increase the exfoliation difficulty of GNPs in CA matrix. Under the combined effect of two aspects, the dispersion quality of GNPs in CA matrix decreased, and the average visible GNPs count of sample G_1.25C_0.3T₃₇R₅ decreased 77.71% compared with sample G_1.25C_0.3T₃₇R₁₃.

Figure 8.

Flow curve of rotation-rate group (G^1.25C^0.3T³⁷R^5-13).

Influences of GNPs' dispersion on the thermal stability of CA/GNPs samples

Thermogravimetric analysis was adopted to study whether there was an improvement of CA/GNPs samples’ thermal stability. TG curves in Figure 9(a) revealed the presence of a two-stage weight-loss process, ascribed to the thermal decomposition⁴¹ with about 2% and 88% weight-loss. From the DTG curves, the weight-loss process appeared in the 214–240°C and 290–420°C ranges, respectively. From the inset graph of Figure 9(b), the maximum loss rate of the first weight-loss stage located at 229°C and 223°C for samples G_0.27C_0.3T₃₇R₉ and G_3.43C_0.3T₃₇R₉. While the maximum-weight-loss rate of the second weight-loss stage (at 372°C) showed no difference between samples G_0.27C_0.3T₃₇R₉ and G_3.43C_0.3T₃₇R₉, perhaps due to the GNPs content is too low to influence the second weight-loss stage. However, it was evident that introducing GNPs affected the decomposition of the first weight-loss stage of samples. The sample G_0.27C_0.3T₃₇R₉ possessed a better thermal stability than that of sample G_3.43C_0.3T₃₇R₉, which might be ascribed to the thickness diversity of GNPs in samples. The excellent thermal conductivity of GNPs will decrease with the increase of GNP layers.¹⁸ As a result, GNPs filler will reversely decrease the thermal conductivity of polymer composites and further weaken the thermal stability of samples.

Figure 9.

Thermogravimetric curves of samples (a): G_0.27C_0.3T₃₇R₉ and G_3.43C_0.3T₃₇R₉; (c): G_1.25C_0.1T₃₇R₉ and G_1.25C_0.5T₃₇R₉; (e): G_1.25C_0.3T₃₃R₉ and G_1.25C_0.3T₄₁R₉; (g): G_1.25C_0.3T₃₇R₅ and G_1.25C_0.3T₃₇R₁₃; DTG curves of samples (b): G_0.27C_0.3T₃₇R₉ and G_3.43C_0.3T₃₇R₉; (d): G_1.25C_0.1T₃₇R₉ and G_1.25C_0.5T₃₇R₉; (f): G_1.25C_0.3T₃₃R₉ and G_1.25C_0.3T₄₁R₉: (h): G_1.25C_0.3T₃₇R₅ and G_1.25C_0.3T₃₇R₁₃.

As analyzed in the previous section, injection flowrate, temperature and screw rotation-rate influenced the dispersion of GNPs in samples. Moreover, the GNPs in the samples G_1.25C_0.5T₃₇R₉, G_1.25C_0.3T₄₁R₉ and G_1.25C_0.3T₃₇R₅possessed fewer layers than samples G_1.25C_0.1T₃₇R₉, G_1.25C_0.3T₃₃R₉ and G_1.25C_0.3T₃₇R₁₃. Figure 9(c-h) presented the TG and DTG curves of CA/GNPs samples with different preparation condition. All the TG curves displayed similar two-stage weight-loss shapes, while the DTG curves presented the various temperature, that the maximum-weight-loss rate of the first weight-loss stage appeared at. When the introduced GNPs content is merely 1.25‰, the maximum-weight-loss rate of the first weight-loss-stage of samples G_1.25C_0.5T₃₇R₉, G_1.25C_0.3T₄₁R₉ and G_1.25C_0.3T₃₇R₅ appeared at 212, 224 and 225°C, while the maximum-weight-loss rate of the first weight-loss-stage of samples G_1.25C_0.1T₃₇R₉, G_1.25C_0.3T₃₃R₉ and G_1.25C_0.3T₃₇R₁₃ appeared at 207, 219 and 219°C, with an increment of 5,5 and 6°C, respectively.

Conclusion

The SC-CO₂ assisted extrusion process positively promotes GNPs dispersion in the CA matrix, which is mainly reflected in the GNPs layer numbers. Besides, the process has limited influence on the GNPs’ diameter and dispersion uniformity. SC-CO₂ injection flowrate, temperature, and rotation-rate show different influence tendencies as three main factors. For SC-CO₂ injection flowrate, low flowrate is unfavorable to GNPs dispersion due to the aggregation of CO₂ molecules between GNPs’ outer layer and CA matrix weakening the stress that CA matrix implemented to GNPs. However, high flowrate is favorable to GNPs dispersion which is attributed to the SC-CO₂ simultaneously weakening the inter-layer interaction of GNPs. In case of the temperature, a high temperature is favorable to GNPs dispersion within the range from 33°C to 41°C, owing to the high diffusivity of SC-CO₂ at high temperature. For screw rotation-rate, a high rate is unfavorable to GNPs dispersion due to the shear thinning effect and residence time shortening of CA/GNPs solution. Finally, TG analysis reveals that the well GNPs dispersion is beneficial for improving the thermal stability of CA/GNPs composite, even when the content of GNPs is merely 1.25‰. Based on the results obtained, improving GNPs dispersion of through supercritical assisted extrusion still exists inadequacies as compared with previous works. For instance, not referring the improving effect of high GNPs content, not investigating other properties and not substituting solvent thoroughly. These directions mentioned above are worth investigating in future research. However, the method of improving GNPs dispersion by supercritical assisted extrusion still possesses an extensive application prospect, such as biosensor technology, biomedical applications and nanofiltration membrane, etc.

Supplemental Material

Supplemental Material - A method of improving the dispersion of graphene nanoplatelets in cellulose acetate based composite

Supplemental Material for A method of improving the dispersion of graphene nanoplatelets in cellulose acetate based composite by Qipeng Hu, Han Gu, Chao Wang, Yajun Ding and Sanjiu Ying in Polymers and Polymer Composites

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Yajun Ding

Sanjiu Ying

Supplemental Material

Supplemental material for this article is available online.

References

Schilling

Bouchard

Khanjian

, et al. Application of chemical and thermal analysis methods for studying cellulose ester plastics. Acc Chem Res 2010; 43: 888–896.

Abdel Hameed

Abdel Gawad

Emam

. Macroporous Cu-MOF@cellulose acetate membrane serviceable in selective removal of dimethoate pesticide from wastewater. J Environ Chem Eng 2021; 9: 105121.

Chen

Qiu

Chen

, et al. Electrospun thymol-loaded porous cellulose acetate fibers with potential biomedical applications. Mater Sci Eng C 2020; 109: 110536.

Gilbert

. Chapter 22 - cellulose plastics. In: Gilbert

(ed) Brydson’s plastics materials. 8th ed. Butterworth-Heinemann, 2017, pp.617–630.

Joly

Coulis

. Comparison of cellulose vs. plastic cigarette filter decomposition under distinct disposal environments. Waste Manage 2018; 72: 349–353.

Puls

Wilson

Hölter

. Degradation of cellulose acetate-based materials: a review. J Polym Environ 2011; 19: 152–165.

Lucena

MdCC

de Alencar

AEV

Mazzeto

, et al. The effect of additives on the thermal degradation of cellulose acetate. Polym Degrad Stab 2003; 80: 149–155.

Hou

Yang

, et al. Preparation and characterization of surface modified boron nitride epoxy composites with enhanced thermal conductivity. RSC Adv 2014; 4: 44282–44290.

Yang

Hou

, et al. Flame retarding epoxy composites with poly (phosphazene-co-bisphenol A)-coated boron nitride to improve thermal conductivity and thermal stability. RSC Adv 2017; 7: 6140–6151.

10.

, et al. Thermal conductivity enhancement of epoxy adhesive using graphene sheets as additives. Int J Therm Sci 2014; 86: 276–283.

11.

Uslu

Gavgali

Erdal

, et al. Determination of mechanical properties of polymer matrix composites reinforced with electrospinning N66, PAN, PVA and PVC nanofibers: a comparative study. Mater Today Commun 2021; 26: 101939.

12.

Yue

Pircheraghi

Monemian

, et al. Epoxy composites with carbon nanotubes and graphene nanoplatelets – Dispersion and synergy effects. Carbon 2014; 78: 268–278.

13.

Chatterjee

Nafezarefi

Tai

, et al. Size and synergy effects of nanofiller hybrids including graphene nanoplatelets and carbon nanotubes in mechanical properties of epoxy composites. Carbon 2012; 50: 5380–5386.

14.

Ramesh

Sun

, et al. Enhanced thermal conductivity in a hybrid graphite nanoplatelet – carbon nanotube filler for epoxy composites. Adv Mater 2008; 20: 4740–4744.

15.

Schwamb

Burg

Schirmer

, et al. An electrical method for the measurement of the thermal and electrical conductivity of reduced graphene oxide nanostructures. Nanotechnology 2009; 20: 405704.

16.

Gemi

Yazman

Uludağ

, et al. The effect of 0.5 wt% additions of carbon nanotubes and ceramic nanoparticles on tensile properties of epoxy-matrix composites: a comparative study. Mater Sci Nanotechnol 2017; 1: 15–22.

17.

Zhong

. Review on polymer/graphite nanoplatelet nanocomposites. J Mater Sci 2011; 46: 5595–5614.

18.

Huang

Zhi

Lin

, et al. Thermal conductivity of graphene-based polymer nanocomposites. Mater Sci Eng R Rep 2020; 142: 100577.

19.

Sellan

Pettes

, et al. Enhanced thermal conductivity of phase change materials with ultrathin-graphite foams for thermal energy storage. Energy Environ Sci 2014; 7: 1185–1192.

20.

Johnson

Dobson

Coleman

. A manufacturing perspective on graphene dispersions. Curr Opin Colloid Interf Sci 2015; 20: 367–382.

21.

Sanes

Sánchez

Pamies

, et al. Extrusion of polymer nanocomposites with graphene and graphene derivative nanofillers: an overview of recent developments. Materials 2020; 13: 549.

22.

Zhao

Liu

, et al. An advanced electrocatalyst with exceptional eletrocatalytic activity via ultrafine Pt-based trimetallic nanoparticles on pristine graphene. Carbon 2015; 87: 116–127.

23.

Wang

, et al. Reverse-micelle-induced exfoliation of graphite into graphene nanosheets with assistance of supercritical CO₂. Chem Mater 2015; 27: 3262–3272.

24.

Baldino

Sarno

Cardea

, et al. Formation of cellulose acetate–graphene oxide nanocomposites by supercritical CO₂ assisted phase inversion. Ind Eng Chem Res 2015; 54: 8147–8156.

25.

Chen

Miao

, et al. Scalable production of hydrophilic graphene nanosheets via in situ ball-milling-assisted supercritical CO₂ exfoliation. Ind Eng Chem Res 2017; 56: 6939–6944.

26.

Ariffin

Ahmad

MSB

. Review: single screw extruder in particulate filler composite. Polym Plast Technol Eng 2011; 50: 395–403.

27.

Vedernikov

Nasonov

Korotkov

, et al. Effects of additives on the cure kinetics of vinyl ester pultrusion resins. J Compos Mater 2021; 55: 2921–2937.

28.

Morisita

. Iσ-Index, a measure of dispersion of individuals. Res Popul Ecol 1962; 4: 1–7.

29.

Ding

Wei

Ying

. In-line rheological behaviors of gun propellant substitute assisted with supercritical CO₂ in extrusion processing. Propell Explos Pyrot 2017; 42: 247–252.

30.

Malard

Pimenta

Dresselhaus

, et al. Raman spectroscopy in graphene. Phys Rep 2009; 473: 51–87.

31.

Zhang

Feldner

Fischer

. FT Raman spectroscopic investigation of cellulose acetate. Cellulose 2011; 18: 995–1003.

32.

Skulason

Gaskell Pe Fau Szkopek

Szkopek

. Optical reflection and transmission properties of exfoliated graphite from a graphene monolayer to several hundred graphene layers. Nanotechnology 2010; 21: 1361–6528.

33.

Cheng

Huang

Wang

, et al. Synthesis of graphene oxide/polyacrylamide composite membranes for organic dyes/water separation in water purification. J Mater Sci 2019; 54: 252–264.

34.

Faniyi

Fasakin

Olofinjana

, et al. The comparative analyses of reduced graphene oxide (RGO) prepared via green, mild and chemical approaches. SN Appl Sci 2019; 1: 1181.

35.

Mehmood

Mubarak

Khalid

, et al. Graphene/PVA buckypaper for strain sensing application. Sci Rep 2020; 10: 20106.

36.

Wang

Liang

Bai

, et al. All cellulose composites based on cellulose diacetate and nanofibrillated cellulose prepared by alkali treatment. Carbohydr Polym 2018; 179: 297–304.

37.

Suter

Sinclair

Coveney

. Principles governing control of aggregation and dispersion of graphene and graphene oxide in polymer melts. Adv Mater 2020; 32: 2003213.

38.

Kunchimon

Tausif

Goswami

, et al. Polyamide 6 and thermoplastic polyurethane recycled hybrid Fibres via twin-screw melt extrusion. J Polym Res 2019; 26: 162.

39.

Sun

Zhou

, et al. Research on configuration of polymer molecular aggregate and its reservoir applicability. J Mater Sci Technol 2016; 37: 908–917.

40.

Ceng

Cai

, et al. A single-phase of supercritical CO₂/Polystyrene solution in foam extrusion. J Wuhan Univ Technol-Mat Sci Edit 2008; 23: 804–808.

41.

Barud

de Araújo Júnior

Santos

, et al. Thermal behavior of cellulose acetate produced from homogeneous acetylation of bacterial cellulose. Thermochim Acta 2008; 471: 61–69.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

1.50 MB