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clay nanocomposites: A molecular dynamic simulation approach
Polymer Testing 45 (2015) 139e151
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Polymer Testing journal homepage: www.elsevier.com/locate/polytest
Property modelling
On O2 gas permeability of PP/PLA/clay nanocomposites: A molecular dynamic simulation approach Hassan Ebadi-Dehaghani a, Mehdi Barikani a, *, Hossein Ali Khonakdar a, b, Seyed Hassan Jafari c, Udo Wagenknecht c, Gert Heinrich c a b c
Iran Polymer and Petrochemical Institute, P.O. Box 14965/115, Tehran, Iran Leibniz Institute of Polymer Research Dresden, D-01067, Dresden, Germany School of Chemical Engineering, College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Iran
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 April 2015 Accepted 22 May 2015 Available online 29 May 2015
Polypropylene/poly(lactic acid) (PP/PLA) blends were prepared by reactive blending in a co-rotating twin-screw extruder. Effect of blend composition on oxygen permeability through compression molded films was investigated and correlated to the microstructure. Two blends, PP-rich (75/25) and PLArich (25/75), were selected in order to consider the effect of incorporation of clay and compatibilization on the permeability. The PP-rich blends had greater oxygen barrier properties compared to those of PLA-rich blends. Several proposed models of permeability for blends and nanocomposites were examined. These models failed to predict the permeability values, especially for PLA-rich blends. The failure was attributed to the immiscibility issue leading to appearance of microvoids at the interface. Molecular dynamics simulations were performed by employing the COMPASS force field to estimate the diffusivity of oxygen gas through pure components, PP-rich and PLA-rich systems. The simulated results were in good agreement with the available experimental data. © 2015 Elsevier Ltd. All rights reserved.
Keywords: PP/PLA blends Oxygen permeability Molecular dynamic simulation
1. Introduction Controlling permeation of gases and liquids through plastic films, membranes and other articles is of great importance in several packaging and industrial applications [1e7]. The general approach in preparing high performance barriers is to use materials having low diffusivity and low solubility of the permeating material. Preparation of barrier films in the packaging industries is carried out via multi-layer co-extrusion, extrusion coating and adhesive lamination processes [1]. These are complex and expensive technologies and the final products are not recyclable. There are trends to replace them with simple processes. The blends of commodity and barrier polymers e.g. ethylene vinyl alcohol (EVOH), polyvinylidene chloride (PVdC), poly(ethylene terephthalate) (PET) and liquid crystals [1e6] are the alternatives. Addition of a small quantity of barrier material into a low-cost matrix can lead to a low-cost product with greatly improved barrier properties [7].
* Corresponding author. E-mail addresses: [email protected] (M. Barikani), [email protected] (H.A. Khonakdar). http://dx.doi.org/10.1016/j.polymertesting.2015.05.010 0142-9418/© 2015 Elsevier Ltd. All rights reserved.
Polypropylene (PP) is a commodity polymer with suitable mechanical properties and good barrier to H2O, but its poor barrier to O2 limits its applications [3]. Conversely, poly(lactic acid) (PLA) is well-known biodegradable polyester with significantly lower O2 permeability and poor barrier to H2O as compared to PP. Meanwhile, incorporation of layered nanoparticles, such as layered silica, organoclay and layered titanate, is reported to decrease the O2 and H2O vapor permeability [8e12]. Thermodynamics play a strong role in intercalation/exfoliation processes, hence it can provide strong interactions at interface region having a fundamental influence on the mass transport and polymer diffusion. In this regard, blending of PP and PLA as a green polymer can be a good selection for solving the permeability problems of the two polymers, and incorporation of clay can improve these properties. Therefore, this hybrid is a biodegradable material with enhanced mechanical and barrier properties. Prediction of permeability of blends, nanocomposites and hybrids is an important issue. There are several proposed models for prediction of permeability that will be discussed in the next part [13e22]. Moreover, molecular dynamics (MD) simulation is another alternative. MD simulation is a useful method for prediction of the diffusion of gases through polymer films and membranes. The diffusion of small molecules
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such as He, O2 CO2, CH4 through glassy polymers, partially crystalline polymers and polymer membranes is of great practical interest in relation to a number of current and possible future practical applications. This area offers many opportunities for the use of computer modeling for optimizing the molecular design of host polymers [23e26]. In our previous works, we reported on the effect of composition and compatibilization on the morphology and rheological properties of PP/PLA blends [27]. Moreover, we considered these effects and also incorporation of clay nanoparticles on mechanical [28] and crystallization behavior [29] of the hybrid system. The aim of the present work is to investigate oxygen gas permeability in PP/PLA/ clay nanocomposite films and its dependency on blend composition, clay loading, state of clay dispersion governed by compatibilization, and crystallization behavior induced by changing the composition and clay incorporation. Moreover, molecular dynamics simulation of O2 permeability is performed for the pure polymers and the blends.
penetration of gas molecules. This concept can be defined as the tortuosity factor (t):
t¼
D0 D
(6)
K¼
P 1f ¼ P0 t
(7)
where 0 subscript indicates the neat polymer. Several attempts, including analytical and numerical modeling, have been made to predict the tortuosity parameter and permeability of filled systems. The main models are summarized in Table 1 [13e22]. 1.2. Molecular dynamics simulations MD simulations solve Newton's equations of motion for a system of N interacting atoms [24]:
v2 ri ¼ Fi ; i ¼ 1…N: vt 2
1.1. Permeation models for blends and nanocomposites
mi
The presence of an impermeable dispersed phase increases the tortuosity of the path that a molecule must traverse in permeating through a film. It is useful to discuss this concept in terms of a tortuosity factor t, which is the effective path length divided by the actual thickness of the film. It is dependent on the volume fraction of impermeable dispersed phase (Equation (1)). This factor can be used to calculate the permeability of the composite, as presented in Equation (2) [1].
The forces are the negative derivatives of a potential function V (r1, r2, …, rN):
t¼1þ
fd 2
Pc f ¼ m Pm t
(1)
(2)
where Pc is the permeability of the composite, Pm is the permeability of the matrix and fmis the volume fraction of matrix. Robeson [1] extended Maxwell's work by applying it to blends for which both components are permeable. Pc derived by Robeson is given in the following equations, where the polymeric components are denoted by the subscripts 1 and 2, and fi stands for the volume fraction of phase i. Lower bound so-called series model (layers normal to permeant flow):
Pc ¼
P1 P2 ðf1 P2 þ f2 P1 Þ
(3)
Upper bound so-called parallel model (layers parallel to permeant flow):
Pc ¼ f1 P1 þ f2 P2
(4)
Paul determined semi-logarithmic mixture law for blends as follows [1]:
ln P ¼ f1 ln P1 þ f2 ln P2
(5)
Here, fi is the volume fraction of the ith component. Assuming PLA as an oxygen barrier component compared to PP, the mentioned models can be used for unfilled blends. In the case of the nanocomposites, the impermeable silicate layers also create a tortuous path in the polymer matrix and, therefore, gas molecules penetrate with more difficulty. Therefore, it is useful to discuss proposed models for permeation in composites. Because of function of layered silicates as the impermeable barrier, a tortuous path in the polymer matrix can be created, leading to a difficult
Fi ¼
(8)
vV vri
(9)
The equations are solved simultaneously in small time steps. The system is followed for some time, taking care that the temperature and pressure remain at the required values, and the coordinates are written to an output file at regular intervals. The coordinates as a function of time represent a trajectory of the system. After initial changes, the system usually reaches an equilibrium state. By averaging over an equilibrium trajectory, many macroscopic properties, such as diffusion processes in membranes can be extracted from the output file [24]. The diffusivity of a gas in an organic solvent, polymer, or zeolite can be calculated by running an MD simulation and determining the mean square displacement (MSD) of the gas in the material. For long periods of times, during which the penetrant molecules have random walks in the polymer matrix, the mean-square-displacement becomes linear in time, and the diffusion coefficients can be calculated using the Einstein relation [24]:
1 d lim D¼ 6N t/∞ dt
*
N X
+ ½ri ðtÞ ri ð0Þ
2
(10)
i
The motion pattern of penetrant gases in host polymer can be qualitatively studied by monitoring the penetrant's displacement jr(t)r(0)j from its initial position. The diffusion coefficient D is then obtained from the slope of a plot of the mean squared displacement against time t.
*
N X
+ ½ri ðtÞ ri ð0Þ2
i
2. Experimental 2.1. Materials PP (PP Moplen HP500N) with melt flow index of 2.1 g/10min (230 C/2.1 kg), density of 0.9 g/cm3 and number average molecular weight of 80000 g/mol was supplied by Basell Co. PLA (4043D
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Table 1 Detailed schematics and predicted relative permeability values for each model discussed in the text. Model
Filler type
Particle geometry and geometrical assumption
Formulas of tortuosity parameter (t)
Nielsen [15]
Ribbona
Aligned ribbons with infinite length
1 þ af 2
Cusslerc [16]
Ribbona
Aligned ribbons with infinite length (m ¼ 1 [20])
"
# ðafÞ2 1f
1 þ m4
Ribbons with infinite length and equal probability of alignment and misalignment (m ¼ 1/2 [21])
Hexagonal flakes with random orientation (m ¼ 2/27 [22])
Random array ribbons with infinite length b
Gusev and Lusti [17]
Disk
Randomly dispersed disks without overlapping
Fredrickson and Bicerano [18]
Diskb
Monodisperse oriented disks placed in a nematic structure
Bharadwajd [19]
2 1þ exp
Aligned ribbons with infinite length (O ¼ 1)
0:71
af 3:47
4
Ribbona
af 3
2 1þxþ0:125x2 2þx
a x ¼ paf 2 ln 2 2 1 þ af O þ 12 3 2
Randomly oriented ribbons with infinite length (O ¼ 0)
a b c d
For ribbons, length is infinite, width, w; thickness, t; aspect ratio, a ¼ w/t. For disks, circular shape of diameter d and thickness t; aspect ratio, a ¼ d/t. m is a geometric factor, which depends on the shape of the dispersed particles. O is an orientation function, which depends on the angle between the normal unit vector of silicate layers and the direction of the preferred orientation.
grade) was purchased from NatureWorks (USA). It has a density of 1.24 g/cm3 and weight average molecular weight of 100000 g/mol. The Elvaloy PTW, a terpolymer of ethylene, butylacrylate (BA) and glycidyl methacrylate (GMA), supplied by DuPont (USA), was used as a reactive compatibilizer. It has a melt flow index of 12 g/10min (190 C/2.16 kg) and density of 0.94 g/cm3. Two types of nanoclay, supplied by Southern Clay Products Inc., USA, were Cloisite 30B, modified with bis-(2-hydroxyethyl) methyl tallow alkyl ammonium cations and Cloisite 15A, modified with dimethyl, dehydrogenated tallow quaternary ammonium cations. Prior to melt
blending, all of the materials were dried for 4 h at 80 C in a vacuum oven. 2.2. Sample preparation PP/PLA/clay nanocomposites were prepared via melt blending in a co-rotating twin-screw extruder (Leistritz, Micro 27, Germany), with screw diameter of 27 mm, L ¼ 36D, screw speed of 150 min1 and throughput of 10 kg/h. The temperature profile was 180 Ce210 C from hopper to die. The blending was carried out
Table 2 Composition of the samples: uncompatibilized blends, PP-rich system, PLA-rich system. Uncompatibilized blends
PP-rich system
PLA-rich system
Pure PP PP/PLA 90/10 PP/PLA 75/25 PP/PLA 50/50 PP/PLA 25/75 PP/PLA 10/90 Pure PLA
PP/PLA 75/25 PP/PLA/PTW 75/25/5 PP/PLA/Cloisite 15A 75/25/1 PP/PLA/Cloisite 15A 75/25/3 PP/PLA/Cloisite 15A 75/25/5 PP/PLA/Cloisite 15A 75/25/7 PP/PLA/PTW/Cloisite 15A 75/25/5/5
PP/PLA 25/75 PP/PLA/PTW 25/75/5 PP/PLA/Cloisite 30B 25/75/1 PP/PLA/Cloisite 30B 25/75/3 PP/PLA/Cloisite 30B 25/75/5 PP/PLA/Cloisite 30B 25/75/7 PP/PLA/PTW/Cloisite 30B 25/75/5/5
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according to the compositions listed in Table 2. As shown, a complete range of composition of unfilled PP/PLA blends was prepared for considering the effect of composition on the permeability of the blends. Moreover, two systems were selected in order to consider the effect of incorporation of clay and compatibilization on permeability, i.e. PP-rich (75/25) and PLA-rich (25/75). For each type of the blends, as well as for the neat components, the same processing procedure was applied so that the thermo-mechanical history of the blends and that of neat polymers remains similar. The extruded samples were pelletized at the die exit, dried and compression molded to film using a press (20 MT Mini Lab, Labtech, Thailand). For each sample, an equal weight of 3.5 g was pre-heated at 260 C for 30 s and pressed under 15 MPa pressure for 10 s in a thin film mold, followed by quenching at room temperature, to obtain thin film for the permeability measurement. The thickness of the films measured by a Millitron micrometer was 100e150 mm for all of the samples. 2.3. Characterization 2.3.1. Transmission electron microscopy The dispersion quality of the nanoparticles within the matrix and the nanostructures of the nanocomposites were investigated using a transmission electron microscope (TEM), Philips EM208 (Netherlands), operated at 100 kV. The samples were microtomed at 160 C. 2.3.2. Scanning electron microscopy Scanning electron microscopy (SEM) was used to characterize the morphology of the blends. Each sample, after proper drying, was cryofractured in liquid nitrogen at 160 C, then sputter coated with 3 nm platinum prior to examination under a NEON 40 EsB (Carl Zeiss, Oberkochen, Germany). SEM images were analyzed using image processing software (JMicroVision v1.27). The volume (Rv) and number (Rn) average radii and polydispersity of the particles (PD), were calculated using Equations (11)e(13) [27e30]. At least 200 particles were considered to measure the parameters.
P nR Rn ¼ P i i ni P RV ¼ P
PD ¼
ni R4i ni R3i
RV Rn
(11)
(12)
(13)
where ni is the number of the droplets having radius Ri. 2.3.3. Differential Scanning Calorimetry (DSC) A Mettler DSC (STARe SW 10.00) was used to study the degree of crystallinity (Xc) of the specimens. About 5 mg of each sample was scanned in a cycle of heatingecoolingeheating from 20 to 200 C at 10 K/min. In order to determine the crystallization enthalpy (DHc), cold crystallization enthalpy (DHcc) and melting enthalpy (DHm), the cooling and second heating scans were used. The Xc of samples was calculated using the following equation [29,30]:
Xc ¼
DH 100 0 $½1 ð%wt:filler=100Þ DHm
(14)
where DH ¼ DHc for cooling curves, i.e. the crystallization enthalpy of the sample, or DH ¼ DHmDHcc for second heating curves, DHm
is the melting enthalpy of the sample, DHcc is the cold crystallization enthalpy of the sample, DH0m is the melting enthalpy of the 100% crystalline polymer matrix (201.1 J/g for PP [31] and 93.0 J/g for PLA [32]) and %wt of filler is the total weight percentage of nanoclay and blend component [32,33]. 2.3.4. Oxygen permeability The permeability to oxygen was measured using a gas permeability tester (Coesfeld, GDP-C, Germany). This compact gas permeability tester determines the permeability of dry gases for materials using the manometric method. The permeation at the bottom chamber of the test specimen is determined by the evaluation of the increase in pressure in the previously evacuated volume versus cm3/m2.24 h bar. The increase in pressure during the test period is evaluated and displayed by an external computer. The average values of three permeability measurements were reported. 2.3.5. Molecular dynamic simulation methodology The diffusivity of oxygen in the pure polymers, PP-rich and PLArich blends were calculated by constructing an amorphous cell containing oxygen and polymer/blend. After constructing the cell, a molecular dynamics simulation performed to calculate the mean square displacement of the oxygen molecule. The modeling was according to Charati and Stern [34] which examined the diffusion of gases in silicone polymers. Materials Studio® 6.0 (Accelrys Software Inc., San Diego, CA) was used for all simulations. In each modeling the following steps were performed: 1) Setting up the initial structures 2) building an amorphous cell 3) relaxation of the cell 4) running and analyzing molecular dynamics 5) exporting data and calculating the diffusivity. The first step is to specify the constituent molecules of the cell. The oxygen molecules and investigating structures must be added to the built amorphous cell. In order to start the simulation, the chains of the polymers were constructed. Then, a cell containing four molecules of oxygen and eight chains of the polymer components or blends was built. The compositions of the selected blends, PP-rich (75/25) and PLA-rich (25/75), were set at the amorphous cell in this step. The target density of the final configurations was determined as 0.95. When an amorphous cell is generated, the molecules may not be equally distributed throughout the cell, creating areas of vacuum. To correct this, a short energy minimization was performed to optimize the cell. After the minimization, a short molecular dynamics simulation should be run to equilibrate the cell. This procedure of minimization and molecular dynamics is known as relaxing the structure and should be carried out whenever an amorphous cell is constructed [35]. For the purpose of this modeling, the constant-temperature, constant-volume (NVT) ensemble was used, as it is a faster ensemble [34e37]. Systematic temperature was set to 300 K in MD runs and a constant temperature control method was used. For the NVT ensemble, the system was equilibrated when the energy in the live update chart document became constant. The velocity-scale thermostat was used during equilibration. Iteration step was chosen as 5000, sampling time was set to 250 ps, and total 2ns trajectories were collected for the analysis. For all systems, COMPASS force fields were applied. The MSD typically has two regions. At short times, the gas molecule collides inside a small pocket of free volume. Since the confined molecules cannot diffuse on this time scale, the MSD levels off to a constant. On a longer time scale, the molecule jumps out of the confined area to another pocket of free volume. The resulting motion of repeated jumps is diffusion, characterized by a MSD that is linear in time (Equation (10)). The MSD in Forcite is averaged over all atoms in the molecules. To determine the diffusion coefficient it is necessary to fit a straight line, y ¼ a.x þ b, to the data in the diffusive regime and abstract the slope a. The unit of a is then Å2/ns.
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According to the above definition and Equation (10), D then follows as [35]:
D ¼ a=6
(15)
3. Results and discussion 3.1. Blend morphology and permeability relationship Permeation has a strong relationship with the morphology of the system. The SEM micrographs of the blends are shown in Fig. 1 and the graphs of permeability values of the pure components and the blends in correlation to their morphologies in Fig. 2. Comparing the micrographs of Fig. 1a to b and 1c to d shows that the discrete PLA spherical domains are almost uniformly dispersed in the matrices, but their sizes in the PLA-rich blends are greater than those in PP-rich blends. This is attributed to the greater viscosity of PP as compared to that of PLA, leading to a greater decrease in the size of the droplets of PLA in the PP-rich blends. This is due to the fact that a ratio of the viscosities of greater than 1 (hPP/hPLA > 1)
143
leads to a fined droplet morphology [27,38]. Homogenously distributed barrier layers or droplets can increase the tortuosity of the path and improve barrier properties of the blends. As shown, the PP-rich blends have greater oxygen barrier compared to the PLA-rich ones. While blending of PP with 10 wt.% of PLA led to a decrease of 8% in permeability, the 75/25 blend had a decrease of more than 600%. This composition seems to be the optimum composition for achieving greater barrier properties. The lower permeability value for PP-rich blend is attributed to the optimum size of domains of the oxygen barrier component (PLA) in the blend leading to an increase in barrier properties due to an increase in tortuosity of permeant path. Therefore, a greater area of the barrier droplets can improve barrier properties of the final blend. Table 3 shows the volume (Rv) and number (Rn) average radii and polydispersity (PD) values of the particles. Regarding the Rn and permeability values of the PP-rich blends, the Rn value of 75/25 blend is greater than 90/10 blend, leading to lower permeation. An interesting point is that exceeding 25 wt % of PLA in the blends led to a dramastic increase in permeability, even greater than that of pure PP film. This is attributed to the state of the blend components, their polarity, crystallinity behavior etc.
Fig. 1. Scanning electron micrographs of PP/PLA blends: a) 90/10 b) 10/90 c) 75/25 d) 25/75 e) and f) 50/50.
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Fig. 2. Permeability values and morphology of the PP/PLA blends.
As mentioned before, the permeability coefficient, P, is the product of solubility, S, and diffusivity, D, coefficients. Both of the coefficients are related to the polarity of permeating molecules as well as the polymer. Because the polarity of PLA is greater than PP, the dissolving of oxygen, as a non-polar gas, into PLA is more difficult than PP. Therefore, oxygen permeability of PLA is lower than PP. Moreover, as a polymer is cooled through the rubbereglass transition from the melt, molecular mobility is rapidly reduced. The change in segmental mobility of the polymer chains at Tg affects the diffusion phenomenon [34e36]. Regarding intrinsically structural differences of PP and PLA, the glass transition temperature of PLA is greater than PP. Hence, it can be said that, while PP is in rubbery plateau at ambient condition, PLA is in glassy state. Therefore, PLA has a lower segmental mobility comparing to that for PP, leading to a difficult center-of mass mean-square displacement and, consequently, lower diffusion. Hence, PLA droplets have functioned as oxygen barriers in PP matrix of PP-rich blends at the experimental conditions. Differently, PP has been at the rubbery plateau at the test conditions. Meares [35] plotted the diffusion coefficients for argon and oxygen in poly(vinyl acetate) (PVAc) in a temperature range encompassing the Tg to be composed of three distinct regions of differing slopes, indicating different diffusion coefficients. For polymers, the total amount and the distribution of free volume cavities can directly influence how fast small molecules can diffuse [40]. Another affecting factor is the crazes in the texture of the main
Table 3 The quantitative values obtained from SEM micrographs of the blends and blend nanocomposites. Components
Composition
Rn [mm]
Rv [mm]
PD
PP/PLA PP/PLA PP/PLA PP/PLA PP/PLA PP/PLA/PTW PP/PLA/Cloisite 15A PP/PLA/PTW/Cloisite 15A PP/PLA/PTW PP/PLA/Cloisite 30B PP/PLA/PTW/Cloisite 30B
90/10 75/25 50/50 25/75 10/90 75/25/5 75/25/5 75/25/5/5 25/75/5 25/75/5 25/75/5/5
0.34 0.54 0.82 0.64 0.62 0.28 0.40 0.23 0.54 0.57 0.31
0.56 0.81 1.22 0.99 0.96 0.69 0.84 0.43 0.79 1.07 0.61
1.65 1.52 1.49 1.55 1.56 2.45 2.08 1.31 1.46 1.88 1.94
phase near the interphase between two phases of PLA-rich blends, as shown in the circles. These crazes arose because of incompatibility of the blend components. Considering the micrographs of 10/90 and 25/75 blends (Fig. 1b and d respectively), and 50/50 blend (Fig. 1e and f) shows that these crazes have increased with increase in PLA content. As shown, the 10/90 blend has the most crazes. These results are in good accordance to our previous published work on the homogeneity of the two PP-rich and PLArich systems using rheological studies. This study showed better compatibility for 75/25 composition compared to 25/75 blend. Structural features which slow the polymer gas transmission rates include polymer chain mobility, chain packing, degree of crystallization, etc. Crystallization behavior of the film samples is another significant factor, determining the diffusion of gases through a membrane [39]. The degree of crystallinity is of great importance because the mass transport phenomena take place more easily in amorphous regions compared to crystal lattices. The DSC thermographs of the second heating and cooling of pure components and the blends are shown in Fig. 3a and b, respectively, and the degrees of crystallinity using Equation (14) are reported in Table 4. As shown, the crystallinity of PP-rich blend is more than that of PLA-rich one, having good accordance with permeability values. Moreover, generation of defects can affect the diffusion [39,41]. As shown in the thermograph of PLA-rich sample (Fig. 3b), the small area of the PP crystallization peak compared to that of PP-rich one implies crystal defects [29,30]. The mechanism of diffusion in the defects is analogous to gas diffusing in very small pores, so that diffusion through them is much easier than through amorphous regions in the polymer [39,41]. That is to say, the presence of microvoids in the matrix facilitate the formation of microchannels, leading to a completely different set of transport mechanisms (Kudsen flow) [39]. As mentioned, the small melting peak corresponding to PP in PLA-rich blend, implying a defective crystal structure of PP chains could act similar to pores or microvoids, and could affect permeability of the corresponding sample significantly. Table 5 shows the theoretical values of proposed models for the blends. The experimental values are shown again for comparing to theoretical values. As shown, these models failed to predict the permeability values well, especially for PLA-rich blends. This is attributed to incompatibility in these compositions, leading to
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145
Fig. 3. DSC thermographs of the second heating from 20 to 200 C at 10 K/min for the components and the blends a) second heating b) cooling.
Table 4 Degree of crystallization for pure components, PP-rich and PLA-rich systems. Components
Composition
Xc [%]
Pure PP PP/PLA PP/PLA/PTW PP/PLA/Cloisite 15A PP/PLA/PTW/Cloisite 15A PP/PLA PP/PLA/PTW PP/PLA/Cloisite 30B PP/PLA/PTW/Cloisite 30B Pure PLA
100/0 75/25 75/25/5 75/25/5 75/25/5/5 25/75 25/75/5 25/75/5 25/75/5/5 0/100
50.7 71.6 51.2 41.0 37.6 34.4 32.1 48.3 22.8 12.0
appearance of microvoids at the interface, as explained. Another reason is the difference in crystallinity behavior between two systems. 3.2. Correlation of nanocomposites' microstructures to permeability values Fig. 4 shows the effect of incorporation of clay nanoparticles and compatibilization by PTW on the morphology of the systems. As shown, the both factors led to decrease the crazes, and these effects are reflected in the permeability data of Figs. 2, 5 and 6, so there was a significant decrease in the permeability of comatibilized and Cloisite 30B filled PLA-rich system. As seen in Fig. 3, the increase in permeation with compatibilization is due to decrease in size of disperse phase and higher value of polydispersity of the droplet size. This corresponds to the effect of PTW compatibilizer on interfacial interaction between the phases, leading to reduced droplet size and greater polydispersity. Interestingly, with the introduction of two clays into the blends, the size of the dispersed domains decreased significantly (Figs. 2c, d and 4b). Simultaneous incorporation of PTW compatibilizer and clay could lead to more Table 5 Experimental and theoretically calculated permeability values of pure components and the blends vs. cm3/m2.day.bar. Sample
Experimental
PP PP/PLA PP/PLA PP/PLA PP/PLA PP/PLA PLA
349 320 58 420 6910 8260 24
90/10 75/25 50/50 25/75 10/90
± ± ± ± ± ± ±
2 3 1 15 86 105 2
Parallel
Series
Paul
349 324 286 212 126 67 24
349 173 95 52 33 27 24
349 285 207 113 55 34 24
decrease in the size of the dispersed-phase domains of the both systems (Table 3, Fig. 2e and f). This effect can increase oxygen permeability in PP-rich blends due to decrease of the size of barrier droplets of PLA, as seen in permeability values of compatibilized PP-rich and PLA-rich in graph of Fig. 2. However, the presence of impermeable clay layers could compensate for this effect significantly. The graphs of Figs. 5 and 6 show a decrease in permeability due to dispersion of barrier layers, i.e. Cloisite 15A and 30B, into the PPrich and PLA-rich systems, respectively. The TEM micrographs of two systems are shown in Fig. 7. Nanocomposites are multiphase systems in which the coexistence of phases with different sorption and diffusion can cause complex transport phenomena [41,42]. Montmorillonite was reported to increase superficial adsorption and specific interactions with some gases and solvents [42]. Moreover, the presence of silicate layers are expected to cause a decrease in permeability of oxygen because of more tortuous paths for the diffusing molecules that must bypass impenetrable platelets (Figs. 7 and 8). This phenomenon is significant when the filler is of nanometer size with high aspect ratio. Fig. 8 shows effect of intercalation and exfoliation on the path tortuosity of the two systems with the help of schematic representation. As shown, not only the content of clay, but also the state of dispersion of the inorganic platelets and the extent of exfoliation in the polymer phase is important for improving the barrier properties of the blends due to increase in tortuosity factor. As expected, there is a decrease in permeability values with increase in nanoclay content up to 5 wt. % in both systems. Exceeding 5 wt.% nanoclay had no significant effect on permeation due to the aggregation phenomenon at high concentrations that decreases the tortuosity factor, leading to increase in the permeation [19,42]. In order to use the permeation models of nanocomposites, the permeability value of the pure blend was assumed as the permeability of the matrix (P0). TEM micrographs with the help of the image analyzer software were used to estimate the average aspect ratio, i.e. a, which is the ratio of the length (l) to the thickness (t) of clay particles as shown in Fig. 9. The predicted permeability values obtained by various models are summarized in Table 6. As shown, the permeability decreases as the volume fraction of clay nanoparticles increases. However, the effect of exfoliation is more pronounced in all of the models due to an intensive increase in aspect ratio of the layers (a), leading to a significant decrease in permeability values of compatibilized nanocomposites. As shown, the permeability decreases as the geometric factor m of the Cussler model [16] increases. Because the predictions of Bharadwaj model [19] with O ¼ 0 (randomly oriented ribbons with infinite length) and Cussler model with [21] m ¼ 1/2
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Fig. 4. Scanning electron micrographs of compatibilized blends and blend nanocomposites: a) PP/PLA/PTW 75/25/5 b) PP/PLA/PTW 25/75/5 c) PP/PLA/Cloisite 15A 75/25/5 d) PP/ PLA/Cloisite 30B 25/75/5 e) PP/PLA/Cloisite 15A/PTW 75/25/5/5 f) PP/PLA/Cloisite 30B/PTW 25/75/5/5.
Fig. 5. Permeability of PP-rich samples encoded: 1) PP/PLA 75/25 2) PP/PLA/PTW 75/ 25/5 3) PP/PLA/Cloisite 15A 75/25/1 4) PP/PLA/Cloisite 15A 75/25/3 5) PP/PLA/Cloisite 15A 75/25/5 6) PP/PLA/Cloisite 15A 75/25/7 7) PP/PLA/Cloisite 15A/PTW 75/25/5/5.
Fig. 6. Permeability of PLA-rich samples encoded: 1) PP/PLA 25/75 2) PP/PLA/PTW 25/ 75/5 3) PP/PLA/Cloisite 30B 25/75/1 4) PP/PLA/Cloisite 30B 25/75/3 5) PP/PLA/Cloisite 30B 25/75/5 6) PP/PLA/Cloisite 30B 25/75/7 7) PP/PLA/Cloisite 30B/PTW 25/75/5/5.
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147
Fig. 7. TEM micrographs of: a) and b) PP/PLA/Cloisite 15A 75/25/3 c) and d) PP/PLA/Cloisite 15A/PTW 75/25/5/5 e) and f) PP/PLA/Cloisite 30B 25/75/3 g) and h) PP/PLA/Cloisite 30B/ PTW 25/75/5/5.
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Fig. 8. The effect of intercalation and exfoliation on the path tortuosity of the two systems with the help of schematic representation: a) PP/PLA/Cloisite 15A 75/25/3 b) PP/PLA/ Cloisite 30B 25/75/3 c) PP/PLA/Cloisite 15A/PTW 75/25/5/5 d) PP/PLA/Cloisite 30B/PTW 25/75/5/5 (The red and black chains show the blend components). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
(ribbons with infinite length and equal probability of alignment and misalignment) are close to our experimental values of PP-rich system, it is concluded that intercalated and exfoliated layers have an equal probability of alignment and misalignment in this system. In the case of PLA-rich system, all of the models failed to predict correctly, as shown in Table 6. 3.3. Molecular dynamic results
Fig. 9. Measuring the aspect ratio of an exfoliated (left) and intercalated (right) clay layers by image analysis. The layers are assumed to be laid in main direction (MD).
Images of different steps in MD for PP and PLA are shown as examples in Fig. 10. From the plot of MSD as a function of time, diffusion coefficient can be derived from the slope of a linear fitting to data points according to Equation (10). Fig. 11 shows the graphs of MSD versus time. Then, the value of linear coefficient a (slope of the fit line) must be divided by 6 (Equation (15)) to obtain the diffusion coefficient in Å2/ps, and the resulting value must be converted to the more commonly used unit cm2/s. Table 7 shows the calculated values of D. Comparing the calculated D values of PP and PLA to those reported in the literature shows that they are very different to the experimental values; since the polymers were quite short, the cell size rather small and the run length was very short and did not allow time for proper Einstein diffusion to occur. The most effective factor determining the accuracy of D values is the
Table 6 Predicted permeability values of PP-rich and PLA-rich systems vs. cm3/m2.day.bar using the models. Sample
Experimental a value fClay
PP/PLA/Cloisite 15A 75/25/1 PP/PLA/Cloisite 15A 75/25/3 PP/PLA/Cloisite 15A 75/25/5 PP/PLA/Cloisite 15A 75/25/7 PP/PLA/Cloisite15A/PTW 75/25/5/5 PP/PLA/Cloisite 30B 25/75/1 PP/PLA/Cloisite 30B 25/75/3 PP/PLA/Cloisite 30B 25/75/5 PP/PLA/Cloisite 30B 25/75/7 PP/PLA/Cloisite30B/PTW 25/75/5/5
54 46 35 35 3.5 3100 2830 2640 2650 2100
± ± ± ± ± ± ± ± ± ±
2 4 2 4 1 45 43 25 68 22
80 80 80 80 200 50 50 50 50 150
0.006 0.018 0.029 0.041 0.028 0.007 0.021 0.034 0.047 0.319
Nielsen Cussler Cussler Fredrickson (random array) and Bicerano m ¼ 1 m ¼ 1/2 m ¼ 2/27
Gusev Bharadwaj and Lusti O¼1 O¼0
45.0 31.0 23.7 19.0 1.2 5835.8 4451.5 3597.7 3019.0 188.3
43.8 31.4 24.1 19.1 0.02 5635.8 4423.3 3644.1 3076.2 7.4
55.7 43.4 30.6 21.4 0.09 6768.5 6040.4 5037.1 4064.9 18.3
53.3 33.3 19.1 11.6 0.03 6654.4 5302.1 3798.5 2672.7 5.6
55.4 42.0 28.4 19.2 0.05 6756.3 5945.4 4841.4 3802.0 11.1
57.3 54.0 49.0 43.3 0.3 6845.7 6630.6 6319.4 5939.8 74.0
12.8 3.9 1.9 1.2 0.003 2433.1 944.8 524.7 338.2 0.7
44.9 31.0 23.7 19.1 1.2 5835.8 4451.5 3597.7 3019.0 188.3
52.7 44.5 38.5 33.9 3.4 6481.7 5766.7 5193.5 4724.3 523.1
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149
Fig. 10. The images of different steps in MD: a) and b) first configurations of PP and PLA chains built of 20 repeating units, respectively, c) and d) the amorphous cells for calculation of diffusion coefficient of oxygen through PP, e) and f) the amorphous cells for calculation of diffusion coefficient of oxygen through PLA, g) and h) the final configurations of PP and PLA after 2 ns trajectories, respectively.
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Fig. 11. The graphs of MSD vs. time after MD, representing the fitting line relation: a) PP b) PLA c) PP-rich and d) PLA-rich systems e) comparing all of the samples.
time of the run. As mentioned, this time (2 ns) was very short in this work because of complexity of the constructed amorphous cell for the blends. The run times of 10 ns for pure polymers were reported Table 7 Diffusion coefficient of simulated samples. Sample
MD simulation values of D (cm2/s)
PP PP/PLA 75/25 PP/PLA 25/75 PLA
3.4 3.0 3.7 3.3
105 105 105 105
by other researchers [e.g. 36, 42]. Comparing the MSD plots versus time for all samples (Fig. 11e), and the data shown in Table 7, confirms the highest permeability value of the PLA-rich blend due to the largest value of D. In other words, the results of MD simulation also confirm that the PP-rich blends have greater oxygen barrier properties compared to those for the PLA-rich blends due to lower D value. Therefore, the simulated results are in good agreement with the available experimental data. As reported the calculated diffusion coefficients using MD simulation for several polymers have not been in particularly good agreement with experiment [36,39e42].
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4. Conclusions Investigations on oxygen gas permeability in PP/PLA/clay nanocomposite films revealed that the permeation is highly dependent on blend composition, clay loading and state of clay dispersion governed by compatibilization. Compared to PLA-rich system, the PP-rich films showed greater barrier to oxygen. Morphological studies revealed that the lower permeability of PPrich films is mainly due to reduced size of dispersed domains of the oxygen barrier component, i.e. PLA in the PP matrix, leading to more tortuous permeant path. Moreover, the higher degree of crystallinity observed in PP-rich films as compared to the PLA-rich system was found to be also responsible for the higher barrier properties of the PP-rich films. From TEM studies, it was concluded that the presence of silicate layers in the blends decreases the oxygen permeability due to the more tortuous paths for diffusion of oxygen molecules that must bypass the impenetrable platelets. Incapability of several existing models for PLA-rich blends was attributed to compatibility issues, presence of microvoids at the interphase region, degree of crystallization and crystalline defects. The deviation of the proposed models was pronounced in presence of compatibilizer due to better dispersion of clay and an intensive increase in aspect ratio of the layers as a result of exfoliation, leading to a significant decrease in permeability values of compatibilized nanocomposites. Molecular dynamics simulations showed good correlation to the permeability values for different compositions. The MD simulation is a strong tool for investigation and prediction of gas permeability through polymers and blends and nanocomposites. Acknowledgments The work described in this paper was supported by a grant from the Iran Polymer and Petrochemical Institute (IPPI) (84150). The authors gratefully acknowledge the Research Vice Chancellor of IPPI and his co-workers due to their helps and assistances. References [1] D.R. Paul, C.B. Bucknall, Polymer Blends, John Wiley & Sons Inc., New York, 2000. [2] L.A. Utracki, Polymer Blends Handbook, Kluwer Academic Publishers, Netherlands, 1999. [3] R.T. Parry, Principle and Applications of Modified Atmosphere Packaging of Food, Chapman & Hall, New York, 1993. [4] M. Pluta, E. Piorkowska, Tough and transparent blends of polylactide with block copolymers of ethylene glycol and propylene glycol, Polym. Test. 41 (2015) 209e218. [5] V. Goodarzi, S.H. Jafari, H.A. Khonakdar, B. Ghalei, M. Mortazavi, Assessment of role of morphology in gas permselectivity of membranes based on polypropylene/ethylene vinyl acetate/clay nanocomposite, J. Membr. Sci. 445 (2013) 76e87. [6] R.J. Shields, D. Bhattacharyya, S. Fakirov, Oxygen permeability analysis of microfibril reinforced composites from PE/PET blends, Compos. A 39 (2008) 940e949. [7] T. Ogasawara, Y. Ishida, T. Ishikawa, T. Aoki, T. Ogura, Helium gas permeability of montmorillonite/epoxy nanocomposites, Compos. A 37 (2006) 2236e2240. [8] Y.C. Ke, P. Stroeve, Polymer-layered Silicate and Silica Nanocomposites, first ed., Elsevier Science, 2005. [9] R. Hiroi, S.S. Ray, M. Okamoto, T. Shiroi, Organically modified layered titanate: a new nanofiller to improve the performance of biodegradable polylactide, Macromol. Rapid Commun. 25 (2004) 1359e1364. [10] Y. Elsayed, C. Lekakou, P. Tomlins, Monitoring and modelling of oxygen transport through un-crosslinked and crosslinked gelatine gels, Polym. Test. 40 (2014) 106e115.
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Contents lists available at ScienceDirect
Polymer Testing journal homepage: www.elsevier.com/locate/polytest
Property modelling
On O2 gas permeability of PP/PLA/clay nanocomposites: A molecular dynamic simulation approach Hassan Ebadi-Dehaghani a, Mehdi Barikani a, *, Hossein Ali Khonakdar a, b, Seyed Hassan Jafari c, Udo Wagenknecht c, Gert Heinrich c a b c
Iran Polymer and Petrochemical Institute, P.O. Box 14965/115, Tehran, Iran Leibniz Institute of Polymer Research Dresden, D-01067, Dresden, Germany School of Chemical Engineering, College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Iran
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 April 2015 Accepted 22 May 2015 Available online 29 May 2015
Polypropylene/poly(lactic acid) (PP/PLA) blends were prepared by reactive blending in a co-rotating twin-screw extruder. Effect of blend composition on oxygen permeability through compression molded films was investigated and correlated to the microstructure. Two blends, PP-rich (75/25) and PLArich (25/75), were selected in order to consider the effect of incorporation of clay and compatibilization on the permeability. The PP-rich blends had greater oxygen barrier properties compared to those of PLA-rich blends. Several proposed models of permeability for blends and nanocomposites were examined. These models failed to predict the permeability values, especially for PLA-rich blends. The failure was attributed to the immiscibility issue leading to appearance of microvoids at the interface. Molecular dynamics simulations were performed by employing the COMPASS force field to estimate the diffusivity of oxygen gas through pure components, PP-rich and PLA-rich systems. The simulated results were in good agreement with the available experimental data. © 2015 Elsevier Ltd. All rights reserved.
Keywords: PP/PLA blends Oxygen permeability Molecular dynamic simulation
1. Introduction Controlling permeation of gases and liquids through plastic films, membranes and other articles is of great importance in several packaging and industrial applications [1e7]. The general approach in preparing high performance barriers is to use materials having low diffusivity and low solubility of the permeating material. Preparation of barrier films in the packaging industries is carried out via multi-layer co-extrusion, extrusion coating and adhesive lamination processes [1]. These are complex and expensive technologies and the final products are not recyclable. There are trends to replace them with simple processes. The blends of commodity and barrier polymers e.g. ethylene vinyl alcohol (EVOH), polyvinylidene chloride (PVdC), poly(ethylene terephthalate) (PET) and liquid crystals [1e6] are the alternatives. Addition of a small quantity of barrier material into a low-cost matrix can lead to a low-cost product with greatly improved barrier properties [7].
* Corresponding author. E-mail addresses: [email protected] (M. Barikani), [email protected] (H.A. Khonakdar). http://dx.doi.org/10.1016/j.polymertesting.2015.05.010 0142-9418/© 2015 Elsevier Ltd. All rights reserved.
Polypropylene (PP) is a commodity polymer with suitable mechanical properties and good barrier to H2O, but its poor barrier to O2 limits its applications [3]. Conversely, poly(lactic acid) (PLA) is well-known biodegradable polyester with significantly lower O2 permeability and poor barrier to H2O as compared to PP. Meanwhile, incorporation of layered nanoparticles, such as layered silica, organoclay and layered titanate, is reported to decrease the O2 and H2O vapor permeability [8e12]. Thermodynamics play a strong role in intercalation/exfoliation processes, hence it can provide strong interactions at interface region having a fundamental influence on the mass transport and polymer diffusion. In this regard, blending of PP and PLA as a green polymer can be a good selection for solving the permeability problems of the two polymers, and incorporation of clay can improve these properties. Therefore, this hybrid is a biodegradable material with enhanced mechanical and barrier properties. Prediction of permeability of blends, nanocomposites and hybrids is an important issue. There are several proposed models for prediction of permeability that will be discussed in the next part [13e22]. Moreover, molecular dynamics (MD) simulation is another alternative. MD simulation is a useful method for prediction of the diffusion of gases through polymer films and membranes. The diffusion of small molecules
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such as He, O2 CO2, CH4 through glassy polymers, partially crystalline polymers and polymer membranes is of great practical interest in relation to a number of current and possible future practical applications. This area offers many opportunities for the use of computer modeling for optimizing the molecular design of host polymers [23e26]. In our previous works, we reported on the effect of composition and compatibilization on the morphology and rheological properties of PP/PLA blends [27]. Moreover, we considered these effects and also incorporation of clay nanoparticles on mechanical [28] and crystallization behavior [29] of the hybrid system. The aim of the present work is to investigate oxygen gas permeability in PP/PLA/ clay nanocomposite films and its dependency on blend composition, clay loading, state of clay dispersion governed by compatibilization, and crystallization behavior induced by changing the composition and clay incorporation. Moreover, molecular dynamics simulation of O2 permeability is performed for the pure polymers and the blends.
penetration of gas molecules. This concept can be defined as the tortuosity factor (t):
t¼
D0 D
(6)
K¼
P 1f ¼ P0 t
(7)
where 0 subscript indicates the neat polymer. Several attempts, including analytical and numerical modeling, have been made to predict the tortuosity parameter and permeability of filled systems. The main models are summarized in Table 1 [13e22]. 1.2. Molecular dynamics simulations MD simulations solve Newton's equations of motion for a system of N interacting atoms [24]:
v2 ri ¼ Fi ; i ¼ 1…N: vt 2
1.1. Permeation models for blends and nanocomposites
mi
The presence of an impermeable dispersed phase increases the tortuosity of the path that a molecule must traverse in permeating through a film. It is useful to discuss this concept in terms of a tortuosity factor t, which is the effective path length divided by the actual thickness of the film. It is dependent on the volume fraction of impermeable dispersed phase (Equation (1)). This factor can be used to calculate the permeability of the composite, as presented in Equation (2) [1].
The forces are the negative derivatives of a potential function V (r1, r2, …, rN):
t¼1þ
fd 2
Pc f ¼ m Pm t
(1)
(2)
where Pc is the permeability of the composite, Pm is the permeability of the matrix and fmis the volume fraction of matrix. Robeson [1] extended Maxwell's work by applying it to blends for which both components are permeable. Pc derived by Robeson is given in the following equations, where the polymeric components are denoted by the subscripts 1 and 2, and fi stands for the volume fraction of phase i. Lower bound so-called series model (layers normal to permeant flow):
Pc ¼
P1 P2 ðf1 P2 þ f2 P1 Þ
(3)
Upper bound so-called parallel model (layers parallel to permeant flow):
Pc ¼ f1 P1 þ f2 P2
(4)
Paul determined semi-logarithmic mixture law for blends as follows [1]:
ln P ¼ f1 ln P1 þ f2 ln P2
(5)
Here, fi is the volume fraction of the ith component. Assuming PLA as an oxygen barrier component compared to PP, the mentioned models can be used for unfilled blends. In the case of the nanocomposites, the impermeable silicate layers also create a tortuous path in the polymer matrix and, therefore, gas molecules penetrate with more difficulty. Therefore, it is useful to discuss proposed models for permeation in composites. Because of function of layered silicates as the impermeable barrier, a tortuous path in the polymer matrix can be created, leading to a difficult
Fi ¼
(8)
vV vri
(9)
The equations are solved simultaneously in small time steps. The system is followed for some time, taking care that the temperature and pressure remain at the required values, and the coordinates are written to an output file at regular intervals. The coordinates as a function of time represent a trajectory of the system. After initial changes, the system usually reaches an equilibrium state. By averaging over an equilibrium trajectory, many macroscopic properties, such as diffusion processes in membranes can be extracted from the output file [24]. The diffusivity of a gas in an organic solvent, polymer, or zeolite can be calculated by running an MD simulation and determining the mean square displacement (MSD) of the gas in the material. For long periods of times, during which the penetrant molecules have random walks in the polymer matrix, the mean-square-displacement becomes linear in time, and the diffusion coefficients can be calculated using the Einstein relation [24]:
1 d lim D¼ 6N t/∞ dt
*
N X
+ ½ri ðtÞ ri ð0Þ
2
(10)
i
The motion pattern of penetrant gases in host polymer can be qualitatively studied by monitoring the penetrant's displacement jr(t)r(0)j from its initial position. The diffusion coefficient D is then obtained from the slope of a plot of the mean squared displacement against time t.
*
N X
+ ½ri ðtÞ ri ð0Þ2
i
2. Experimental 2.1. Materials PP (PP Moplen HP500N) with melt flow index of 2.1 g/10min (230 C/2.1 kg), density of 0.9 g/cm3 and number average molecular weight of 80000 g/mol was supplied by Basell Co. PLA (4043D
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Table 1 Detailed schematics and predicted relative permeability values for each model discussed in the text. Model
Filler type
Particle geometry and geometrical assumption
Formulas of tortuosity parameter (t)
Nielsen [15]
Ribbona
Aligned ribbons with infinite length
1 þ af 2
Cusslerc [16]
Ribbona
Aligned ribbons with infinite length (m ¼ 1 [20])
"
# ðafÞ2 1f
1 þ m4
Ribbons with infinite length and equal probability of alignment and misalignment (m ¼ 1/2 [21])
Hexagonal flakes with random orientation (m ¼ 2/27 [22])
Random array ribbons with infinite length b
Gusev and Lusti [17]
Disk
Randomly dispersed disks without overlapping
Fredrickson and Bicerano [18]
Diskb
Monodisperse oriented disks placed in a nematic structure
Bharadwajd [19]
2 1þ exp
Aligned ribbons with infinite length (O ¼ 1)
0:71
af 3:47
4
Ribbona
af 3
2 1þxþ0:125x2 2þx
a x ¼ paf 2 ln 2 2 1 þ af O þ 12 3 2
Randomly oriented ribbons with infinite length (O ¼ 0)
a b c d
For ribbons, length is infinite, width, w; thickness, t; aspect ratio, a ¼ w/t. For disks, circular shape of diameter d and thickness t; aspect ratio, a ¼ d/t. m is a geometric factor, which depends on the shape of the dispersed particles. O is an orientation function, which depends on the angle between the normal unit vector of silicate layers and the direction of the preferred orientation.
grade) was purchased from NatureWorks (USA). It has a density of 1.24 g/cm3 and weight average molecular weight of 100000 g/mol. The Elvaloy PTW, a terpolymer of ethylene, butylacrylate (BA) and glycidyl methacrylate (GMA), supplied by DuPont (USA), was used as a reactive compatibilizer. It has a melt flow index of 12 g/10min (190 C/2.16 kg) and density of 0.94 g/cm3. Two types of nanoclay, supplied by Southern Clay Products Inc., USA, were Cloisite 30B, modified with bis-(2-hydroxyethyl) methyl tallow alkyl ammonium cations and Cloisite 15A, modified with dimethyl, dehydrogenated tallow quaternary ammonium cations. Prior to melt
blending, all of the materials were dried for 4 h at 80 C in a vacuum oven. 2.2. Sample preparation PP/PLA/clay nanocomposites were prepared via melt blending in a co-rotating twin-screw extruder (Leistritz, Micro 27, Germany), with screw diameter of 27 mm, L ¼ 36D, screw speed of 150 min1 and throughput of 10 kg/h. The temperature profile was 180 Ce210 C from hopper to die. The blending was carried out
Table 2 Composition of the samples: uncompatibilized blends, PP-rich system, PLA-rich system. Uncompatibilized blends
PP-rich system
PLA-rich system
Pure PP PP/PLA 90/10 PP/PLA 75/25 PP/PLA 50/50 PP/PLA 25/75 PP/PLA 10/90 Pure PLA
PP/PLA 75/25 PP/PLA/PTW 75/25/5 PP/PLA/Cloisite 15A 75/25/1 PP/PLA/Cloisite 15A 75/25/3 PP/PLA/Cloisite 15A 75/25/5 PP/PLA/Cloisite 15A 75/25/7 PP/PLA/PTW/Cloisite 15A 75/25/5/5
PP/PLA 25/75 PP/PLA/PTW 25/75/5 PP/PLA/Cloisite 30B 25/75/1 PP/PLA/Cloisite 30B 25/75/3 PP/PLA/Cloisite 30B 25/75/5 PP/PLA/Cloisite 30B 25/75/7 PP/PLA/PTW/Cloisite 30B 25/75/5/5
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according to the compositions listed in Table 2. As shown, a complete range of composition of unfilled PP/PLA blends was prepared for considering the effect of composition on the permeability of the blends. Moreover, two systems were selected in order to consider the effect of incorporation of clay and compatibilization on permeability, i.e. PP-rich (75/25) and PLA-rich (25/75). For each type of the blends, as well as for the neat components, the same processing procedure was applied so that the thermo-mechanical history of the blends and that of neat polymers remains similar. The extruded samples were pelletized at the die exit, dried and compression molded to film using a press (20 MT Mini Lab, Labtech, Thailand). For each sample, an equal weight of 3.5 g was pre-heated at 260 C for 30 s and pressed under 15 MPa pressure for 10 s in a thin film mold, followed by quenching at room temperature, to obtain thin film for the permeability measurement. The thickness of the films measured by a Millitron micrometer was 100e150 mm for all of the samples. 2.3. Characterization 2.3.1. Transmission electron microscopy The dispersion quality of the nanoparticles within the matrix and the nanostructures of the nanocomposites were investigated using a transmission electron microscope (TEM), Philips EM208 (Netherlands), operated at 100 kV. The samples were microtomed at 160 C. 2.3.2. Scanning electron microscopy Scanning electron microscopy (SEM) was used to characterize the morphology of the blends. Each sample, after proper drying, was cryofractured in liquid nitrogen at 160 C, then sputter coated with 3 nm platinum prior to examination under a NEON 40 EsB (Carl Zeiss, Oberkochen, Germany). SEM images were analyzed using image processing software (JMicroVision v1.27). The volume (Rv) and number (Rn) average radii and polydispersity of the particles (PD), were calculated using Equations (11)e(13) [27e30]. At least 200 particles were considered to measure the parameters.
P nR Rn ¼ P i i ni P RV ¼ P
PD ¼
ni R4i ni R3i
RV Rn
(11)
(12)
(13)
where ni is the number of the droplets having radius Ri. 2.3.3. Differential Scanning Calorimetry (DSC) A Mettler DSC (STARe SW 10.00) was used to study the degree of crystallinity (Xc) of the specimens. About 5 mg of each sample was scanned in a cycle of heatingecoolingeheating from 20 to 200 C at 10 K/min. In order to determine the crystallization enthalpy (DHc), cold crystallization enthalpy (DHcc) and melting enthalpy (DHm), the cooling and second heating scans were used. The Xc of samples was calculated using the following equation [29,30]:
Xc ¼
DH 100 0 $½1 ð%wt:filler=100Þ DHm
(14)
where DH ¼ DHc for cooling curves, i.e. the crystallization enthalpy of the sample, or DH ¼ DHmDHcc for second heating curves, DHm
is the melting enthalpy of the sample, DHcc is the cold crystallization enthalpy of the sample, DH0m is the melting enthalpy of the 100% crystalline polymer matrix (201.1 J/g for PP [31] and 93.0 J/g for PLA [32]) and %wt of filler is the total weight percentage of nanoclay and blend component [32,33]. 2.3.4. Oxygen permeability The permeability to oxygen was measured using a gas permeability tester (Coesfeld, GDP-C, Germany). This compact gas permeability tester determines the permeability of dry gases for materials using the manometric method. The permeation at the bottom chamber of the test specimen is determined by the evaluation of the increase in pressure in the previously evacuated volume versus cm3/m2.24 h bar. The increase in pressure during the test period is evaluated and displayed by an external computer. The average values of three permeability measurements were reported. 2.3.5. Molecular dynamic simulation methodology The diffusivity of oxygen in the pure polymers, PP-rich and PLArich blends were calculated by constructing an amorphous cell containing oxygen and polymer/blend. After constructing the cell, a molecular dynamics simulation performed to calculate the mean square displacement of the oxygen molecule. The modeling was according to Charati and Stern [34] which examined the diffusion of gases in silicone polymers. Materials Studio® 6.0 (Accelrys Software Inc., San Diego, CA) was used for all simulations. In each modeling the following steps were performed: 1) Setting up the initial structures 2) building an amorphous cell 3) relaxation of the cell 4) running and analyzing molecular dynamics 5) exporting data and calculating the diffusivity. The first step is to specify the constituent molecules of the cell. The oxygen molecules and investigating structures must be added to the built amorphous cell. In order to start the simulation, the chains of the polymers were constructed. Then, a cell containing four molecules of oxygen and eight chains of the polymer components or blends was built. The compositions of the selected blends, PP-rich (75/25) and PLA-rich (25/75), were set at the amorphous cell in this step. The target density of the final configurations was determined as 0.95. When an amorphous cell is generated, the molecules may not be equally distributed throughout the cell, creating areas of vacuum. To correct this, a short energy minimization was performed to optimize the cell. After the minimization, a short molecular dynamics simulation should be run to equilibrate the cell. This procedure of minimization and molecular dynamics is known as relaxing the structure and should be carried out whenever an amorphous cell is constructed [35]. For the purpose of this modeling, the constant-temperature, constant-volume (NVT) ensemble was used, as it is a faster ensemble [34e37]. Systematic temperature was set to 300 K in MD runs and a constant temperature control method was used. For the NVT ensemble, the system was equilibrated when the energy in the live update chart document became constant. The velocity-scale thermostat was used during equilibration. Iteration step was chosen as 5000, sampling time was set to 250 ps, and total 2ns trajectories were collected for the analysis. For all systems, COMPASS force fields were applied. The MSD typically has two regions. At short times, the gas molecule collides inside a small pocket of free volume. Since the confined molecules cannot diffuse on this time scale, the MSD levels off to a constant. On a longer time scale, the molecule jumps out of the confined area to another pocket of free volume. The resulting motion of repeated jumps is diffusion, characterized by a MSD that is linear in time (Equation (10)). The MSD in Forcite is averaged over all atoms in the molecules. To determine the diffusion coefficient it is necessary to fit a straight line, y ¼ a.x þ b, to the data in the diffusive regime and abstract the slope a. The unit of a is then Å2/ns.
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According to the above definition and Equation (10), D then follows as [35]:
D ¼ a=6
(15)
3. Results and discussion 3.1. Blend morphology and permeability relationship Permeation has a strong relationship with the morphology of the system. The SEM micrographs of the blends are shown in Fig. 1 and the graphs of permeability values of the pure components and the blends in correlation to their morphologies in Fig. 2. Comparing the micrographs of Fig. 1a to b and 1c to d shows that the discrete PLA spherical domains are almost uniformly dispersed in the matrices, but their sizes in the PLA-rich blends are greater than those in PP-rich blends. This is attributed to the greater viscosity of PP as compared to that of PLA, leading to a greater decrease in the size of the droplets of PLA in the PP-rich blends. This is due to the fact that a ratio of the viscosities of greater than 1 (hPP/hPLA > 1)
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leads to a fined droplet morphology [27,38]. Homogenously distributed barrier layers or droplets can increase the tortuosity of the path and improve barrier properties of the blends. As shown, the PP-rich blends have greater oxygen barrier compared to the PLA-rich ones. While blending of PP with 10 wt.% of PLA led to a decrease of 8% in permeability, the 75/25 blend had a decrease of more than 600%. This composition seems to be the optimum composition for achieving greater barrier properties. The lower permeability value for PP-rich blend is attributed to the optimum size of domains of the oxygen barrier component (PLA) in the blend leading to an increase in barrier properties due to an increase in tortuosity of permeant path. Therefore, a greater area of the barrier droplets can improve barrier properties of the final blend. Table 3 shows the volume (Rv) and number (Rn) average radii and polydispersity (PD) values of the particles. Regarding the Rn and permeability values of the PP-rich blends, the Rn value of 75/25 blend is greater than 90/10 blend, leading to lower permeation. An interesting point is that exceeding 25 wt % of PLA in the blends led to a dramastic increase in permeability, even greater than that of pure PP film. This is attributed to the state of the blend components, their polarity, crystallinity behavior etc.
Fig. 1. Scanning electron micrographs of PP/PLA blends: a) 90/10 b) 10/90 c) 75/25 d) 25/75 e) and f) 50/50.
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Fig. 2. Permeability values and morphology of the PP/PLA blends.
As mentioned before, the permeability coefficient, P, is the product of solubility, S, and diffusivity, D, coefficients. Both of the coefficients are related to the polarity of permeating molecules as well as the polymer. Because the polarity of PLA is greater than PP, the dissolving of oxygen, as a non-polar gas, into PLA is more difficult than PP. Therefore, oxygen permeability of PLA is lower than PP. Moreover, as a polymer is cooled through the rubbereglass transition from the melt, molecular mobility is rapidly reduced. The change in segmental mobility of the polymer chains at Tg affects the diffusion phenomenon [34e36]. Regarding intrinsically structural differences of PP and PLA, the glass transition temperature of PLA is greater than PP. Hence, it can be said that, while PP is in rubbery plateau at ambient condition, PLA is in glassy state. Therefore, PLA has a lower segmental mobility comparing to that for PP, leading to a difficult center-of mass mean-square displacement and, consequently, lower diffusion. Hence, PLA droplets have functioned as oxygen barriers in PP matrix of PP-rich blends at the experimental conditions. Differently, PP has been at the rubbery plateau at the test conditions. Meares [35] plotted the diffusion coefficients for argon and oxygen in poly(vinyl acetate) (PVAc) in a temperature range encompassing the Tg to be composed of three distinct regions of differing slopes, indicating different diffusion coefficients. For polymers, the total amount and the distribution of free volume cavities can directly influence how fast small molecules can diffuse [40]. Another affecting factor is the crazes in the texture of the main
Table 3 The quantitative values obtained from SEM micrographs of the blends and blend nanocomposites. Components
Composition
Rn [mm]
Rv [mm]
PD
PP/PLA PP/PLA PP/PLA PP/PLA PP/PLA PP/PLA/PTW PP/PLA/Cloisite 15A PP/PLA/PTW/Cloisite 15A PP/PLA/PTW PP/PLA/Cloisite 30B PP/PLA/PTW/Cloisite 30B
90/10 75/25 50/50 25/75 10/90 75/25/5 75/25/5 75/25/5/5 25/75/5 25/75/5 25/75/5/5
0.34 0.54 0.82 0.64 0.62 0.28 0.40 0.23 0.54 0.57 0.31
0.56 0.81 1.22 0.99 0.96 0.69 0.84 0.43 0.79 1.07 0.61
1.65 1.52 1.49 1.55 1.56 2.45 2.08 1.31 1.46 1.88 1.94
phase near the interphase between two phases of PLA-rich blends, as shown in the circles. These crazes arose because of incompatibility of the blend components. Considering the micrographs of 10/90 and 25/75 blends (Fig. 1b and d respectively), and 50/50 blend (Fig. 1e and f) shows that these crazes have increased with increase in PLA content. As shown, the 10/90 blend has the most crazes. These results are in good accordance to our previous published work on the homogeneity of the two PP-rich and PLArich systems using rheological studies. This study showed better compatibility for 75/25 composition compared to 25/75 blend. Structural features which slow the polymer gas transmission rates include polymer chain mobility, chain packing, degree of crystallization, etc. Crystallization behavior of the film samples is another significant factor, determining the diffusion of gases through a membrane [39]. The degree of crystallinity is of great importance because the mass transport phenomena take place more easily in amorphous regions compared to crystal lattices. The DSC thermographs of the second heating and cooling of pure components and the blends are shown in Fig. 3a and b, respectively, and the degrees of crystallinity using Equation (14) are reported in Table 4. As shown, the crystallinity of PP-rich blend is more than that of PLA-rich one, having good accordance with permeability values. Moreover, generation of defects can affect the diffusion [39,41]. As shown in the thermograph of PLA-rich sample (Fig. 3b), the small area of the PP crystallization peak compared to that of PP-rich one implies crystal defects [29,30]. The mechanism of diffusion in the defects is analogous to gas diffusing in very small pores, so that diffusion through them is much easier than through amorphous regions in the polymer [39,41]. That is to say, the presence of microvoids in the matrix facilitate the formation of microchannels, leading to a completely different set of transport mechanisms (Kudsen flow) [39]. As mentioned, the small melting peak corresponding to PP in PLA-rich blend, implying a defective crystal structure of PP chains could act similar to pores or microvoids, and could affect permeability of the corresponding sample significantly. Table 5 shows the theoretical values of proposed models for the blends. The experimental values are shown again for comparing to theoretical values. As shown, these models failed to predict the permeability values well, especially for PLA-rich blends. This is attributed to incompatibility in these compositions, leading to
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Fig. 3. DSC thermographs of the second heating from 20 to 200 C at 10 K/min for the components and the blends a) second heating b) cooling.
Table 4 Degree of crystallization for pure components, PP-rich and PLA-rich systems. Components
Composition
Xc [%]
Pure PP PP/PLA PP/PLA/PTW PP/PLA/Cloisite 15A PP/PLA/PTW/Cloisite 15A PP/PLA PP/PLA/PTW PP/PLA/Cloisite 30B PP/PLA/PTW/Cloisite 30B Pure PLA
100/0 75/25 75/25/5 75/25/5 75/25/5/5 25/75 25/75/5 25/75/5 25/75/5/5 0/100
50.7 71.6 51.2 41.0 37.6 34.4 32.1 48.3 22.8 12.0
appearance of microvoids at the interface, as explained. Another reason is the difference in crystallinity behavior between two systems. 3.2. Correlation of nanocomposites' microstructures to permeability values Fig. 4 shows the effect of incorporation of clay nanoparticles and compatibilization by PTW on the morphology of the systems. As shown, the both factors led to decrease the crazes, and these effects are reflected in the permeability data of Figs. 2, 5 and 6, so there was a significant decrease in the permeability of comatibilized and Cloisite 30B filled PLA-rich system. As seen in Fig. 3, the increase in permeation with compatibilization is due to decrease in size of disperse phase and higher value of polydispersity of the droplet size. This corresponds to the effect of PTW compatibilizer on interfacial interaction between the phases, leading to reduced droplet size and greater polydispersity. Interestingly, with the introduction of two clays into the blends, the size of the dispersed domains decreased significantly (Figs. 2c, d and 4b). Simultaneous incorporation of PTW compatibilizer and clay could lead to more Table 5 Experimental and theoretically calculated permeability values of pure components and the blends vs. cm3/m2.day.bar. Sample
Experimental
PP PP/PLA PP/PLA PP/PLA PP/PLA PP/PLA PLA
349 320 58 420 6910 8260 24
90/10 75/25 50/50 25/75 10/90
± ± ± ± ± ± ±
2 3 1 15 86 105 2
Parallel
Series
Paul
349 324 286 212 126 67 24
349 173 95 52 33 27 24
349 285 207 113 55 34 24
decrease in the size of the dispersed-phase domains of the both systems (Table 3, Fig. 2e and f). This effect can increase oxygen permeability in PP-rich blends due to decrease of the size of barrier droplets of PLA, as seen in permeability values of compatibilized PP-rich and PLA-rich in graph of Fig. 2. However, the presence of impermeable clay layers could compensate for this effect significantly. The graphs of Figs. 5 and 6 show a decrease in permeability due to dispersion of barrier layers, i.e. Cloisite 15A and 30B, into the PPrich and PLA-rich systems, respectively. The TEM micrographs of two systems are shown in Fig. 7. Nanocomposites are multiphase systems in which the coexistence of phases with different sorption and diffusion can cause complex transport phenomena [41,42]. Montmorillonite was reported to increase superficial adsorption and specific interactions with some gases and solvents [42]. Moreover, the presence of silicate layers are expected to cause a decrease in permeability of oxygen because of more tortuous paths for the diffusing molecules that must bypass impenetrable platelets (Figs. 7 and 8). This phenomenon is significant when the filler is of nanometer size with high aspect ratio. Fig. 8 shows effect of intercalation and exfoliation on the path tortuosity of the two systems with the help of schematic representation. As shown, not only the content of clay, but also the state of dispersion of the inorganic platelets and the extent of exfoliation in the polymer phase is important for improving the barrier properties of the blends due to increase in tortuosity factor. As expected, there is a decrease in permeability values with increase in nanoclay content up to 5 wt. % in both systems. Exceeding 5 wt.% nanoclay had no significant effect on permeation due to the aggregation phenomenon at high concentrations that decreases the tortuosity factor, leading to increase in the permeation [19,42]. In order to use the permeation models of nanocomposites, the permeability value of the pure blend was assumed as the permeability of the matrix (P0). TEM micrographs with the help of the image analyzer software were used to estimate the average aspect ratio, i.e. a, which is the ratio of the length (l) to the thickness (t) of clay particles as shown in Fig. 9. The predicted permeability values obtained by various models are summarized in Table 6. As shown, the permeability decreases as the volume fraction of clay nanoparticles increases. However, the effect of exfoliation is more pronounced in all of the models due to an intensive increase in aspect ratio of the layers (a), leading to a significant decrease in permeability values of compatibilized nanocomposites. As shown, the permeability decreases as the geometric factor m of the Cussler model [16] increases. Because the predictions of Bharadwaj model [19] with O ¼ 0 (randomly oriented ribbons with infinite length) and Cussler model with [21] m ¼ 1/2
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Fig. 4. Scanning electron micrographs of compatibilized blends and blend nanocomposites: a) PP/PLA/PTW 75/25/5 b) PP/PLA/PTW 25/75/5 c) PP/PLA/Cloisite 15A 75/25/5 d) PP/ PLA/Cloisite 30B 25/75/5 e) PP/PLA/Cloisite 15A/PTW 75/25/5/5 f) PP/PLA/Cloisite 30B/PTW 25/75/5/5.
Fig. 5. Permeability of PP-rich samples encoded: 1) PP/PLA 75/25 2) PP/PLA/PTW 75/ 25/5 3) PP/PLA/Cloisite 15A 75/25/1 4) PP/PLA/Cloisite 15A 75/25/3 5) PP/PLA/Cloisite 15A 75/25/5 6) PP/PLA/Cloisite 15A 75/25/7 7) PP/PLA/Cloisite 15A/PTW 75/25/5/5.
Fig. 6. Permeability of PLA-rich samples encoded: 1) PP/PLA 25/75 2) PP/PLA/PTW 25/ 75/5 3) PP/PLA/Cloisite 30B 25/75/1 4) PP/PLA/Cloisite 30B 25/75/3 5) PP/PLA/Cloisite 30B 25/75/5 6) PP/PLA/Cloisite 30B 25/75/7 7) PP/PLA/Cloisite 30B/PTW 25/75/5/5.
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Fig. 7. TEM micrographs of: a) and b) PP/PLA/Cloisite 15A 75/25/3 c) and d) PP/PLA/Cloisite 15A/PTW 75/25/5/5 e) and f) PP/PLA/Cloisite 30B 25/75/3 g) and h) PP/PLA/Cloisite 30B/ PTW 25/75/5/5.
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Fig. 8. The effect of intercalation and exfoliation on the path tortuosity of the two systems with the help of schematic representation: a) PP/PLA/Cloisite 15A 75/25/3 b) PP/PLA/ Cloisite 30B 25/75/3 c) PP/PLA/Cloisite 15A/PTW 75/25/5/5 d) PP/PLA/Cloisite 30B/PTW 25/75/5/5 (The red and black chains show the blend components). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
(ribbons with infinite length and equal probability of alignment and misalignment) are close to our experimental values of PP-rich system, it is concluded that intercalated and exfoliated layers have an equal probability of alignment and misalignment in this system. In the case of PLA-rich system, all of the models failed to predict correctly, as shown in Table 6. 3.3. Molecular dynamic results
Fig. 9. Measuring the aspect ratio of an exfoliated (left) and intercalated (right) clay layers by image analysis. The layers are assumed to be laid in main direction (MD).
Images of different steps in MD for PP and PLA are shown as examples in Fig. 10. From the plot of MSD as a function of time, diffusion coefficient can be derived from the slope of a linear fitting to data points according to Equation (10). Fig. 11 shows the graphs of MSD versus time. Then, the value of linear coefficient a (slope of the fit line) must be divided by 6 (Equation (15)) to obtain the diffusion coefficient in Å2/ps, and the resulting value must be converted to the more commonly used unit cm2/s. Table 7 shows the calculated values of D. Comparing the calculated D values of PP and PLA to those reported in the literature shows that they are very different to the experimental values; since the polymers were quite short, the cell size rather small and the run length was very short and did not allow time for proper Einstein diffusion to occur. The most effective factor determining the accuracy of D values is the
Table 6 Predicted permeability values of PP-rich and PLA-rich systems vs. cm3/m2.day.bar using the models. Sample
Experimental a value fClay
PP/PLA/Cloisite 15A 75/25/1 PP/PLA/Cloisite 15A 75/25/3 PP/PLA/Cloisite 15A 75/25/5 PP/PLA/Cloisite 15A 75/25/7 PP/PLA/Cloisite15A/PTW 75/25/5/5 PP/PLA/Cloisite 30B 25/75/1 PP/PLA/Cloisite 30B 25/75/3 PP/PLA/Cloisite 30B 25/75/5 PP/PLA/Cloisite 30B 25/75/7 PP/PLA/Cloisite30B/PTW 25/75/5/5
54 46 35 35 3.5 3100 2830 2640 2650 2100
± ± ± ± ± ± ± ± ± ±
2 4 2 4 1 45 43 25 68 22
80 80 80 80 200 50 50 50 50 150
0.006 0.018 0.029 0.041 0.028 0.007 0.021 0.034 0.047 0.319
Nielsen Cussler Cussler Fredrickson (random array) and Bicerano m ¼ 1 m ¼ 1/2 m ¼ 2/27
Gusev Bharadwaj and Lusti O¼1 O¼0
45.0 31.0 23.7 19.0 1.2 5835.8 4451.5 3597.7 3019.0 188.3
43.8 31.4 24.1 19.1 0.02 5635.8 4423.3 3644.1 3076.2 7.4
55.7 43.4 30.6 21.4 0.09 6768.5 6040.4 5037.1 4064.9 18.3
53.3 33.3 19.1 11.6 0.03 6654.4 5302.1 3798.5 2672.7 5.6
55.4 42.0 28.4 19.2 0.05 6756.3 5945.4 4841.4 3802.0 11.1
57.3 54.0 49.0 43.3 0.3 6845.7 6630.6 6319.4 5939.8 74.0
12.8 3.9 1.9 1.2 0.003 2433.1 944.8 524.7 338.2 0.7
44.9 31.0 23.7 19.1 1.2 5835.8 4451.5 3597.7 3019.0 188.3
52.7 44.5 38.5 33.9 3.4 6481.7 5766.7 5193.5 4724.3 523.1
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Fig. 10. The images of different steps in MD: a) and b) first configurations of PP and PLA chains built of 20 repeating units, respectively, c) and d) the amorphous cells for calculation of diffusion coefficient of oxygen through PP, e) and f) the amorphous cells for calculation of diffusion coefficient of oxygen through PLA, g) and h) the final configurations of PP and PLA after 2 ns trajectories, respectively.
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Fig. 11. The graphs of MSD vs. time after MD, representing the fitting line relation: a) PP b) PLA c) PP-rich and d) PLA-rich systems e) comparing all of the samples.
time of the run. As mentioned, this time (2 ns) was very short in this work because of complexity of the constructed amorphous cell for the blends. The run times of 10 ns for pure polymers were reported Table 7 Diffusion coefficient of simulated samples. Sample
MD simulation values of D (cm2/s)
PP PP/PLA 75/25 PP/PLA 25/75 PLA
3.4 3.0 3.7 3.3
105 105 105 105
by other researchers [e.g. 36, 42]. Comparing the MSD plots versus time for all samples (Fig. 11e), and the data shown in Table 7, confirms the highest permeability value of the PLA-rich blend due to the largest value of D. In other words, the results of MD simulation also confirm that the PP-rich blends have greater oxygen barrier properties compared to those for the PLA-rich blends due to lower D value. Therefore, the simulated results are in good agreement with the available experimental data. As reported the calculated diffusion coefficients using MD simulation for several polymers have not been in particularly good agreement with experiment [36,39e42].
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4. Conclusions Investigations on oxygen gas permeability in PP/PLA/clay nanocomposite films revealed that the permeation is highly dependent on blend composition, clay loading and state of clay dispersion governed by compatibilization. Compared to PLA-rich system, the PP-rich films showed greater barrier to oxygen. Morphological studies revealed that the lower permeability of PPrich films is mainly due to reduced size of dispersed domains of the oxygen barrier component, i.e. PLA in the PP matrix, leading to more tortuous permeant path. Moreover, the higher degree of crystallinity observed in PP-rich films as compared to the PLA-rich system was found to be also responsible for the higher barrier properties of the PP-rich films. From TEM studies, it was concluded that the presence of silicate layers in the blends decreases the oxygen permeability due to the more tortuous paths for diffusion of oxygen molecules that must bypass the impenetrable platelets. Incapability of several existing models for PLA-rich blends was attributed to compatibility issues, presence of microvoids at the interphase region, degree of crystallization and crystalline defects. The deviation of the proposed models was pronounced in presence of compatibilizer due to better dispersion of clay and an intensive increase in aspect ratio of the layers as a result of exfoliation, leading to a significant decrease in permeability values of compatibilized nanocomposites. Molecular dynamics simulations showed good correlation to the permeability values for different compositions. The MD simulation is a strong tool for investigation and prediction of gas permeability through polymers and blends and nanocomposites. Acknowledgments The work described in this paper was supported by a grant from the Iran Polymer and Petrochemical Institute (IPPI) (84150). The authors gratefully acknowledge the Research Vice Chancellor of IPPI and his co-workers due to their helps and assistances. References [1] D.R. Paul, C.B. Bucknall, Polymer Blends, John Wiley & Sons Inc., New York, 2000. [2] L.A. Utracki, Polymer Blends Handbook, Kluwer Academic Publishers, Netherlands, 1999. [3] R.T. Parry, Principle and Applications of Modified Atmosphere Packaging of Food, Chapman & Hall, New York, 1993. [4] M. Pluta, E. Piorkowska, Tough and transparent blends of polylactide with block copolymers of ethylene glycol and propylene glycol, Polym. Test. 41 (2015) 209e218. [5] V. Goodarzi, S.H. Jafari, H.A. Khonakdar, B. Ghalei, M. Mortazavi, Assessment of role of morphology in gas permselectivity of membranes based on polypropylene/ethylene vinyl acetate/clay nanocomposite, J. Membr. Sci. 445 (2013) 76e87. [6] R.J. Shields, D. Bhattacharyya, S. Fakirov, Oxygen permeability analysis of microfibril reinforced composites from PE/PET blends, Compos. A 39 (2008) 940e949. [7] T. Ogasawara, Y. Ishida, T. Ishikawa, T. Aoki, T. Ogura, Helium gas permeability of montmorillonite/epoxy nanocomposites, Compos. A 37 (2006) 2236e2240. [8] Y.C. Ke, P. Stroeve, Polymer-layered Silicate and Silica Nanocomposites, first ed., Elsevier Science, 2005. [9] R. Hiroi, S.S. Ray, M. Okamoto, T. Shiroi, Organically modified layered titanate: a new nanofiller to improve the performance of biodegradable polylactide, Macromol. Rapid Commun. 25 (2004) 1359e1364. [10] Y. Elsayed, C. Lekakou, P. Tomlins, Monitoring and modelling of oxygen transport through un-crosslinked and crosslinked gelatine gels, Polym. Test. 40 (2014) 106e115.
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