# Off-line and in-line analysis of phase morphology and evolution during solidification process of PP/PEOc blend in an internal mixer.

INTRODUCTIONBlending of two or more polymers has proved an effective and important alternative method to introduce raw materials with tailor and improved properties. There has been an extraordinary growth in the field of polymer-blend production during the last few years compared with homopolymers and copolymers (1), (2). The properties of immiscible polymer blends depend strongly on their phase morphology, i.e., the domain size, the size distribution, and the shape of the dispersed domains. It is generally desired to produce blends with well-defined, stable, and reproducible morphologies (3), (4).

As the mechanical and the physical properties of the product depend on the final morphology, it is important to understand the influencing factors of phase morphology. The ultimate morphology of polymer blends is determined, to a large extent, by material parameters (such as composition, viscosity, elasticity, and interfacial tension of the component phases), as well as process parameters (such as mixing time, mixing temperature, rotation rate of rotor or screw, and type of mixer). Under fixed processing conditions, it may be summarized qualitatively that the domain size tends to decrease with the matrix viscosity and the matrix elasticity and increase with the interracial tension, the disparity in phase viscosity, and the volume fraction of the dispersed phase (5-7).

On the other hand, it must be taken into account that the final morphology also depends on the quenching processing conditions. Up to now, the research fields of solidification process mainly focused on metal materials and thermosetting resin composites (8), (9). As for the latter, it mainly paid attention to the studies of curing reaction and its kinetic. Although solidification process of immiscible polymer blends has been studied, it mainly focused on the formation and development process of crystallization (10-13). However, the study of phase morphology and evolution under natural cooling condition during solidification process of polymer blend has not been explored.

In early literature, morphology development was studied using scanning electron microscopy (SEM). Before SEM observation, specimens were sampled at certain intervals and then frozen in liquid nitrogen. Light scattering techniques, which give information on optical properties, have proven to be useful for obtaining deep insights into the molecular and structural parameters of polymers. The main advantage is that the measurement does not damage or interrupt the system. Deanin and Crugnola (14) obtained the correlation length, which can characterize the phase dimension by light scattering techniques. For traditional small-angle laser scattering (SALS), the central speckle usually covers the scattering information with thin samples and the multiple scattering complicates the process of scattering information with thick samples. While back SALS (BSALS) has experimental differences from SALS, and it is the scattering part in reverse, it is independent of the central speckle and it is believed to give direct information on the formation, dissolution, and deformation of molecular aggregates (15), (16).

In this study, phase morphology and evolution of PP/PEOc blend during solidification process was investigated based on the image analysis of SEM micrographs. Furthermore, in-line BSALS was used to investigate the solidification process of PP/PEOc blends under the natural cooling conditions in real time after mixing. This research opens a different window to the study of blend solidification and enables unambiguous understanding of solidification processes in polymer blend.

IMAGE ANALYSIS

The Average Size of the Dispersed Phase

The SEM micrographs were binarized using our self-made software EMPP, and the particles of the dispersed phase were chosen as our studied object. The number of pixels embodied by every particle was calculated using the software. Then, the software created a circle that enclosed the same number of pixels and defined the diameter of the circle as that of each particle. Thus, the equivalent diameter ([d.sub.p]) of each particle can be obtained. As an example, a detailed analysis process used in the work is presented in Fig. 1a-d. Consequently, the average size of the dispersed phase over all particles could be calculated by averaging the [d.sub.p] using Eq. 1.

[FIGURE 1 OMITTED]

[d.sub.p] = [[[[SIGMA].sub.i.sup.[infinity]][n.sub.i][([d.sub.p]).sub.i]]/[[[SIGMA].sub.i.sup.[infinity]][n.sub.i]]] (1)

where [n.sub.i] is the number of particles. To obtain more reliable data, about 100 particles were considered to calculate this structure parameter for each sample.

To compare [d.sub.p] with other diameters, the SEM micrographs were also quantitatively analyzed by image analysis technique to obtain the number average diameter ([D.sub.n]). About 100 particles were also considered for the diameter measurements. The diameter [D.sub.n] is determined using the following equation,

[D.sub.n] = [[[[SIGMA].sub.i][N.sub.i][D.sub.i]]/[[[SIGMA].sub.i][N.sub.i]]] (2)

where [D.sub.i] is the diameter of each droplet and [N.sub.i] is the number of droplet with a diameter [D.sub.i].

The Average Characteristic Length

In this article, the characteristic length (L) of the dispersed phase was used to describe the particles sizes of dispersed phase. In a similar manner to the definition given by Guinier and Fournet (17), L is defined as the span from one side of a particle to the other, which is shown in Figs. 1d and 2. and one could easily understand its meaning. Scanning the image using a set of lines circling around the centroids of domains for every 2 degrees, a set of L could be easily obtained.

[FIGURE 2 OMITTED]

The probability density P(L) could be defined by finding L from a frequency of the events N(L),

P(L) = [N(L)/[[[integral].sub.0.sup.[infinity]]N(L)dL]] (3)

A normalized probability function P(L/[L.sub.m]) is further defined by

P(L/[L.sub.m]) [equivalent to] P(L)[L.sub.m]/c (4)

to compare the shapes of the probability density distribution functions P(L) obtained at different solidification temperatures. Therefore, the average characteristic length. [L.sub.m], could be obtained by

[L.sub.m] = [[integral].sub.0.sup.[infinity]] LP(L)dL (5)

P(L/[L.sub.m]) is dimensionless, and the constant c is defined by

[[integral].sub.0.sup.[infinity]] P(L/[L.sub.m])d(L/[L.sub.m]) = 1/c (6)

Furthermore, by plotting P(L/[L.sub.m]) against the normalized characteristic length L/[L.sub.m], the shapes of P(L/[L.sub.m]) obtained at different solidification temperatures can be compared. Thus, the function of P(L/[L.sub.m]) can be used as a scaling function to study the self-similarity of the phase morphology at different solidification temperatures.

The Fractal Dimension [D.sub.f]

Let F(L) be the probability of the characteristic length of dispersed phase larger than L. This probability, related to the probability density, P(L), is given by

F(L) = [[integral].sub.L.sup.[infinity]] P(L)dL (7)

If F(L) is fractal, it is dimensionless. Thus, the change of scale corresponded to transforming L to [lambda] x L. The aforementioned fractal property of the distribution required the invariance

F(L) [varies] F([lambda] x L) (8)

for any positive [lambda]. The only functional form that satisfies Eq. 8 is the power law:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (9)

where [D.sub.f] corresponded to the [D.sub.f], which is defined by box-counting methods (18-20). However, it was not recommended that we called this [D.sub.f] a dimension if L did not denote a one-dimensional measure, that is, a length. In that case, we should simply regard [D.sub.f] as a parameter characterizing the distribution.

Eq. 9 can be transformed to

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (10)

Plotting log P(L) against log L, as shown in Fig. 3, the [D.sub.f] can be obtained.

[FIGURE 3 OMITTED]

To show the relation between [D.sub.f] and the width of the dimensionless region, consider the plot shown in Fig. 3. The larger the absolute value of the slope, the larger the [D.sub.f]. Meanwhile, the width of the dimensionless region was larger. Therefore, a larger [D.sub.f] corresponded to a larger width of the dimensionless region, that is, the distribution of the main part of the characteristic length was narrower.

THEORETICAL BACKGROUNDS

Integral Invariant

If the dispersed phase in polymer blends can be regarded as a particle, we can calculate the size of the particle using a correlation function. For this purpose, we used a modification of the Dcbye-Bueche description of scattering from random heterogeneous media, which gives for spherically symmetrical systems (21), (22). The scattering intensity is given as

[I.sub.s] = 4[pi][KV.sub.s][bar.[[eta].sup.2]] [[integral].sub.0.sup.[infinity]] [gamma](r) [[sin(hr)]/hr][r.sup.2]dr (11)

where K is a proportionality constant and h = (4[pi]/[lambda]). sin([theta]/2). [[bar.[eta]].sup.2] is the mean square fluctuation and [eta] is the fluctuation in scattering power of the system, which for SALS is equal to the deviation in polarization from its mean value at position r. [gamma](r) is the correlation function corresponding to fluctuation of medium and can be obtained by the inverse Fourier transformation,

[gamma](r) = [C/[[eta].sup.2]][[integral].sub.0.sup.[infinity]] I(h)[sin(hr)/hr][h.sup.2]dh (12)

If r = 0, the density correlation function had a value of 1, i.e.,

[gamma](0) = [C/[[eta].sup.2]][[integral].sub.0.sup.[infinity]]I(h)[h.sup.2]dh = 1 (13)

[[bar.[[eta].sup.2]]/C] = [[integral].sub.0.sup.[infinity]]I(h)[h.sup.2]dh = Q (14)

From Eq. 14, the integral invariant Q is related to the fluctuation of density. In general, Q could be used to describe fluctuations of different parameters such as pressure, dielectric coefficient, composition, etc. In addition, the integral invariant Q also represented the dispersible uniformity of the mixture, including the difference of concentration, the dimension of the dispersed phase, and the acuity of interface. The better the particles were dispersed, the smaller the parameter Q became. Then, Q can be easily calculated from the variation of I(h) with h from the BSALS for the blend from Eq. 4.

Heterogeneity Distance

General for systems not having apparently a defined structure, [gamma](r) often decreased monotonically with r and may be represented by an empirical equation such as

[gamma](r) = exp(-[gamma]/[a.sub.c]) (15)

where the parameter [a.sub.c] is known as correlation distance and can be used to describe the size of the heterogeneity. For discrete particles in dilute solution, [a.sub.c] is related to the particle size. To describe the particles sizes, heterogeneity distance is given by

lc = 2 [[integral].sub.0.sup.[infinity]][gamma](r)dr (16)

If Eq. 15 is substituted into Eq. 16, one can obtain the following equation after rearrangement,

lc = [[integral].sub.0.sup.[infinity]] I(h)hdh/[[integral].sub.0.sup.[infinity]] I (h)[h.sup.2]dh (17)

lc can also easily calculated from the variation of I(h) with h from the BSALS for the blend from Eq. 17.

EXPERIMENTAL PARTS

Materials

The basic materials used in this study were a commercial grade polypropylene (PP1300) supplied by Beijing Yanshan Petrochemical Company, China, and a commercial grade PEOc (Engage 8150. a Metallocene-catalyzed copolymer of ethylene and 1-octene with 25 wt%) provided by Dow Elastomers Company. Characteristics of the materials used are given in Table 1.

TABLE 1. Characteristics of the materials used in this work. Materials Density (kg/[m.sup.3]) MFI (g/10 min) (a) PP 0.90 1.1 PEOc 0.868 0.5 (a) For PP, MFI was measured under 2.16 kg at 230[degrees]C. For PEOc, MFI was measured under 2.16 kg at 190[degrees]C.

Blend Preparation

The polymer blend with composition PP/PEOc = 70/30 (volume ratio) was prepared in an internal mixer (XXS-30 mixer with rotor diameter of 35mm and total volume of 50[cm.sup.3], China). Prior to processing, all materials were dried for 12 h under vacuum at 50[degrees]C. The blend experiments were first performed at constant rotor speed of 40 rpm at 200[degrees]C for 10 min, and then the mixer was stopped to make the blend under the natural cooling conditions without any shear. In addition, the samples were taken out of the mixer at different solidification temperatures (200, 180. 160, 140, 120, 110, and 100[degrees]C, respectively), and they were put immediately into liquid nitrogen for at least 10 min to make sure that the fracture was sufficiently brittle.

Morphological Investigation

The phase morphology of the blend was investigated using a Philips XL-30 ESEM SEM. Because it was difficult to extract the PP phase without affecting the PEOc phase using the solvent, all samples were etched in n-heptane at 60[degrees]C for 10 min after brittle fracture to extract the elastomeric PEOc phase. The samples were dried for a period of 72 h and were coated with gold prior to SEM examination. Considering that the quality and resolution of SEM images were strongly affected by the thickness of the plated gold, the sputter time was strictly controlled to be identical for each sample. The microscope operating at 25 kV was used to observe the specimens, and several microscopy photographs were taken for each sample.

In-line BSALS

BSALS experiments have been performed using self-made equipment including an optical set-up and software. The optical bench is composed of a nonpolarized He-Ne laser source with wavelength of 633nm, classical optical devices and a CCD camera. The scheme of the in-line BSALS system connected to melting mixer is shown in Fig. 4. The detail of inline BSALS system with apparatus and the principle are presented. The system was connected to mixer with a window-made by quartz glass in case that the laser can throw on the specimen in the mixing room. The scattering information from the mixing specimen was tracked by in-line apparatus and gripped by computer. Using our self-made software, these scattering patterns were digitally analyzed to obtain the angular dependence of scattering intensity for the subsequent calculation that was discussed in theoretical background.

[FIGURE 4 OMITTED]

RESULTS AND DISCUSSION

Off-line Analysis

Generally speaking, the coalescence phenomenon of polymer blends generally appeared in the phase separation process. In addition, when prolonged the mixing time or increased the shear rate, the coalescence phenomenon was also observed in some blending system (23), (24).

The PP/PEOc (70/30) blend was mixed in the internal mixer at a rotor speed of 40 rpm for 10 min at 200[degrees]C. Then, the mixer was stopped to make the blend under the natural cooling conditions without any shear. Meanwhile, the temperature and the time were recorded. The PP/PEOc (70/30) blend was chosen to study the phase morphology and evolution of PP/PEOc blend under the natural cooling conditions during solidification process, because the phase morphology showed well-defined droplet-matrix morphology in this blend composition (25). Figure 5 shows the scanning electron micrographs of PP/PEOc (70/30) blend at different solidification temperatures. The light area of the SEM represented the PP phase and black for the PEOc phase. It was evident from the SEM photomicrographs that the PEOc component was the dispersed phase and PP formed the continuous phase. In other words, a droplet-matrix type of morphology could be observed and this type of morphology was observed for many other blends. It also could be seen that the domain size increased slightly as the temperature of the samples decreased.

[FIGURE 5 OMITTED]

Although these micrographs showed a vivid phase structure and morphology, it was hard to quantitatively determine the variation of phase size just based on these micrographs. Meanwhile, its distribution width was also hard to quantitatively determine based on these images only. For this reason, the SEM micrographs were transformed by digital image analysis software designed by our group to obtain the domain size [d.sub.p].

The number average diameter ([D.sub.n]) of the dispersed phase droplets and its average size [d.sub.p] as a function of the solidification temperatures are shown in Fig. 6. The [D.sub.n] and the [d.sub.p] were defined by Eqs. 2 and 1, respectively. Figure 7 shows the distribution of the average size [d.sub.p] as a function of the solidification temperatures. It could be seen that the domain size and its distribution had no significant changes during the solidification process before 120[degrees]C, although the samples would pass though the melting temperature of PP (about 168[degrees]C). When the temperature decreased to the crystallization temperature (about 116[degrees]C), the domain sizes increased dramatically and its distribution width tended to increase. This was due to the coalescence phenomenon. Coalescence, the recombination of particles, was known to take place during the mixing process and must arise from the forced collisions of dispersed droplets. As the temperature decreased, the cooling became slower, allowing for plenty of time for coalescence. So these results indicated coalescence phenomenon was obvious in the solidification process under the natural cooling conditions.

[FIGURE 6 OMITTED]

[FIGURE 7 OMITTED]

In addition, the average characteristic length [L.sub.m] is used to study the domain coalescence during solidification process of PP/PEOc blend in an internal mixer. Figure 8 shows the average characteristic length [L.sub.m] as a function of the temperature during natural cooling. It showed that the changes of [L.sub.m] were in a good agreement with those of [d.sub.p] and [D.sub.n]. Both the variation trends and the values of these three parameters were very similar, demonstrating that [d.sub.p] and [L.sub.m] can be used to characterize the size and size distribution of the dispersed phase. Figure 9a show the plots of P(L) against L at different solidification temperatures. It could be seen that all curves in the plots had a single peak. The position and the width of the peak had no significant changes during the solidification process before 120[degrees]C. After that, they both increased which indicated that the domain size of dispersed phase increased. Meanwhile, the peak height decreased at that temperature, which means that the percentage of smaller sizes decreased, that is, the distribution of sizes becomes less uniform.

[FIGURE 8 OMITTED]

In addition, the distribution of the characteristic length could be further studied in terms of a nonlinear mathematical method, i.e., the [D.sub.f]. It is well known that when studying the fractal behavior, it should be certain whether the object has a fractal character. As discussed in the Image Analysis section, the function of P(L/[L.sub.m]) could be used as a scaling function to study the self-similarity of the phase morphology during the solidification process by comparing the histograms of P(L/[L.sub.m]) at different solidification temperatures. Figure 9b shows the plots of P(L/[L.sub.m]) against L/[L.sub.m] at different solidification temperatures. The figure reflected that the curves obtained at different solidification temperatures fell on a master curve. This demonstrated that the phase morphology of the PP/PEOc (70/30) blend systems were self-similar during the solidification process under natural cooling, especially before the crystallization temperature. Therefore, the [D.sub.f] can be calculated after determining the self-similarity of the phase morphologies at different solidification temperatures. The relation between [D.sub.f] and solidification temperatures is shown in Fig. 10. The change of [D.sub.f] can be divided into two different stages. Before the crystallization temperature 116[degrees]C, [D.sub.f] is basically a constant as temperature decreased. This means that the percentage of different sizes in the dimension-less region was invariable. On the other hand, [D.sub.f] decreased dramatically after the crystallization temperature. This pointed out that not only did the particle sizes become larger but also the distribution of L in the dimensionless region was less uniform.

[FIGURE 9 OMITTED]

[FIGURE 10 OMITTED]

In-line Analysis

It was well known that the in situ sampling only obtained the phase morphology at different temperature sites, and it was difficult to observe the variations of phase morphology continuously within the period of time. In our preceding research, in-line BSALS experiments were used to investigate the phase behavior of PP/PEOc blends, and good results were obtained (16), (25). Because its main advantage was that the measurement didn't damage or interrupt the samples and the variations of phase behavior were observed continuously within the period of time by using this equipment. So, we utilized this equipment to investigate the phase behavior during solidification process under the natural cooling condition.

The blend was mixed in the internal mixer at the rotor speed of 40 rpm for 10 min at 200[degrees]C. Then, the mixer was stopped to make the blend under the natural cooling conditions without any shear. Meanwhile, the scattering information from the mixing specimen was tracked by online apparatus and gripped by computer. Figures 11 and 12 show the evolution of heterogeneity distance lc and integral invariant Q of the dispersed PEOc particles as a function of solidification temperature and time during solidification process, respectively. The results showed that the lc (domain sizes) and Q (dispersible uniformity) both had little fluctuant change and moved around a certain value before the crystallization temperature (about 116[degrees]C, point C in the Figs. 11 and 12). The results indicated that the phase morphology changed very little during solidification process, although the samples would pass though the melting temperature of PP (about 168[degrees]C, point B in the Figs. 11 and 12). When the temperature decreased to the crystallization temperature, the domain sizes increased dramatically and its distribution width also tended to increase. The values of lc and Q were almost constant when the temperature reached 105[degrees]C (point D in the Figs. 11 and 12).

[FIGURE 11 OMITTED]

[FIGURE 12 OMITTED]

Yao investigated the phase diagram of this system and pointed out that the phase diagram of the PP/PEOc blend system should obey the behavior of upper critical solution temperature (26). According to Yao's study, PP/PEOc (70/30) blend was in the state of phase separation below 200[degrees]C, so there was no domain coalescence induced by phase separation. When the temperature decreased to the crystallization temperature, the domain sizes increased dramatically and its distribution width also tended to increase. This attributed to the domain coalescence because the cooling became slower as the temperature decreased, allowing for plenty of time for coalescence.

The results obtained from BSALS are basically in agreement with those obtained from SEM, but the change range of domain size was very large after the crystallization of PP compared with the results obtained from SEM. This might be caused by the crystallization of PP, which influenced the signal acquisition of light scattering. The large increase of these structure parameters might attribute to domain coalescence and crystallization of PP.

CONCLUSIONS

In this article, phase morphology and evolution of PP/ PEOc blend during solidification process was investigated based on the image analysis of SEM micrographs. Furthermore, in-line BSALS was used to investigate the solidification process of PP/PEOc blends under the natural cooling conditions in real time after mixing. The results indicated that the domain size and distribution had no significant changes before the crystallization temperature, although the samples would pass though the melting temperature of PP (about 168[degrees]C). When the temperature decreased to the crystallization temperature (about 116[degrees]C). the domain sizes increased dramatically and its distribution width also tended to increase. The results obtained from BSALS were basically in agreement with those obtained from SEM, which means BSALS was valid to study the phase morphology and evolution during solidification process of polymer blend.

REFERENCES

(1.) T. McNally, P. McShane, G.M. Nally, W.R. Murphy. M. Cook, and A. Miller, Polymer, 43. 3785 (2002).

(2.) K. Jayanarayanan, S. Thomas, and K. Joseph. Compos A, 39, 164 (2008).

(3.) P.T. Hietaoja, R.M. Holsti-Miettinen, J.V. Seppala, and O.T. Ikkala, J. Appl. Polym. Sci., 54, 1613 (1994).

(4.) S. Wu, Polym. Eng. Sci., 27, 335 (1987).

(5.) C.C. Han. Multiphase Flow in Polymer Processing. Academic Press. New York (1981).

(6.) L.A. Utracki and Z.H. Shi. Polym. Eng. Sci., 32. 1824 (1992).

(7.) I. Manas-Zloczower, A. Nir, and Z. Tadmor, Rubber Chem. Technol., 57, 583 (1984).

(8.) Y.G. Jeong. T. Hashida, S.L. Hsu, and C.W. Paul, Macro-molecules. 38, 2889 (2005).

(9.) Y.G. Jeong, T. Hashida, G.L. Wu, S.L. Hsu, and C.W. Paul, Macromolecules, 39, 274 (2006).

(10.) E.J. Kaiser. J.J. Mcgrath, and A. Benard, J. Appl. Polym. Sci., 76, 1516 (2000).

(11.) T. Hashida, Y.G. Jeong, Y. Hua, S.L. Hsu, and C.W. Paul, Macromolecules, 38, 2876 (2005).

(12.) J.P. Liu and B.J. Jungnickel, J. Polym. Sci. B: Polym. Phys., 44, 338 (2006).

(13.) M.L. Di Lorenzo, Prog. Polym. Sci., 28, 663 (2003).

(14.) R.D. Deanin and A.M. Crugnola, Advances in Chemistry Series, Vol. 154, American Chemical Society, Washington 284 (1976).

(15.) J.M. Zhou and J. Sheng, Polymer, 38, 3727 (1997).

(16.) L. Zhu, X.H. Xu, F.J. Wang, N. Song, and J. Sheng, Mater. Sci. Eng. A, 494, 449 (2008).

(17.) A. Guinier and G. Fournet, Small Angle Scattering of X-Ray, Wiley, New York (1955).

(18.) H. Takayasu, Fractals in the Physical Sciences. Manchester University Press, Manchester (1989).

(19.) B.B. Mandelbrot, The Fractal Geometry of Nature, Freeman, New York (1983).

(20.) J.Z. Zhang, Fractal, Tsinghua University Press, Beijing (1995).

(21.) P. Debye and A.M. Bueche, J. Appl. Phys., 20, 518 (1949).

(22.) P. Debye, H.R. Anderson, and H. Brumberger, J. Appl. Phys., 28. 679 (1957).

(23.) J.K. Lee and CD. Han, Polymer, 40, 6277 (1999).

(24.) J.K. Lee and CD. Han, Polymer, 41, 1799 (2000).

(25.) X.L. Yan, X.H. Xu, and L. Zhu, J. Mater. Sci., 42. 8645 (2007).

(26.) Y.H. Yao, X. Dong, C.G. Zhang, F.S. Zou, C.C. Han, Polymer, 51, 3225 (2010).

Correspondence to: Xinhua Xu; e-mail: xhxu@tju.edu.cn

Contract grant sponsor: National Natural Science Foundation of China: contract grant number: 20490220.

Published online in Wiley Online Library (wileyonlinelibrary.com).

[C] 2011 Society of Plastics Engineers

Lin Zhu, (1), (2) Na Song, (1) Xinhua Xu (1)

(1) School of Materials Science and Engineering, Tianjin University, Tianjin 300072, People's Republic of China

(2) School of Materials Science and Engineering, Jiangsu University, Zhenjiang 212013, People's Republic of China

DOI 10.1002/pen.21821

Printer friendly Cite/link Email Feedback | |

Author: | Zhu, Lin; Song, Na; Xu, Xinhua |
---|---|

Publication: | Polymer Engineering and Science |

Article Type: | Report |

Geographic Code: | 9CHIN |

Date: | Mar 1, 2011 |

Words: | 4457 |

Previous Article: | Uniform shell patterning using rubber-assisted hot embossing process. II. process analysis. |

Next Article: | Electrical properties of Polyaniline-manganese chloride composites. |

Topics: |