Effect of Thermal Annealing on the Electrical Conductivity of Copper

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Effect of Thermal Annealing on the Electrical Conductivity of Copper−Tin Polymer Composites Qing Yang,† Megan Hoarfrost Beers,*,‡ Vishrut Mehta,‡ Ting Gao,‡ and Dilworth Parkinson*,† †

Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States Tyco Electronics Corporation, TE Connectivity Ltd., Menlo Park, California 94025, United States

‡

S Supporting Information *

ABSTRACT: Polyvinylidene fluoride (PVDF) copolymer conductive composites containing 40 vol % copper (Cu) and tin (Sn) fillers are prepared by injection molding. Postmolding thermal annealing is found to increase the electrical conductivity of the composites by an order of magnitude. The volume ratio between Cu and Sn is found to have a significant effect on filler distribution but a weaker effect on electrical conductivity compared to the annealing conditions. Synchrotron X-ray tomography is used to visualize and quantitatively analyze the morphology and distribution of the filler particles, indicating that higher conductivity can be attributed to better dispersion of the lowmelting-point Sn filler, which provides better interparticle contact in the Cu network.

KEYWORDS: polymer composite, conductive composite, injection molding, electrical conductivity, X-ray tomography

1. INTRODUCTION Conductive polymer composite materials offer desirable electrical conductivity while maintaining some typical advantages of polymers such as mechanical flexibility, good manufacturability, and chemical resistance. Over the years they have been used in a variety of applications such as electromagnetic shielding, electrostatic protection, batteries, electrodes, sensors, and others. The electrical conductivity of a composite relies on the formation of continuous conductive pathways (forming a network) above a critical filler content, which can be described by percolation theory.1−3 Higher conductivity can be achieved by increasing the volume fraction of the filler. However, higher filler loading usually increases melt viscosity and compromises processability and mechanical properties. A major research goal in academia and industry has been to find optimal combinations of filler materials, compositions, and processing parameters to optimize the electrical conductivity while maintaining processability. One approach is to use low-melting-point metal fillers that have low viscosity at the processing temperature, thus facilitating easy processability. For example, a number of researchers have studied polypropylene composites filled with low-melting-point Sn−Pb alloys, taking advantage of their low melt viscosity at the molding temperature.4−6 However, the electrical conductivities of the resulting composites are about 4 orders of magnitude lower than the intrinsic conductivity of Sn−Pb, primarily because the Sn−Pb ends up poorly dispersed in the polymer matrix, making it difficult to form a conductive network. © XXXX American Chemical Society

Electrical conductivity can be further increased while maintaining good processability by combining solid conductive fillers with the low-melting metal alloys, such as Bi−Sn,7 Sn− Zn−Cd,8,9 and Zn−Sn.10 In polyamides filled with Cu fibers and a low-melting Zn−Sn alloy, Michaeli et al.10 observed conductivities only 2 orders of magnitude lower than the intrinsic conductivity of Cu, and more than 2 orders of magnitude higher than analogous composites filled with Cu fibers only. The enhancement in conductivity can be attributed to a “welding” effect of the molten alloy introducing more contact between adjacent metal fillers and resulting in more homogeneity of the composites. In addition to compositional variables, processing conditions such as the mixing temperature and duration,11 the shear energy applied during mixing, the molding temperature and pressures,4,10,12,13 the mold design, and any thermal treatments after molding can have significant effects on the electrical and mechanical properties of conductive composites. These effects often result from differences in filler particle distribution and alignment (if they are anisotropic in shape), which are strongly influenced by the processing conditions. In addition, high amounts of shear during processing can physically break up filler particles resulting in new particles with different shapes and sizes. Especially for filler materials that melt and flow at the processing temperatures, final filler distribution is also dependReceived: October 31, 2016 Accepted: December 9, 2016 Published: December 9, 2016 A

DOI: 10.1021/acsami.6b13956 ACS Appl. Mater. Interfaces XXXX, XXX, XXX−XXX

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ACS Applied Materials & Interfaces ent on the enthalpic favorability of interaction with the polymer matrix material at the processing temperatures. Due to its high electrical conductivity, desirable mechanical properties, and relatively low cost, Cu has been used as a conductive filler in a variety of polymer matrices.12,14−22 In this study, dendritic-shaped Cu powder is used due to its low packing factor and therefore low percolation threshold for electrical conductivity in polymer composites.23 Sn is used as a low-melting filler because it has decent conductivity and its melting point is within the molding temperature window. Polyvinylidene fluoride (PVDF) copolymer is used as the resin matrix due to its compatibility with high filler loadings. Furthermore, PVDF has a favorable crystalline structure: Conductive particles tend to reside in the amorphous regions, and the excluded volume effect24 of the crystalline regions leads to higher conductivity for a given particle loading. Since PVDF is hydrophobic, achieving favorable distribution of Cu/Sn relies more strongly on processing conditions compared to favorable molecular interactions, thus highlighting the importance of studying the effect of processing conditions on the distribution of Cu/Sn in PVDF. In this work, the effects of filler composition and thermal annealing on filler distribution and electrical conductivity are investigated. The information presented provides a deeper understanding of conductive composite materials that contain a combination of low- and high-melting metals as well as provides insight into how to process them in order to best utilize them for applications. Computed microtomography has been widely used in materials, biomedical, and geological sciences to visualize microstructures and model transport properties.25−30 While many studies on conductive polymer composites provide microscopic information on the fillers, the images are mostly of two-dimensional cross sections. In this study, X-ray tomography is used to investigate metal filled polymer composites and provide not only direct 3D visualization of the filler distribution and morphology but also a voxel-based compositional map at submicrometer resolution. The quantitative microstructural information is then related to the experimentally measured electrical conductivity and used to explain the dependence of the electrical conductivity on the filler composition and thermal annealing.

Figure 1. (a) SEM image of dendritic shaped Cu particles. (b) Injection-molded stair-step bar of Cu−Sn filled composite. The dimensions of the bar are approximately 100 mm long by 10 mm wide, and the thicknesses of the three steps are 0.5, 1, and 1.5 mm. using Agilent Technologies digital multimeters and a lab-made test fixture. Four-point resistivity measurements were made along the length of the bar using flat-headed probes spaced 10 mm apart on the top and bottom of the sample, with a current of 0.1 A. Contact resistance between the probes and the sample was therefore eliminated by the separation of current and voltage electrodes. Conductivity values for the middle section were averaged from a total of 10 measurements at two different locations and reported in this study. Conductivities of the unmolded samples were measured on singlesection thin slices cut from chunks by a low-speed saw, resulting in similar thickness to the stair-step bars. Rectangular cuboid shaped samples of approximately 0.5 mm × 0.5 mm × 10 mm were cut from each composite sample using a low-speed diamond saw and were scanned at Beamline 8.3.2 of the Advanced Light Source at Lawrence Berkeley National Laboratory. For each sample, synchrotron X-ray beams with two different energies, 29 and 42 keV, were used to provide X-ray absorption information necessary to calculate the compositions of both metal fillers. A 50 μm LuAG scintillator was used to convert X-rays to visible light, which was imaged with a 10× lens and a PCO.Edge sCMOS camera. For each scan, the sample was continuously rotated 180° while a total of 2049 projection images were collected. The image resolution is 2560 × 2160 pixels, and the pixel size is 0.65 μm. As shown in the absorption spectra in Figure 2, Sn exhibits a sharp rise in X-ray absorption at 29.4 keV corresponding to its binding energy of the K shell.31 This provides a reverse contrast between Cu and Sn above and below this edge. Each sample was imaged at two different energies, one above and one below 29.4 keV, in order to utilize this contrast and calculate the Cu and Sn composition distributions. We chose 42 keV as the higher end energy (rather than an energy closer to 29.4 keV) to circumvent the significant level of absorption of Sn immediately above the edge and increase the signal-to-noise ratio. This was especially critical given the generally high X-ray absorption of the conductive composite samples, which is a result of their high metal particle content. 3D tomographic reconstructions were performed at the National Energy Research Scientific Computing Center (NERSC) using a model-based iteration reconstruction (MBIR) algorithm.32,33 The pixel intensities of the resulting 3D images correspond to the linear absorption coefficients (μ) of the samples. Assuming the absorption of the polymer matrix is negligible compared to the absorption of Sn and Cu, this dual-energy approach allows quantitative extraction of compositional information on a binary Cu−Sn system, using a

2. EXPERIMENTAL SECTION Composite samples with different metal loadings and processing conditions were prepared. Commercially available dendritic Cu and spherical Sn powders, roughly 26 and 10 μm in diameter, respectively, were compounded with polyvinylidene fluoride (PVDF) copolymer resin (Kynar Flex) at 200 °C. An SEM of the dendritic Cu powder is shown in Figure 1a. An amount of each composite material was preserved for control samples referred to as “unmolded samples”, while additional material was molded into stair-step-shaped bar samples (Figure 1b) using a Haake MiniJet table-top injection molding system. The compounds were thermally equilibrated to the barrel temperature, which was set above the melting point of the resin (225 °C) for 3 min before injection into the mold, which was set to a temperature of 200 °C. After holding under pressure for 15 s, the molds were quenched in room temperature water to cool the molded part. Two of the four molded samples were then annealed at 210 °C under vacuum for 8 h. The annealing temperature was selected to be below the melting points of Sn (232 °C) to prevent Sn from fully melting and separating out of the composite material during annealing. The annealed samples were then brought out of the oven and allowed to cool to room temperature before electrical measurements. Prior to X-ray microtomography experiments, the electrical conductivity was measured for all three sections of the bar samples B


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of Cu and Sn also affects conductivity, though to a lesser extent. Although the conductivity of pure Sn (∼9 × 104 S/cm) is lower than that of pure Cu (∼6 × 105 S/cm), higher Sn content (24 vol %) molded samples have 5−15% higher conductivities compared to those of corresponding samples with lower Sn content (16 vol %). This enhancement is hypothesized to be due to a “welding” effect of Sn facilitating contact between Cu particles and thus reducing interparticle contact resistance. In order to understand how the conductivities of the samples are related to their microstructures, voxel-based compositions of both Cu and Sn were calculated from X-ray tomography images based on eqs 1 and 2. The overall Cu and Sn compositions calculated by this method for a representative volume of 7.4 × 106 μm3 are within ±4 vol % of the nominal Cu and Sn compositions. Figure 3 shows 3D visualizations of the Cu and Sn distributions for microstructurally representative 130 × 130 × 260 μm3 volumes of each composite sample. In general, all six samples contain similar networks of Cu dendrites. As expected from the filler compositions, the volume ratios of Cu/Sn in Cu3Sn2 and Cu3Sn2-A are noticeably higher than those in Cu2Sn3 and Cu2Sn3-A. Both the unmolded and as-molded samples (Cu3Sn2-U, Cu2Sn3-U, Cu3Sn2, and Cu2Sn3) have isolated, Sn-rich particles throughout the structure suggesting that the compounding and molding conditions are not sufficient for deforming and distributing Sn agglomerates. Sn and PVDF do not have favorable enthalpic interactions. In fact, in the absence of the Cu filler, it is impossible to distribute Sn in PVDF via the melt compounding method utilized in this work. Rather, the Sn agglomerates into large pools and separates from the polymer. Therefore, it is critical to optimize the processing conditions for the PVDF/Cu/Sn composites to achieve optimal filler distribution. Annealing after compounding is one strategy to improve Sn distribution. After annealing the molded samples at 210 °C, the Sn particles become better dispersed, and the isolated rounded Sn particles largely disappear, as seen in Figure 3. This morphological change is likely induced by the longer diffusion time provided during the annealing procedure (at an elevated temperature where the viscosities of both Sn and PVDF are reduced) coupled with the absence of shear force that is experienced during molding, together allowing Sn to better migrate to and coat the Cu particles. Voxel-based bivariate histograms of the calculated Cu−Sn composition distributions were plotted using Matlab and are shown in Figure 4 to provide a quantitative description of the observations made based on the images in Figure 3. The composition distributions for the unmolded and as-molded samples all contain “tail” features in the top left regions of

Figure 2. X-ray absorption spectra of Cu (dotted line) and Sn (dashed line).31 The arrows indicate the elemental contrast between Cu and Sn at the two energies (29 and 42 keV) used to scan the samples in this study. modified version of the mass attenuation equation for absorbers with multiple constituent elements:34,35 μ(29 keV) = vCu·μ(Cu, 29 keV) + vSn · μ(Sn, 29 keV)

(1)

μ(42 keV) = vCu·μ(Cu, 42 keV) + vSn · μ(Sn, 42 keV)

(2)

where vCu and vSn are the volume fractions of Cn and Sn, respectively.

3. RESULTS AND DISCUSSION The six samples studied in this work are summarized in Table 1. The total loading of the metal fillers was kept constant at 40 vol %, much higher than the literature reported percolation threshold of 5−15 vol % for polymer composites filled with dendritic Cu.12,23 For commercial use, it is desirable for the conductivity to be homogeneous and stable, which is best accomplished by using a metal content well above the percolation threshold, hence the selection of 40 vol % filler loading. The Cu/Sn ratio was varied, and samples were studied before molding, after molding, and after postmolding thermal annealing. The electrical conductivities of all six samples are also summarized in Table 1. The unmolded samples have conductivities more than an order of magnitude lower than those of the molded samples, indicating the beneficial effect of molding on the microstructural conductive network. After being annealed at 210 °C, the conductivities of the molded samples improve by an order of magnitude. The volume ratio

Table 1. Electrical Conductivities of the Cu−Sn Polymer Composites under Different Metal Loadings and Processing Conditionsa sample ID

Cu/Sn volume ratio

barrel temperature (°C)

mold temperature (°C)

Cu3Sn2-U Cu3Sn2 Cu3Sn2-A Cu2Sn3-U Cu2Sn3 Cu2Sn3-A

3:2 3:2 3:2 2:3 2:3 2:3

unmolded 225 225 unmolded 225 225

unmolded 200 200 unmolded 200 200

thermal annealing in vacuum after molding

210 °C for 8 h

210 °C for 8 h

electrical conductivity (S/cm) 25.1 (4.05 (3.82 21.9 (4.55 (4.16

± ± ± ± ± ±

0.08 0.26) × 0.01) × 0.002 0.17) × 0.09) ×

102 103 102 103

a

The sample IDs that will be used throughout the text are listed alongside the processing conditions and electrical conductivities corresponding to each sample. C


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Figure 3. 3D volume renderings of all six composite samples in perspective views (a−f). The color map indicates the composition of Cu (increasing from dark red to bright yellow), and the grayscale map indicates the composition of Sn (increasing from dark to light gray). In order to visualize both fillers, the Cu volume is truncated by half to emphasize the difference in Sn particle distribution between the different samples.

Figure 4. Cu and Sn compositions of representative 300 × 300 × 300 voxel regions of interest in all six composite samples (a−f). The color map indicates increasing density of data from blue to red.

Figure 4a,b,d,e. This region corresponds to voxels with high Sn content and low Cu content, such as the voxels where the

isolated Sn-rich particles are observed in Figure 3a,b,d,e,. The composition distributions, therefore, provide a more quantitaD


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ACS Applied Materials & Interfaces tive confirmation of poor Sn dispersion throughout the filler network in these samples. After annealing at 210 °C for 8 h, the “tail” features in the high Sn content region are significantly diminished, as shown in Figure 4c,f. This indicates that the large isolated Sn particles have largely disappeared in favor of a more even distribution of Sn throughout the sample. Meanwhile, the number of voxels with high Cu and low Sn content is also somewhat reduced in the annealed samples. Since the melting temperature of Cu (>1000 °C) is much higher than the processing temperatures and the Cu particles remain intact during the molding and annealing processes, this also reflects better Sn distribution and coating in the annealed samples. The data points in the resulting histograms for the annealed samples are more concentrated in the area near the nominal Cu, Sn compositions ((0.24, 0.16) and (0.16, 0.24)) compared to the unmolded and as-molded samples. This is further indication that the Cu and Sn are more evenly dispersed in the sample and implies that the Sn provides a more consistent and intimate coating of the Cu particles. This microstructural information is consistent with the electrical conductivity measurements in that more uniform Sn coatings around Cu particles lead to reduced interparticle contact resistance and a better formed conductive network, and thus higher conductivities. As expected from the Cu/Sn ratios, Cu2Sn3 samples have more Sn-rich regions than their Cu3Sn2 counterparts. Even for the thermally annealed sample, there is a cluster of data points located in the high Sn/low Cu region of the histogram. However, despite the isolated Sn particles present in these samples that likely do not contribute significantly to increasing the conductivity and despite the higher overall ratio of Sn which has a lower intrinsic conductivity compared to Cu, the molded samples have even higher conductivities compared to those of the analogous Cu3Sn2 samples. The higher ratio of Sn to Cu in the Cu2Sn3 samples appears to have ensured that there is sufficient Sn to facilitate good interparticle contact between the Cu particles and a robust network formation, resulting in the high conductivities. It is worth noting that the semicrystalline nature of PVDF makes it a desirable polymer matrix for conductive composites, as conductive particles tend to reside in the amorphous regions, and the excluded volume effect of the crystalline regions leads to higher conductivity for a given particle loading.24 This begs the question of whether changes in PVDF % crystallinity before and after annealing could be contributing to the change in conductivity. However, differential scanning calorimetry (DSC) experiments showed that there was no change in the % crystallinity of PVDF before and after annealing (see the Supporting Information). Therefore, it is the redistribution of the Cu/Sn filler network observed by X-ray tomography that is more likely the main factor leading to the difference in conductivity. To take a closer look at the Sn distributions for the six samples in this study, Figure 5 shows just the voxel-based Sn distributions without respect to Cu composition. Both annealed samples (gray lines) have normal distributions around their respective nominal average Sn vol % (16 and 24%), while the unmolded and the as-molded samples (red and orange, respectively) have noticeably skewed distributions of Sn, with a greater number of voxels containing very low or very high Sn compositions. The high-composition ends of the histograms correspond to isolated large Sn particles that are not effectively coating Cu, while the low-composition ends (less than their

Figure 5. Histograms showing the distribution of Sn composition in (a) Cu3Sn2 and (b) Cu2Sn3 samples.

respective nominal average Sn vol %) of the histograms correspond to areas with little Sn. The peaks of the Sn composition curves for the unmolded and as-molded samples actually shift to the left, which reflects the fact that there is poor Sn distribution and thus very few voxels with the nominal average Sn vol % (16 and 24%). For Cu3Sn2 (solid lines), the skewness of the unmolded sample is almost identical to that of the as-molded sample, whereas for Cu2Sn3 (dashed lines), the skewness of the unmolded sample is even greater than that of the as-molded sample, which is expected due to the greater excess of Sn in Cu2Sn3 samples compared to that in the Cu3Sn2 samples. To further demonstrate the effect of thermal annealing on the morphology and distribution of Sn, SEM analysis was performed on Cu2Sn3 and Cu2Sn3-A, as shown in Figure 6. Before annealing, Sn is agglomerated into several large, isolated particles, while after annealing, Sn is better dispersed around small Cu particles. Some Sn aggregates still exist in the annealed sample, consistent with the Cu−Sn composition map constructed by X-ray tomography shown in Figure 4f.

4. CONCLUSION Cu−Sn−PVDF polymer composite samples with two different filler compositions were prepared by melt compounding and injection molding. Postmolding thermal annealing was found to have a significant effect on their electrical conductivities, leading to an order of magnitude increase in conductivity compared to the as-molded samples. The volume ratio between Cu and Sn significantly influences filler distribution but has only a weak effect on conductivity. 3D visualizations and composition distribution analysis from X-ray tomography reveal detailed information about the Cu/Sn network morphology and their E


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Figure 6. SEM images of the composite sample (a) Cu2Sn3 (before annealing) and (b) Cu2Sn3-A (after annealing). Sn has a higher contrast than Cu. Some pore-looking features in the images are where Cu or Sn particles were polished off during SEM sample preparation.

distributions. Dispersion of the low-melting filler, Sn, is an indicator of effective coating and interparticle contact of the dendritic Cu network and is the primary factor for the large observed differences in conductivity between the samples studied in this work.

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ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsami.6b13956. Differential scanning calorimetry (DSC) data showing that the % PVDF crystallinity is similar with and without Cu/Sn fillers, as well as before and after annealing (PDF)

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. ORCID

Dilworth Parkinson: 0000-0002-1817-0716 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank Michael Tang for running the MBIR tomographic reconstructions, Richard Lloyd for help with electrical measurements, as well as Min Zheng, Jaydip Das, Jerzy Gazda, Jim Toth, and Nick Pugliano for helpful discussions. This work was supported by Tyco Electronics Corporation, a TE Connectivity Ltd. company. The X-ray Tomography facility at the Advanced Light Source is supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

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REFERENCES

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Effect of Thermal Annealing on the Electrical Conductivity of Copper

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