Abstract
Nanocrystalline thermoelectric materials based on Si have long been of interest because Si is earthabundant, inexpensive, and nontoxic. However, a poor understanding of phonon grain boundary scattering and its effect on thermal conductivity has impeded efforts to improve the thermoelectric figure of merit. Here, we report an abinitio based computational study of thermal transport in nanocrystalline Sibased materials using a variancereduced Monte Carlo method with the full phonon dispersion and intrinsic lifetimes from firstprinciples as input. By fitting the transmission profile of grain boundaries, we obtain excellent agreement with experimental thermal conductivity of nanocrystalline Si [Wang et al. Nano Letters 11, 2206 (2011)]. Based on these calculations, we examine phonon transport in nanocrystalline SiGe alloys with abinitio electronphonon scattering rates. Our calculations show that low energy phonons still transport substantial amounts of heat in these materials, despite scattering by electronphonon interactions, due to the high transmission of phonons at grain boundaries, and thus improvements in ZT are still possible by disrupting these modes. This work demonstrates the important insights into phonon transport that can be obtained using abinitio based Monte Carlo simulations in complex nanostructured materials.
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Introduction
Thermoelectric (TE) materials are of interest for energy applications such as waste heat recovery and quiet, reliable refrigeration^{1,2,3,4}. The performance of TE devices is determined by the figure of merit , where S is the Seebeck coefficient, σ is the electrical conductivity, T is the absolute temperature, and κ_{e} and κ_{ph} are the electronic and lattice thermal conductivity^{5}. Substantial increases in ZT have been achieved in recent years by reducing lattice thermal conductivity in nanostructured materials while minimizing decreases in electronic properties^{4,6}.
Nanocrystalline materials, or polycrystals with nanoscale grain size, in particular have been demonstrated as efficient TE materials in bulk form^{4,6,7,8,9,10,11,12}. The thermoelectric efficiency is improved by the reduction of lattice thermal conductivity due to phonon grain boundary scattering when the phonon mean free paths are comparable to the grain size. Of the many materials that have been processed into nanocrystalline form, nanocrystalline Si is a promising candidate as it is inexpensive and not toxic^{7}. However, the peak ZT achieved in nanostructured silicon is around 0.7, still lower than those of champion thermoelectric materials primarily because of its high thermal conductivity.
Further improving ZT requires a detailed understanding of phonon grain boundary scattering and its effect relative to other scattering mechanisms such as phononphonon and electronphonon scattering. Both computation and experiment have been used extensively to investigate phonon transmission across grain boundaries. Many studies using atomic calculations reported that the transmission of phonons across the grain boundaries depends on the phonon frequency and sharply decreases as the frequency increases^{13,14,15,16}. Experimental work by Wang et al. reported a T^{2} temperature dependent thermal conductivity of nanocrystalline silicon, which further confirms this trend^{17}. Recently, the transmission coefficients between Si and Al were reported by Hua et al. using measurements from the timedomain thermoreflectance method and abinitio phonon transport modeling^{18}. They found that the transmission coefficients were close to unity for low frequency phonons and decreased to a small value above 3–4 THz.
The strong dependence of transmission coefficients on phonon frequency has an important effect on which phonons conduct heat in nanocrystals and hence on strategies to further reduce the thermal conductivity of nanocrystalline silicon. Quantitatively determining which phonons carry heat in nanocrystals requires simulating phonon transport in the structure of a nanocrystalline solid. Several works used a Monte Carlo (MC) algorithm to examine thermal transport in nanostructures including nanocrystals^{19,20,21,22,23}. However, most of these simulations assumed a grey model for the phonon dispersion due to computational cost excepting the work of Hao et al.^{19}. Recent advances in variancereduced Monte Carlo (VRMC) simulations have resulted in significant computational speedups on the order of 10^{9} compared with traditional MC simulation^{24,25}. These algorithms have been used to study thermal transport in nanomeshes and complex 3D cellular structures^{26,27}. Hua and Minnich used the method to study phonon transport in Si and SiGe nanocrystals assuming an isotropic phonon dispersion and semiempirical relaxation times. They reported that low frequency phonons make an unexpectedly large contribution to thermal conductivity in nanocrystals, compared to the prediction of a gray model, due to the high transmission of low frequency phonon modes^{23}. However, this work employed a simplified, isotropic dispersion that differs from the actual Si dispersion.
Including the full phonon dispersion with the correct phonon density of states is essential to inferring key properties such as phonon transmission coefficients at the grain boundaries from experimental data^{28,29}. Several works have reported MC methods that incorporate a full phonon dispersion^{30,31}, however, these methods are computationally expensive as they do not employ the efficient algorithm of Peraud et al.^{24,25}. Vermeersch et al. developed a VRMC method using abinitio full phonon dispersion to study crossplane thermal transport in thin films^{32}. As yet, no abinitio based study of phonon transport in nanocrystalline solids needed to identify strategies to further decrease thermal conductivity has been reported.
Here, we examine thermal transport in nanocrystalline Si and SiGe using VRMC simulations based on the full phonon dispersion and intrinsic lifetimes obtained from firstprinciples. By fitting the transmission coefficient profiles versus phonon frequency, we achieve excellent agreement with experimental thermal conductivity of nanocrystalline Si^{17}. The trend of the fitted transmission coefficients is consistent with that obtained experimentally by Hua et al.^{18}. Based on these calculations, we find that low energy phonons with mean free path longer than grain size transport substantial amounts of heat in nanocrystalline SiGe, even with consideration of electronphonon scattering, due to their nearunity transmission across grain boundaries. In SiGe with 2% Ge concentration, these phonons contribute over a third of the thermal conductivity. This observation suggests that gains in ZT are possible by suppressing the contribution of low energy phonons.
Method
Our geometry for nanocrystalline Si is a 3D cubic simulation box oriented along the coordinates of Brillouin zone as shown in Fig. 1(a). A temperature gradient is applied in the x direction to set up a heat flux. We apply a periodic heat flux boundary condition along the x direction^{19} and a specular boundary condition in the y and z directions so that the computational domain represents a unit cell that is repeated in all directions. Three grain boundaries (blue, green and pink plane in Fig. 1(a)) perpendicular to the x, y, z directions bisect the cubic simulation box. Because of symmetry boundary conditions, the size of grain boundaries is the same as that of the cubic simulation domain.
Phonon transport in this domain is described by the Boltzmann transport equation under the relaxation time approximation. The deviational, energy based Boltzmann transport equation is given by (ref. 24)
where is the desired deviational distribution function, v_{k ,p} is the group velocity, ω_{k},_{p} is the angular frequency, is the BoseEinstein distribution at the control temperature T_{eq}, and τ( k , p, T) is the relaxation time. Here k and p denote the wavevector and polarization of a phonon mode, respectively.
We solve this equation using the linearized version of Peraud and Hadjiconstantinou’s algorithm with a spatially variable equilibrium temperature^{25}. In this work, we generalize this algorithm to accept the full phonon dispersion and intrinsic lifetimes for natural bulk Si provided by Lindsay et al. We used 16,384 k points over the entire Brillouin zone, with six polarizations at each k . The number of deviational particles is set as N_{ph} = 8 · 10^{6} for each simulation. Following the algorithm of ref. 25, these particles are advected through the domain and scattered by internal scattering mechanisms as well as reflected and transmitted by grain boundaries. The net heat flux is obtained by considering the initial and final position of each particle over a defined period of time, from which thermal conductivity is obtained with knowledge of the imposed temperature differential and size of the simulation box. The final reported thermal conductivity is an average of the values obtained from ten simulations. The effective mean free path for each phonon mode in the domain including grain boundary scattering is calculated by averaging the total simulation time over the total number of scattering events that occurred for a phonon mode.
We briefly describe the modifications to the original algorithm^{25} that are necessary to incorporate the full phonon dispersion. In the original algorithm, the isofrequency surface of Brillouin zone is a sphere, and so when a new phonon is drawn after scattering, its traveling direction is chosen isotropically. In the case of a full dispersion with arbitrary anisotropies, this method is no longer appropriate.
The two classes of scattering events we must consider are inelastic scattering, when the phonon frequency is redrawn after internal scattering, or elastic scattering, when the phonon frequency is maintained after grain boundary scattering. To incorporate these scattering event classes, we histogram all of the phonon modes by frequency. As in the original algorithm, if internal scattering occurs we first select the phonon frequency using the inverse transform method^{24}. Subsequently, to determine the phonon travelling direction we choose the wave vector k and polarization p by randomly choosing a mode within the selected frequency bin with probability proportional to the weight of the mode w_{k}. In the case of elastic grain boundary scattering, the frequency is maintained and we only perform the latter procedure. Note that for elastic grain boundary scattering, the probability to choose a mode in the frequency bin is weighted by the mode’s component of group velocity parallel to the boundary normal. In the original algorithm, this weight corresponds to a cosine term. The advection and scattering processes in the MC simulation are the same as that in the original algorithm. The only other change is that the volumetric source corresponding to a steady state linear temperature profile follows the distribution, rather than a distribution with a cosine term as the isotropic case.
Phonon transmission and reflection at grain boundaries are the key processes in the nanocrystalline solid that affect thermal conductivity, and there are two parameters that describe these processes. First, the transmission coefficient describes the probability that the phonon transmits through a grain boundary. Second, the specularity parameter describes the probability that the phonon maintains its transverse momentum on scattering. Grain boundary scattering is implemented in the following procedure. Consider that a phonon mode incident on a grain boundary is specified to have a transmission coefficient t_{gb} and a specularity parameter P. When the phonon encounters the grain boundary, a random number is drawn. If the random number is less than the transmission coefficient, the phonon transmits the grain boundary, otherwise the phonon is reflected. Then, a second random number is drawn. If the random number is less than the specularity value, the phonon is specularly transmitted or reflected, otherwise it is diffusely scattered in the forward or backward direction for transmission or reflection, respectively.
The essential inputs to the MC simulation are the transmission coefficients and specularity parameters of phonon modes as a function of their frequencies or wavelengths. In this work, we use spectral profiles of these parameters following the trends of recent experimental measurements. The transmission coefficients are obtained by fitting MC simulation results to the experimental thermal conductivity of nanocrystalline Si^{17}. The general trend of transmission coefficients verus frequency follows that of measurements by Hua et al.^{18}, who found that transmission coefficients between Al and Si interface are near unity for low frequency phonon and decrease rapidly to near zero with increasing frequency^{18}. For the specularity parameter, experiments by Ravichandran et al. suggest that the specularity at rough silicon boundaries can be specified by Ziman’s specularity, , with η ~ 0.12 − 0.15 nm^{33}. Here is phonon wavelength and η is taken to be an adjustable parameter with a value in the range specified above.
Figure 1 (b) shows the temperature profile ΔT for 550 nm grain size sample at 300 K, which are recorded during the simulation. The absolute temperature (T) can be calculated by T = 300 K + ΔT. The temperature difference across the simulation box is 0.1 K. For this calculation, the domain consisted of the standard cubic simulation box with three grain boundaries, and the transmission coefficients and the specularity are the same as that used in nanocrystalline Si with 550 nm grain size at 300 K in Fig. 2(c).
Results
We first examine how the different grain boundary orientations affect thermal conductivity by considering domains with a single grain boundary. We took the grain size to be 550 nm and calculated the thermal conductivity for various constant transmission and specularity parameters with only the pink grain boundary, which is parallel to heat flux, or with only the blue grain boundary, which is perpendicular to the heat flux.
Figure 1(c) shows the thermal conductivity of a domain with a parallel grain boundary as a function of specularity calculated using VRMC simulation and a theoretical result based on FuchsSondheimer theory^{34}. The thermal conductivity increases with an increase of specularity, however, the thermal conductivity is independent of transmission. Therefore, only the specularity of parallel grain boundaries affects the thermal conductivity. This observation can be understood from symmetry of the grain boundary with respect to the imposed thermal gradient.
Figure 1(d) shows the thermal conductivity of a domain with a perpendicular grain boundary as a function of transmission coefficient. Overall, the specularity has little effect, particularly when the transmission is less than 0.8. The bulk thermal conductivity is achieved when the transmission coefficient and specularity are both equal to unity, as expected. Therefore, for perpendicular grain boundaries, the transmission coefficients are the main factor that modify the thermal conductivity. Comparing the variation in thermal conductivity in Fig. 1(c) and (d) with changes in transmission coefficients and specularity parameters, we found that the perpendicular grain boundaries have a larger effect on the thermal conductivity than the parallel grain boundaries.
With this understanding, we now examine the experimental thermal conductivity measurements of Wang et al. on nanocrystalline silicon^{17}. In their work, Wang et al. observed that the thermal conductivity varies as T^{2} at low temperature, indicating the transmission coefficients cannot be a constant for all modes as such a profile would lead to a T^{3} trend. Our code, with abinitio dispersion, allows us to determine which transmission coefficients and specularity parameters are capable of explaining the data.
We fit these data using trends for transmission coefficients and specularity parameters obtained from prior experimental measurement as described in Method section. Figure 2(a) and (b) shows the fitted transmission coefficients for the 550 nm grain size sample versus frequency for longitudinal and transverse acoustic phonons. The data of Hua et al.^{18} are plotted for comparison and agree reasonably well with the fitted profile. The resulting thermal conductivity versus temperature for the 550 nm grain size sample is given in Fig. 2(c). The fitted specularity parameter yields η = 0.125 nm, which is consistent with the measurement by Ravichandran et al.^{33}. The selfconsistency of these results provide evidence that the transmission coefficient and specularity parameters obtained in refs 18 and 33 are correctly describing the phonon interactions with grain boundaries.
Using the same transmission coefficients we fit the specularity of the nanocrystalline Si with grain sizes 144 nm and 76 nm at 300 K, yielding η = 0.26 nm and η = 0.11 nm for grain sizes 144 nm and 76 nm, respectively. As shown in Fig. 2(c), the thermal conductivities of the MC simulation are consistent with the measurements when the temperature is larger than 100 K. The thermal conductivity of MC simulation shows some discrepancy with the measurements as the temperature decreases but the trend is not dissimilar from the experiment. To facilitate subsequent comparisons between different grain sizes, we use the same transmission coefficients and specularity obtained from the 550 nm sample for all further calculations.
The fitted transmission coefficients and specularity parameters thus indicate that low frequency phonons experience high transmission at frequencies less than 2–3 THz while high frequency phonons above around 4 THz are largely reflected. We now examine the impact of this strong frequency dependence on the phonon modes responsible for heat conduction in the nanocrystals.
We present the thermal conductivity as a function of frequency in Fig. 3(a) and (b). Figure 3(a) compares the accumulation of thermal conductivity of nanocrystalline Si with the 550 nm grain size at 30 K, 100 K, and 300 K. At 300 K, phonons with frequency less than 4 THz contribute 60% in nanocrystalline Si with 550 nm grain size, nearly the same amount as in bulk Si, 69%. In addition, the contribution of low frequency phonons increases as the temperature decreases in nanocrystalline Si. Figure 3(b) shows that similar trends are observed for the 20 nm grain size, while the contribution from low frequency phonons is decreased in nanocrystalline Si with smaller grain sizes. The thermal conductivity of nanocrystalline Si with 20 nm grain size decreases to 8.4 W/mK.
Figure 3(c) shows the mean free path for each phonon mode versus frequency. The longest mean free path in nanocrystalline Si with 550 nm grain size is around 33% of that in bulk Si, approximately 11 microns, but is still much larger than the grain size. The mean free path of high frequency phonons is not strongly affected by grain boundary scattering, as expected, because of the high phononphonon scattering rate for high frequency phonons. The normalized accumulation of thermal conductivity versus mean free path, Fig. 3(d), demonstrates that the contribution of phonons with mean free path larger than grain size is 26.7% for 550 nm, respectively, at 300 K. In addition, the contribution from these phonons increases as the temperature decreases.
It is interesting to note the outsize role of low energy phonons to thermal conductivity. In bulk Si at 300 K, the fraction of the number of phonon modes with mean free path larger than 550 nm is 0.73% yet they contribute almost 50% to the thermal conductivity. This outsize role remains in nanocrystalline Si: we find that the fraction of the number of phonon modes with larger mean free path than the grain size is 0.38% and 1.92% for 550 nm and 20 nm grain sizes, respectively, yet these modes contribute nearly a third to the total thermal conductivity. Therefore, very small portion of phonons is responsible for a major fraction of the thermal conductivity, suggesting that disrupting these modes will lead to large reductions in thermal conductivity.
We now use our simulations to examine phonon transport in nanocrystalline SiGe thermoelectrics. These materials have two additional scattering mechanisms, point defect scattering and electronphonon scattering, compared to nanocrystalline Si. We first account for mass defect scattering rate using the Tamura formula^{35} and add it to the intrinsic phononphonon scattering rate by Matthiessen’s rule neglecting the coupling between phononphonon scatterings and defect scatterings^{36}. For SiGe with 2% Ge concentration, we obtain a bulk thermal conductivity of around 34.3 W/mK at 300 K, a value that is consistent with previous work^{37}. Taking the transmission coefficients and specularity parameters to be the same as those of nanocrystalline Si with 550 nm grain size, we find that point defect scattering results in a decrease in thermal conductivity from 34.3 (bulk SiGe) to 18.8 and 4.6 W/mK for 550 nm and 20 nm grain sizes, respectively. The origin of this decrease can be identified from Fig. 4(a), which shows that point defect scattering strongly scatters high frequency phonons but has only minimal effect of phonons with frequency less than 2 THz. In nanocrystalline SiGe, the longest mean free path is approximately 11 microns and 573 nm for 550 nm and 20 nm grain sizes, respectively. Thus, the longest mean free path is decreased almost one order compared with that in bulk SiGe but is again much larger than the grain sizes.
Second, electronphonon interactions can significantly decrease the thermal conductivity of Si due largely to scattering of low frequency phonons^{38}. We obtained abinitio electronphonon scattering rates for both ptype and ntype doping with a concentration of 10^{20} cm^{−3} from Liao et al.^{38}. This scattering mechanism results in an additional reduction of thermal conductivity of ptype nanocrystalline SiGe. The decrease is more severe for the 550 nm grain size, from 18.8 to 13.2 W/mK, than for the 20 nm grain size, from 4.6 to 4.4 W/mK. This observation can be explained from Fig. 4(b), which compares the mean free path ratio, relative to those of the corresponding undoped materials, of each phonon for ptype bulk SiGe and ptype nanocrystalline SiGe with 550 nm and 20 nm grain sizes. The figure shows that low frequency phonons are strongly scattered in the 550 nm sample while they are minimally affected in the 20 nm sample, indicating that the electronphonon scattering rate is small compared to the grain boundary scattering rate in the latter case. Therefore, the relative effect of electronphonon interactions is smaller in nanocrystalline solids with smaller grains. Similar trends are observed for ntype doping.
The effect of these scattering mechanisms on thermal conductivity is presented in Fig. 4(c) and (d), which shows the normalized accumulation of thermal conductivity of undoped and ptype bulk and nanocrystalline Si and SiGe versus frequency. As the high frequency phonons are strongly scattered by Ge point defects, the relative contribution of low frequency phonons in bulk SiGe to the thermal conductivity increases compared to that of bulk Si. Due to the electronphonon scattering for low frequency phonons, ptype bulk Si and SiGe has a smaller contribution from low frequency phonons compared with their undoped counterparts. A similar trend is shown in Fig. 4(d) for nanocrystalline Si with 550 nm grain size. The contribution from low frequency phonon remains almost unchanged in ptype nanocrystalline SiGe with 20 nm grain size compared with that of undoped material, indicating that grain boundary scattering dominates in smaller grain size nanocrystalline SiGe.
Finally, we show the normalized thermal conductivity accumulation for undoped and ptype materials in Fig. 5(a) and (b), respectively. Compared with bulk Si and nanocrystalline SiGe, bulk SiGe has the largest contribution from phonons with large mean free path. Even in the doped samples with electronphonon interactions, the fraction of the number of phonon modes with mean free path larger than the grain size is 0.11% and 0.82% for 550 nm and 20 nm grain sizes, respectively, yet in both cases these low energy modes contribute nearly a third of the total thermal conductivity. We reach a similar conclusion for ntype doping. Therefore, our abinitio based analysis shows that potential for improving ZT remains by reducing lattice thermal conductivity in nanocrystalline SiGe alloys.
Conclusion
We studied thermal transport in nanocrystalline Si with an abinitio based Monte Carlo method with the full phonon dispersion and intrinsic lifetimes obtained from firstprinciples. We find that the high transmission of low frequency phonons across the grain boundary leads to a small fraction of the modes contributing substantially to the thermal conductivity of nanocrystalline Si. This conclusion remains true even in nanocrystalline SiGe despite pointdefect and abinitio electronphonon scattering, indicating that the key to further reducing lattice thermal conductivity is to disrupt the transport of low energy phonons. Our study also shows the powerful insights that can be obtained with abinitio based phonon transport modeling of complex nanostructures with a minimum of adjustable parameters.
Additional Information
How to cite this article: Yang, L. and Minnich, A. J. Thermal transport in nanocrystalline Si and SiGe by ab initio based Monte Carlo simulation. Sci. Rep. 7, 44254; doi: 10.1038/srep44254 (2017).
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02 May 2017
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02 May 2017
Scientific Reports 7: Article number: 44254; published online: 14 March 2017; updated: 02 May 2017 This Article contains errors in Figures 3, 4 and 5 where the graphs were labelled incorrectly. The correct Figures 3, 4 and 5 appear below as Figures 1, 2 and 3 respectively.
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Acknowledgements
The authors thank Lucas Lindsay for providing the Si dispersion and phononphonon lifetimes, and Bolin Liao and Jiawei Zhou for providing electronphonon lifetimes. This work was supported by the DARPA MATRIX program under Award Number HR00111520039.
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Lina Yang and Austin J. Minnich wrote this manuscript. All authors reviewed the manuscript.
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Yang, L., Minnich, A. Thermal transport in nanocrystalline Si and SiGe by ab initio based Monte Carlo simulation. Sci Rep 7, 44254 (2017). https://doi.org/10.1038/srep44254
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