1.Department of Chemical Engineering,Texas A&M University,College Station,USA;2.Department of Electrical and Computer Engineering,Texas A&M University,College Station,USA;3.Department of Materials Science and Engineering,Texas A&M University,College Station,USA
Abstract:
Due to recent advances in the field of microelectronics, the growth in microelectronics applications, and the exponentially increasing demand for microelectronic devices in the power sector, it is important to study the behavior of silicon at the nanoscale, given that nanoclusters of silicon could be used to design a new kind of lithium-ion batteries with strongly enhanced performance. Here, molecular dynamics was employed to calculate the self-diffusion coefficients of silicon clusters at room temperature and at a temperature approaching the melting point of silicon, complementing experimental efforts in this field. Silicon clusters of the same spherical geometry and size but with different vacancy fractions were studied using molecular dynamics using the Tersoff potential in order to estimate phase changes and self-diffusion coefficients. At 300 K, the self-diffusion coefficient was found to vary non-monotonically: the self-diffusion coefficient at a vacancy fraction of 7.5% is half than the vacancy at a fraction of 0%, while the self-diffusion coefficient at a vacancy fraction of 20% is two orders of magnitude larger than that at a vacancy fraction of 0%. However, there is only a marginal monotonic increase in the self-diffusion coefficient values with vacancy fraction at 2000 K. The results of this investigation of vacancy-mediated self-diffusion could aid attempts to improve diffusion control, which is crucial to nanocluster applications in various devices, and the results also provide insight into how the temperature, energy, pressure, and phase changes of the silicon clusters depend on vacancy fraction. This may ultimately allow the design and selection of materials for thermoelectric and optoelectronic devices and thermal transducers to be optimized. Our results also indicated that the findings we obtained for the clusters are independent of the particular random vacancy distribution considered and the heating rate applied to the clusters.
Graphical Abstract Silicon nanoparticles (SNP) are among the best options to choose from for the design of devices for renewable energies; SNP based material performance can be effectively tailored by controling the vacancy, temperature and other properties of the SNP.
