Large tunability of lattice thermal conductivity of monolayer silicene via mechanical strain

By Han Xie, Ouyang Tao, 'Eric Germaneau, G. Qin, Ming Hu, Hua Bao. 2015

160 citations recorded by the source corpus.

Strain engineering is one of the most promising and effective routes toward continuously tuning the electronic and optic properties of materials, while thermal properties are generally believed to be insensitive to mechanical strain. In this paper, the strain-dependent thermal conductivity of monolayer silicene under uniform biaxial tension is computed by solving the phonon Boltzmann transport equation with interatomic force constants extracted from first-principles calculations. Unlike the commonly believed understanding that thermal conductivity only slightly decreases with increased tensile strain for bulk materials, it is found that the thermal conductivity of silicene can increase dramatically with strain. Depending on the size, the maximum thermal conductivity of strained silicene can be a few times higher than that of the unstrained case. Such an unusual strain dependence is mainly attributed to the dramatic enhancement in the acoustic phonon lifetime. Such enhancement plausibly originates from the flattening of the buckling of the silicene structure upon stretching, which is unique for silicene as compared with other common two-dimensional materials. Our findings offer perspectives on modulating the thermal properties of low-dimensional structures for applications such as thermoelectrics, thermal circuits, and nanoelectronics.

Subfields: Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/eb8546a5a03bcf6bcda3524d74354ec72e93a8e8

How researchers see this paper

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In Materials Chemistry

1. Xiaobo Yin
2. Yujie Wei
3. Baowen Li
4. Xiaokun Gu
5. Ronggui Yang
6. C. Hua
7. S. Lee
8. X. Ruan

Citations in the corpus

• Survey of ab initio phonon thermal transport (2018, background): “phene's cousins, (silicene, germanene, and stanene) [15,104], and multilayer graphene and BN [105].”
• Colloquium: Phononic thermal properties of two-dimensional materials (2017, background): “Figure 8(b) shows phonon dispersion of silicene as an example (Xie et al., 2016).”
• Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport (2021, unknown): “[86] Han Xie, Tao Ouyang, Éric Germaneau, Guangzhao Qin, Ming Hu, and Hua Bao, “Large tunability of lattice thermal conductivity of monolayer silicene via mechanical strain,” Phys.”