Shixian Liu

In this study, lattice dynamics calculations based on the Neuroevolution Machine-learned Potential (NEP) were performed for three types of silicon nanostructures: thin films, nanowires, and quantum dots. The temperature and size dependence of the specific heat capacity was systematically examined. The results reveal a significant enhancement in the specific heat capacity of nanostructures at low temperatures compared to bulk silicon, primarily due to phonon confinement, discrete energy spectra, and the emergence of low-frequency surface vibrational modes. These findings underscore the dominant role of nonlinear acoustic phonons at low temperatures, with increasing contributions from optical modes as the temperature rises. Notably, this work reports the temperature-dependent evolution of local fitting exponents in the specific heat scaling relation C_v ∼T^n(T) for nanostructured systems. The high accuracy and computational efficiency of the NEP model allow for detailed characterization of the complex phonon behaviors that govern thermal properties at the nanoscale.

This study investigates the dual nature of phonons—encompassing both particle-like and wave-like behaviors—and their roles in thermal transport within pillared graphene nanoribbons (PGNRs). Monte Carlo simulations are employed to evaluate how the presence of pillars affects the thermal conductivity of graphene nanoribbons (GNRs), revealing that pillars significantly reduce thermal conductivity by enhancing phonon-boundary scattering, thereby emphasizing particle effects. A comparison with molecular dynamics simulations enables quantitative assessment of the respective contributions of particle and wave phenomena to the observed reduction in thermal conductivity. Notably, as the width of PGNRs decreases, the influence of wave effects initially increases and then diminishes, suggesting a saturation behavior. Furthermore, this study introduces and evaluates the concept of phonon resonance hybridization depth in PGNRs.

In this study, the temperature fluctuations in three-dimensional confined nanostructures (quantum dots) of germanium, silicon, and diamond were calculated using the \(T^{3/2}\)model based on the particle-in-a-box (PIAB) approach and compared with the Debye \(T^3\)model. The analysis focused on quantum dots with sizes ranging from 1 to 100 nm. According to the PIAB \(T^{3/2}\)model, temperature fluctuations decrease as the temperature decreases, consistent with the principles of statistical physics. In contrast, the Debye \(T^3\)model predicts an increase in temperature fluctuations with decreasing temperature, contradicting the principles of statistical physics. These results emphasize the significant impact of quantum confinement and highlight the limitations of the Debye \(T^3\)model in describing nanoscale systems. Furthermore, distribution diagrams illustrating temperature fluctuations as functions of size and temperature were established for the first time. Based on these diagrams, clear boundaries were defined for the temperature and thermophysical property ranges where reliable predictions can be made.

This study introduces a novel method for calculating the thermal conductivity of graphene using a Monte Carlo approach to evaluate anisotropic three-phonon interactions. The phonon dispersion relation is derived using a force constant model that incorporates up to fifth-order nearest-neighbor interactions, while the phonon density of states (DOS) is computed via a generalized Gilat-Raubenheimer method. A quantitative relationship for the scaling exponent of the specific heat capacity at low temperatures is established, emphasizing the unique two-dimensional characteristics of graphene. To address anisotropic effects, the Monte Carlo approach efficiently identifies three-phonon combinations that adhere to the conservation laws of energy and momentum. The findings highlight the pivotal role of anisotropic phonon interactions in graphene’s thermal conductivity. The thermal conductivity values obtained through the iterative method exhibit strong agreement with previous three-phonon calculations, thereby validating the model. Nevertheless, discrepancies with experimental data suggest that incorporating higher-order phonon processes, such as four-phonon scattering, may further improve predictive accuracy.

We estimate the thermal properties of unsmooth Si nanowires, considering key factors such as size (diameter), surface texture (roughness) and quantum size effects (phonon states) at different temperatures. For nanowires with a diameter of less than 20 nm, we highlight the importance of quantum size effects in heat capacity calculations, using dispersion relations derived from the modified frequency equation for the elasticity of a rod. The thermal conductivities of nanowires with diameters of 37, 56, and 115nm are predicted using the Fuchs– Sondheimer model and Soffer’s specular parameter. Notably, the roughness parameters are chosen to reflect the technological characteristics of the real surfaces. Our findings reveal that surface texture plays a significant role in thermal conductivity, particularly in the realm of ballistic heat transfer within nanowires. This study provides practical recommendations for developing new thermal management materials.