arXiv subjects

Lina Yang

Publications and source records attributed to Lina Yang.

29 records · Page 2Linked to original sources

arXiv2021

Quantitatively Predicting Modal Thermal Conductivity of Nanocrystalline Si by full band Monte Carlo simulations

Thermal transport of nanocrystalline Si is of great importance for the application of thermoelectrics. A better understanding of the modal thermal conductivity of nanocrystalline Si will be expected to benefit the efficiency of thermoelectrics. In this work, the variance reduced Monte Carlo simulation with full band of phonon dispersion is applied to study the modal thermal conductivity of nanocrystalline Si. Importantly, the phonon modal transmissions across the grain boundaries which are modeled by the amorphous Si interface are calculated by the mode-resolved atomistic Greens function method. The predicted ratios of thermal conductivity of nanocrystalline Si to that of bulk Si agree well with that of the experimental measurements in a wide range of grain size. The thermal conductivity of nanocrystalline Si is decreased from 54 percent to 3 percent and the contribution of phonons with mean free path larger than the grain size increases from 30 percent to 96 percnet as the grain size decreases from 550 nm to 10 nm. This work demonstrates that the full band Monte Carlo simulation using phonon modal transmission by the mode-resolved atomistic Greens function method can capture the phonon transport picture in complex nanostructures, and therefore can provide guidance for designing high performance Si based thermoelectrics.

cond-mat.mtrl-sci↗

arXiv2021

Optimizing Thermal Transport in Graphene Nanoribbon Based on Phonon Resonance Hybridization

As a critical way to modulate thermal transport in nanostructures, phonon resonance hybridization has become an issue of great concern in the field of phonon engineering. In this work, we optimized phonon transport across graphene nanoribbon and obtained minimized thermal conductance by means of designing pillared nanostructures based on resonance hybridization. Specifically, the optimization of thermal conductance was performed by the combination of atomic Green` s function and Bayesian optimization. Interestingly, it is found that thermal conductance decreases non-monotonically with the increasing of number for pillared structure, which is severed as resonator and blocks phonon transport. Further mode-analysis and atomic Green` s function calculations revealed that the anomalous tendency originates from decreased phonon transmission in a wide frequency range. Additionally, nonequilibrium molecular dynamics simulations are performed to verify the results with the consideration of high-order phonon scattering. This finding provides novel insights into the control of phonon transport in nanostructures.

cond-mat.mes-hall↗

arXiv2020

Maximization and Minimization of Interfacial Thermal Conductance by Modulating the Mass Distribution of Interlayer

Tuning interfacial thermal conductance has been a key task for the thermal management of nanoelectronic devices. Here, it is studied how the interfacial thermal conductance is great influenced by modulating the mass distribution of the interlayer of one-dimensional atomic chain. By nonequilibrium Green's function and machine learning algorithm, the maximum/minimum value of thermal conductance and its corresponding mass distribution are calculated. Interestingly, the mass distribution corresponding to the maximum thermal conductance is not a simple function, such as linear and exponential distribution predicted in previous works, it is similar to a sinusoidal curve around linear distribution for larger thickness interlayer. Further, the mechanism of the abnormal results is explained by analyzing the phonon transmission spectra and density of states. The work provides deep insight into optimizing and designing interfacial thermal conductance by modulating mass distribution of interlayer atoms.

cond-mat.mtrl-sci↗

arXiv2020

Quantum multifractality in thermal conduction across random interfaces

Self-affine morphology of random interfaces governs their functionalities across tribological, geological, (opto-)electrical and biological applications. However, the knowledge of how energy carriers or generally classical/quantum waves interact with structural irregularity is still incomplete. In this work, we study vibrational energy transport through random interfaces exhibiting different correlation functions on the two-dimensional hexagonal lattice. We show that random interfaces at the atomic scale are Cantor composites populated on geometrical fractals, thus multifractals, and calculate their quantized conductance using atomistic approaches. We obtain a universal scaling law, which contains self-similarity for mass perturbation, and exponential scaling of structural irregularity quantified by fractal dimension. The multifractal nature and Cantor-composite picture may also be extendable to charge and photon transport across random interfaces.

cond-mat.mes-hall↗

arXiv2019

Directionally Constrained Fully Convolutional Neural Network For Airborne Lidar Point Cloud Classification

Point cloud classification plays an important role in a wide range of airborne light detection and ranging (LiDAR) applications, such as topographic mapping, forest monitoring, power line detection, and road detection. However, due to the sensor noise, high redundancy, incompleteness, and complexity of airborne LiDAR systems, point cloud classification is challenging. In this paper, we proposed a directionally constrained fully convolutional neural network (D-FCN) that can take the original 3D coordinates and LiDAR intensity as input; thus, it can directly apply to unstructured 3D point clouds for semantic labeling. Specifically, we first introduce a novel directionally constrained point convolution (D-Conv) module to extract locally representative features of 3D point sets from the projected 2D receptive fields. To make full use of the orientation information of neighborhood points, the proposed D-Conv module performs convolution in an orientation-aware manner by using a directionally constrained nearest neighborhood search. Then, we designed a multiscale fully convolutional neural network with downsampling and upsampling blocks to enable multiscale point feature learning. The proposed D-FCN model can therefore process input point cloud with arbitrary sizes and directly predict the semantic labels for all the input points in an end-to-end manner. Without involving additional geometry features as input, the proposed method has demonstrated superior performance on the International Society for Photogrammetry and Remote Sensing (ISPRS) 3D labeling benchmark dataset. The results show that our model has achieved a new state-of-the-art level of performance with an average F1 score of 70.7%, and it has improved the performance by a large margin on categories with a small number of points (such as powerline, car, and facade).

cs.CV↗

arXiv2017

Ultralow thermal conductivity of single crystalline porous silicon nanowires

Porous materials provide a large surface to volume ratio, thereby providing a knob to alter fundamental properties in unprecedented ways. In thermal transport, porous nanomaterials can reduce thermal conductivity by not only enhancing phonon scattering from the boundaries of the pores and therefore decreasing the phonon mean free path, but also by reducing the phonon group velocity. Here we establish a structure-property relationship by measuring the porosity and thermal conductivity of individual electrolessly etched single crystalline silicon nanowires using a novel electron beam heating technique. Such porous silicon nanowires exhibit extremely low diffusive thermal conductivity (as low as 0.33 Wm-1K-1 at 300K for 43% porosity), even lower than that of amorphous silicon. The origin of such ultralow thermal conductivity is understood as a reduction in the phonon group velocity, experimentally verified by measuring the Young modulus, as well as the smallest structural size ever reported in crystalline Silicon (less than 5nm). Molecular dynamics simulations support the observation of a drastic reduction in thermal conductivity of silicon nanowires as a function of porosity. Such porous materials provide an intriguing platform to tune phonon transport, which can be useful in the design of functional materials towards electronics and nano-electromechanical systems.

cond-mat.mtrl-sci↗

arXiv2016

Thermal transport size effects in silicon membranes featuring nanopillars as local resonators

Silicon membranes patterned by nanometer-scale pillars standing on the surface provide a practical platform for thermal conductivity reduction by resonance hybridization. Using molecular simulations, we investigate the effect of nanopillar size, unit-cell size, and finite-structure size on the net capacity of the local resonators in reducing the thermal conductivity of the base membrane. The results indicate that the thermal conductivity reduction increases as the ratio of the volumetric size of a unit nanopillar to that of the base membrane is increased, and the intensity of this reduction varies with unit-cell size at a rate dependent on the volumetric ratio. Considering sample size, the resonance-induced thermal conductivity drop is shown to increase slightly with the number of unit cells until it would eventually level off.

cond-mat.mes-hall↗

arXiv2014

Thermoelectric Properties of Nanoscale three dimensional Si Phononic Crystal

The thermoelectric properties of n type nanoscale three dimensional (3D) Si phononic crystals (PnCs) with spherical pores are studied. Density functional theory and Boltzmann transport equation under the relaxation time approximation are applied to study the electronic transport coefficients, electrical conductivity, Seebeck coefficient and electronic thermal conductivity. We found that the electronic transport coefficients in 3D Si PnC at room temperature (300 K) change very little compared with that of Si, for example, electrical conductivity and electronic thermal conductivity is decreased by 0.26 to 0.41 and 0.39 to 0.55 depending on carrier concentration, respectively, and the Seebeck coefficient is similar to that of bulk Si. However, the lattice thermal conductivity of 3D Si PnCs with spherical pores is decreased by a factor of 500 calculated by molecular dynamics methods, leading to the ZT of 0.76, which is about 30 times of that of porous Si. This work indicates that 3D Si PnC is a promising candidate for high efficiency thermoelectric materials.

cond-mat.mtrl-sci↗

arXiv2014

Significant Reduction of Graphene Thermal Conductivity by Phononic Crystal Structure

We studied the thermal conductivity of graphene phononic crystal (GPnC), also named as graphene nanomesh, by molecular dynamics simulations. The dependences of thermal conductivity of GPnCs on both length and temperature are investigated. It is found that the thermal conductivity of GPnCs is significantly lower than that of graphene and can be efficiently tuned by changing the porosity and period length. For example, the ratio of thermal conductivity of GPnC to thermal conductivity of graphene can be changed from 0.1 to 0.01 when the porosity is changed from about 21% to 65%. The phonon participation ratio spectra reveal that more phonon modes are localized in GPnCs with larger porosity. Our results suggest that creating GPnCs is a valuable method to efficiently manipulate the thermal conductivity of graphene.

cond-mat.mtrl-sci↗

arXiv2013

Extreme Low Thermal Conductivity in Nanoscale 3D Si Phononic Crystal with Spherical Pores

Thermoelectric material provides a high hope for converting harmful and useless heat into useful energy, electricity. It can also be used as solid state Peltier coolers in integrated circuits, an outstanding challenge for electronic engineers. The desire for a high efficient thermoelectric material has never been so keen. Although many works have been done, we are still far from having a recipe for thermoelectric materials. Nano-structuring provides an effective way to increase figure of merit (ZT) by reducing the thermal conductivity without affecting electronic property.1 Here, we propose a novel nanoscale three-dimensional (3D) Si phononic crystal (PnC) with spherical pores, which can reduce the thermal conductivity of bulk Si by a factor up to 10,000 times at room temperature. The extreme-low thermal conductivity could lead to a larger value of ZT than unity. The thermal conductivity changes little when temperature increases from room temperature to 1100 K. The phonon participation ratio spectra show there are more phonon localizations as the porosity of PnC increases.

cond-mat.mes-hall↗

arXiv2013

Reduction of Thermal Conductivity by Nanoscale 3D Phononic Crystal

The thermal conductivity of nanostructures needs to be as small as possible so that it will have a greater efficiency for solid-state electricity generation/refrigeration by thermoelectrics. We studied how the period length and the mass ratio affect the thermal conductivity of isotopic nanoscale 3D phononic crystal of Si. Simulation results by equilibrium molecular dynamics show isotopic nanoscale 3D phononic crystal can make a significance reduction on the thermal conductivity of bulk Si at high temperature,1000 K. Size and mass effects are obvious in manipulating thermal conductivity. The thermal conductivity decreases as the period length and mass ratio increases. The phonon dispersion curves show the decrease of group velocities in 3D phononic crystals. The phonon's localization and band gap is clearly shown in spectra of normalized inverse participation ratio in nanoscale 3D phononic crystal structure.

cond-mat.mes-hall↗