arXiv subjects

Chenxi Li

Publications and source records attributed to Chenxi Li.

76 records · Page 5Linked to original sources

arXiv2021

Theoretical Prediction of Heterogeneous Integration of Dissimilar Semiconductor with Various Ultra-Thin Oxides and 2D Materials

In this paper, we have built a numerical p-n Si/GaAs heterojunction model using a quantum-mechanical tunneling theory with various quantum tunneling interfacial materials including two-dimensional semiconductors such as hexagonal boron nitride (h-BN) and graphene and ALD-enabled oxide materials such as HfO2, Al2O3, and SiO2. Their tunneling efficiencies and tunneling current with different thicknesses were systematically calculated and compared. Multiphysics modeling was used with the aforementioned tunneling interfacial materials to analyze changes in strain under different temperature conditions. Considering the transport properties and thermal-induced strain analysis, Al2O3 among three oxide materials and graphene in 2D materials are favorable material choices that offer the highest heterojunction quality. Overall, our results offer the viable route to guide the selection of quantum tunneling materials for myriad possible combinations of new heterostructures that can be obtained via remote epitaxy and the UO method.

cond-mat.mes-hall↗

arXiv2021

A Reactive Molecular Dynamics Study of Hydrogenation on Diamond Surfaces

Hydrogenated diamond has been regarded as a promising material in electronic device applications, especially in field-effect transistors (FETs). However, the quality of diamond hydrogenation has not yet been established, nor has the specific orientation that would provide the optimum hydrogen coverage. In addition, most theoretical work in the literature use models with 100% hydrogenated diamond surfaces to study electronic properties, which is far from the experimentally observed hydrogen coverage. In this work, we have carried out a detailed study using fully atomistic reactive molecular dynamics (MD) simulations on low indices diamond surfaces i.e. (001), (013), (110), (113) and (111) to evaluate the quality and hydrogenation thresholds on different diamond surfaces and their possible effects on electronic properties. Our simulation results indicate that the 100% surface hydrogenation in these surfaces is hard to achieve because of the steric repulsion between the terminated hydrogen atoms. Among all the considered surfaces, the (001), (110), and (113) surfaces incorporate a larger number of hydrogen atoms and passivate the surface dangling bonds. Our results on hydrogen stability also suggest that these surfaces with optimum hydrogen coverage are robust under extreme conditions and could provide homogeneous p-type surface conductivity in the diamond surfaces, a key requirement for high-field, high-frequency device applications.

cond-mat.mtrl-sci↗

arXiv2020

A General Drag Coefficient for Flow over a Sphere

A generalized physics-based expression for the drag coefficient of spherical particles moving in a fluid is derived. The proposed correlation incorporates essential rarefied physics, low-speed hydrodynamics, and shock-wave physics to accurately model the particle-drag force for a wide range of Mach and Knudsen numbers (and therefore Reynolds number) a particle may experience. Owing to the basis of the derivation in physics-based scaling laws, the proposed correlation embeds gas-specific properties and has explicit dependence on the ratio of specific heat capacities at constant pressure and constant volume. The correlation is applicable for arbitrary particle relative velocity, particle diameter, gas pressure, gas temperature, and surface temperature. Compared to existing drag models, the correlation is shown to more accurately reproduce a wide range of experimental data. Finally, the new correlation is applied to simulate dust particles' trajectories in high-speed flow, relevant to a spacecraft entering the Martian atmosphere. The enhanced surface heat flux due to particle impact is found to be sensitive to the particle drag model.

physics.flu-dyn↗

arXiv2020

Expectile Neural Networks for Genetic Data Analysis of Complex Diseases

The genetic etiologies of common diseases are highly complex and heterogeneous. Classic statistical methods, such as linear regression, have successfully identified numerous genetic variants associated with complex diseases. Nonetheless, for most complex diseases, the identified variants only account for a small proportion of heritability. Challenges remain to discover additional variants contributing to complex diseases. Expectile regression is a generalization of linear regression and provides completed information on the conditional distribution of a phenotype of interest. While expectile regression has many nice properties and holds great promise for genetic data analyses (e.g., investigating genetic variants predisposing to a high-risk population), it has been rarely used in genetic research. In this paper, we develop an expectile neural network (ENN) method for genetic data analyses of complex diseases. Similar to expectile regression, ENN provides a comprehensive view of relationships between genetic variants and disease phenotypes and can be used to discover genetic variants predisposing to sub-populations (e.g., high-risk groups). We further integrate the idea of neural networks into ENN, making it capable of capturing non-linear and non-additive genetic effects (e.g., gene-gene interactions). Through simulations, we showed that the proposed method outperformed an existing expectile regression when there exist complex relationships between genetic variants and disease phenotypes. We also applied the proposed method to the genetic data from the Study of Addiction: Genetics and Environment(SAGE), investigating the relationships of candidate genes with smoking quantity.

stat.AP↗