The cross-plane interaction across interface changes phonon kinetics and spectrum near the interface, and the interaction effects on both in-plane and cross-plane thermal transport are investigated in graphene/Si heterostructure. The interaction with substrates dramatically reduces the in-plane thermal conductivity of graphene by changing the behaviors of the out-of-plane phonons as well as adding phonon-substrate scatterings. Applying pressure up to 2.6 GPa to the sandwiched graphene reduces the cross-plane interfacial thermal resistance by 50% without altering the in-plane thermal conductivity in a significant way. The pressure increases the inter-layer coupling and creates a low-energy phonon transport channel between graphene and Si with minor effects on phonons propagating along the graphene. This study suggests the anisotropic control of thermal transport, and the physics and calculation results can be used to improve the thermal design and analysis in two-dimensional nano-electronic devices.

Multiple-CS-NP sintered structure of 600 K yields similar porosity as the counterpart sintered at surface premelting temperature (900 K).

Copper nanowires (CuNWs) are a key building block to facilitate carrier conduction across a broad range of nanodevices. For integration into nanoscale devices, manipulation and welding of these nanowires need to be overcome. Based on high energy density laser processing investigation, we report on innovative welding of single CuNWs to a silver film using a tightly focused laser beam combined with manipulation of CuNWs through the dielectrophoresis (DEP) method. Two types of lasers, femtosecond (FS) and continuous-wave (CW), were employed to analyze, improve, and control Cu-NW melting characteristics under high energy density irradiation. The FS laser welding of CuNWs resulted in a metallic joint with a low contact resistance suitable for functional electronic nanodevices. Computational simulations using the 1-D heat diffusion equation and finite difference method (FDM) were performed to gain an insight into metal–laser interactions for high performance welded contact development. Simulation studies on lasers established contrasting melting behavior of metal under laser irradiation. The device feasibility of CuNW based welded contacts was evaluated in terms of the electrical performance of a glucose sensor. It was possible to sense glucose concentration down to 10⁻⁶ M, demonstrating a path towards integration of CuNWs into wearable, flexible nanoelectronic devices.

A large number of microstructural parameters and a wide range of transport physics impose challenges on thermal transport analysis of alloy. Herein, modern data science techniques are employed to overcome the challenges, pursuing effective calculation of thermal transport properties. This emerging approach is tested for precipitate‐hardened aluminum (Al) alloy with consideration of precipitate morphology. The finite element method (FEM) is employed to create a database of effective thermal conductivity of hypothetical Al alloys with varying precipitate morphological and thermal transport features. Using the FEM‐generated data sets, the correlation analysis is conducted to qualitatively evaluate the importance of various precipitate features. The correlation analysis identifies the surface area, average diameter, and volume fraction of precipitates as the most descriptive features for determining the thermal conductivity of alloys. Afterward machine learning (ML) models are trained to accurately predict the effective thermal conductivity. Comparing the ML predictions with effective thermal conductivity and microstructural information from experiments, precipitate thermal transport properties can be calculated, such as interfacial conductance between Al matrix and precipitate, without atomistic simulations. This research demonstrates the feasibility of data‐driven approaches for effective thermal transport calculation and the promise of the FEM‐generated data analysis for more comprehensive evaluation of metallic alloys.