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Abstract Gallium‐based liquid metal (LM) composite with metallic fillers is an emerging class of thermal interface materials (TIMs), which are widely applied in electronics and power systems to improve their performance. In situ alloying between gallium and many metallic fillers like copper and silver, however, leads to a deteriorated composite stability. This paper presents an interfacial engineering approach using 3‐chloropropyltriethoxysilane (CPTES) to serve as effective thermal linkers and diffusion barriers at the copper‐gallium oxide interfaces in the LM matrix, achieving an enhancement in both thermal conductivity and stability of the composite. By mixing LM with copper particles modified by CPTES, a thermal conductivity (κ) as high as 65.9 W m−1K−1is achieved. In addition, κ can be tuned by altering the terminal groups of silane molecules, demonstrating the flexibility of this approach. The potential use of such composite as a TIM is also shown in the heat dissipation of a computer central processing unit. While most studies on LM‐based composites enhance the material performance via direct mixing of various fillers, this work provides a different approach to fabricate high‐performance LM‐based composites and may further advance their applications in various areas including thermal management systems, flexible electronics, consumer electronics, and biomedical systems.
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Metal Matrix–Metal Nanoparticle Composites with Tunable Melting Temperature and High Thermal Conductivity for Phase-Change Thermal Storage
- Award ID(s):
- 1236656
- PAR ID:
- 10188006
- Date Published:
- Journal Name:
- ACS Nano
- Volume:
- 9
- Issue:
- 2
- ISSN:
- 1936-0851
- Page Range / eLocation ID:
- 1341 to 1351
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The goal of this work is to predict the effect of part geometry and process parameters on the instantaneous spatiotemporal distribution of temperature, also called the thermal field or temperature history, in metal parts as they are being built layer-by-layer using additive manufacturing (AM) processes. In pursuit of this goal, the objective of this work is to develop and verify a graph theory-based approach for predicting the temperature distribution in metal AM parts. This objective is consequential to overcome the current poor process consistency and part quality in AM. One of the main reasons for poor part quality in metal AM processes is ascribed to the nature of temperature distribution in the part. For instance, steep thermal gradients created in the part during printing leads to defects, such as warping and thermal stress-induced cracking. Existing nonproprietary approaches to predict the temperature distribution in AM parts predominantly use mesh-based finite element analyses that are computationally tortuous—the simulation of a few layers typically requires several hours, if not days. Hence, to alleviate these challenges in metal AM processes, there is a need for efficient computational models to predict the temperature distribution, and thereby guide part design and selection of process parameters instead of expensive empirical testing. Compared with finite element analyses techniques, the proposed mesh-free graph theory-based approach facilitates prediction of the temperature distribution within a few minutes on a desktop computer. To explore these assertions, we conducted the following two studies: (1) comparing the heat diffusion trends predicted using the graph theory approach with finite element analysis, and analytical heat transfer calculations based on Green’s functions for an elementary cuboid geometry which is subjected to an impulse heat input in a certain part of its volume and (2) simulating the laser powder bed fusion metal AM of three-part geometries with (a) Goldak’s moving heat source finite element method, (b) the proposed graph theory approach, and (c) further comparing the thermal trends predicted from the last two approaches with a commercial solution. From the first study, we report that the thermal trends approximated by the graph theory approach are found to be accurate within 5% of the Green’s functions-based analytical solution (in terms of the symmetric mean absolute percentage error). Results from the second study show that the thermal trends predicted for the AM parts using graph theory approach agree with finite element analyses, and the computational time for predicting the temperature distribution was significantly reduced with graph theory. For instance, for one of the AM part geometries studied, the temperature trends were predicted in less than 18 min within 10% error using the graph theory approach compared with over 180 min with finite element analyses. Although this paper is restricted to theoretical development and verification of the graph theory approach, our forthcoming research will focus on experimental validation through in-process thermal measurements.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1021/nn505328j
