通过声子漏斗提高半导体薄膜的热导率

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
C. Jaymes Dionne, Sandip Thakur, Nick Scholz, Patrick Hopkins, Ashutosh Giri
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引用次数: 0

摘要

热力学第二定律认为,能量从热向冷扩散。由此产生的温度梯度决定了各种技术的效率和故障率。然而,随着材料的尺寸缩小到纳米级,适当的散热策略变得更具挑战性,因为声子的平均自由路径变得大于特征长度尺度。这将导致依赖于界面和边界的温度梯度,最终可能导致严重的热瓶颈。在这里,我们发现了一种被称为 "声子漏斗 "的现象,它可以控制声子传输,优先将声子能量从几何限制的界面热瓶颈引导到局部较冷区域。这种现象取代了基于宏观尺度温度梯度的热扩散,从而引入了一种纳米尺度机制,即边界散射会增加薄膜的声子热导率,这与传统认识上的效果恰恰相反。这项研究推进了人们对纳米尺度声子传输以及高效散射方法在增强热传输方面的作用的基本认识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Enhancing the thermal conductivity of semiconductor thin films via phonon funneling

Enhancing the thermal conductivity of semiconductor thin films via phonon funneling

The second law of thermodynamics asserts that energy diffuses from hot to cold. The resulting temperature gradients drive the efficiencies and failures in a plethora of technologies. However, as the dimensionalities of materials shrink to the nanoscale regime, proper heat dissipation strategies become more challenging since the mean free paths of phonons become larger than the characteristic length scales. This leads to temperature gradients that are dependent on interfaces and boundaries, which ultimately can lead to severe thermal bottlenecks. Herein, we uncover a phenomenon which we refer to as ‘phonon funneling’, that allows the control of phonon transport to preferentially direct phonon energy away from geometrically confined interfacial thermal bottlenecks and into localized colder regions. This phenomenon supersedes heat diffusion based on the macroscale temperature gradients, thus introducing a nanoscale regime in which boundary scattering increases the phonon thermal conductivity of thin films, an opposite effect than what is traditionally realized. This work advances the fundamental understanding of phonon transport at the nanoscale and the role of efficient scattering methods for enhancing thermal transport.

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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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