Maximization and minimization of interfacial thermal conductance by modulating the mass distribution of the interlayer

Yang, Lina; Xiao, Wen‐Jing; Ma, Dengke; Jiang, Yi; Yang, Nuo

doi:10.1103/physrevb.103.155305

Cited by 36 publications

(18 citation statements)

References 46 publications

(50 reference statements)

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“…6 a. At the lower frequency (less than 10 THz), phonon transmission coefficients are showing an oscillating characteristic with the increasing frequency, which can be mostly attributed to periodic mass distribution 24 . Besides, it can also be seen from the Fig.…”

Section: Resultsmentioning

confidence: 91%

A comparative study of interfacial thermal conductance between metal and semiconductor

Zhang

Wang

et al. 2022

Sci Rep

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To understand and control thermal conductance of interface between metal and semiconductor has now become a crucial task for the thermal design and management of nano-electronic and micro-electronic devices. The interfacial alignments and electronic characteristics of the interfaces between metal and semiconductor are studied using a first-principles calculation based on hybrid density functional theory. The thermal conductance of interfaces between metal and semiconductor were calculated and analyzed using diffuse mismatch model, acoustic mismatch model and nonequilibrium molecular dynamics methods. Especially, according to nonequilibrium molecular dynamics, the values of thermal conductance were obtained to be 32.55 MW m−2 K−1 and 341.87 MW m−2 K−1 at C–Cu and Si–Cu interfaces, respectively. These results of theoretical simulation calculations are basically consistent with the current experimental data, which indicates that phonon–phonon interaction play a more important role than electron–phonon interaction during heat transport. It may be effective way to improve the interfacial thermal conductance through enhancing the interface coupling strength at the metal–semiconductor interface because the strong interfacial scattering plays a role in suppressing in the weaker interface coupling heterostructure, leading to the lower thermal conductance of interfaces. This could provide a beneficial reference for the design of the Schottky diode and thermal management at the interfaces between metal and semiconductor.

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Section: Resultsmentioning

confidence: 91%

A comparative study of interfacial thermal conductance between metal and semiconductor

Zhang

Wang

et al. 2022

Sci Rep

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“…[153] In the materials design, the nonequilibrium Green's function and machine learning algorithm can be combined to optimize the mass distribution of the interlayer, giving the maximum and minimum 𝑅 K values. [154] To manipulate the 𝑅 K of general interfaces, numerous studies suggest the impact of interfacial roughness, [38,[155][156][157] nanostructure modification, [158][159][160] isotopephonon scattering, [161] and transition layers at an interface. [162,163] For thermoelectrics, a high electrical conductivity but a low thermal conductivity is preferred, leading to a high thermoelectric figure of merit (ZT).…”

Section: Thermal Measurements and Thermal Engineering Of Gbsmentioning

confidence: 99%

A Review on Phonon Transport within Polycrystalline Materials

Hao¹,

Garg²

2021

ES. Mater. Manuf.

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As one fundamental problem in materials science research, thermal transport across grain boundaries is critical to many energy-related applications. Due to the complexity of grain boundaries, the current understanding on how a grain boundary interacts with heat carriers is still in its infancy. This review summarizes the current progresses of this important topic, with its further extension to general interfaces. One focus is on major modeling and simulation techniques to predict the thermal resistance of a grain boundary. The corresponding thermal measurements of individual grain boundaries and grain-boundary thermal engineering are also discussed. A better understanding of the grain-boundary phonon transport is critical to many energy-related applications, where the concerned thermal transport within a polycrystalline material can be largely suppressed by grain boundaries.

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“…However, the strong correlation between the electrons and holes at the L-point are making it challenging to accurately and efficiently predict the bandgap either with ab initio calculations or with k•p perturbations. [5][6][7][8] With the recent rapid development of artificial intelligence, many black-box and phenomenological tools have been created and accepted by researchers to predict various materials properties in an economical and efficient manner [9][10][11][12][13][14][15][16][17][18][19][20][21][22] using machine learning methodologies. [23][24][25][26][27][28] Owolabi et al [29] have used support vector (SV) regression to predict bandgaps of doped TiO 2 semiconductors and to generate the crystal lattice parameters of pseudo-cubic/cubic perovskites.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning to Predict the L-Point Direct Bandgap of Bi1-xSbx Nanomaterials

Táng¹,

Jean-Baptiste²,

Schuyler³

et al. 2022

Eng. Sci.

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With the development of modern nanoscience and nanotechnology, Bi 1-x Sb x can be synthesized into different nanoscale and nanostructured forms, including thin films, nanowires, nanotubes, nanoribbons, and many others. However, due to the strong correlation between electrons and holes at the L-point in the Brillouin zone, the direct band evolves in an anomalous manner under the quantum confinement when nanostructured. Due to the alloying and the low symmetry, predicting the L-point direct bandgap in a nanomaterial using either ab initio calculations or k•p perturbations can be computationally costive or inaccurate. We here try to solve this problem using the machine learning methods, including the support vector regression, the regression tree, the Gaussian process regression, and the artificial neural network. A goodness-of-fit of ~0.99 can be achieved for Bi 1-x Sb x thin films and nanowires.

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Maximization and minimization of interfacial thermal conductance by modulating the mass distribution of the interlayer

Cited by 36 publications

References 46 publications

A comparative study of interfacial thermal conductance between metal and semiconductor

A comparative study of interfacial thermal conductance between metal and semiconductor

A Review on Phonon Transport within Polycrystalline Materials

Machine Learning to Predict the L-Point Direct Bandgap of Bi1-xSbx Nanomaterials

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