scholarly journals Role of light and heavy embedded nanoparticles on the thermal conductivity of SiGe alloys

2011 ◽  
Vol 84 (12) ◽  
Author(s):  
A. Kundu ◽  
N. Mingo ◽  
D. A. Broido ◽  
D. A. Stewart
Author(s):  
Dongyan Xu ◽  
Joseph P. Feser ◽  
Yang Zhao ◽  
Hong Lu ◽  
Peter Burke ◽  
...  

Semiconductor alloys with epitaxially embedded nanoparticles have been shown to be very promising materials for thermoelectric energy conversion applications. In this work, we report on thermal conductivity characterization of two classes of p-type nanoparticle-in-alloy composite materials: compensated InGaAs semiconductor matrix with randomly distributed ErAs nanoparticles, and GaSb and its alloys with embedded ErSb nanoparticles. The three omega method is used to measure thermal conductivity of all materials. It is shown that thermal conductivity of compensated p-type ErAs:InGaAs is comparable to the n-type ErAs:InGaAs and it reduces with the increase in the erbium concentration. ErSb:GaSb nanocomposites are intrinsically p-type and show a thermal conductivity substantially lower than the pure GaSb compound. By comparing nanostructured samples from alloyed (InGaSb) and unalloyed (GaSb) matrix materials, we show that alloying is complimentary to the role of the nanostructure in reducing thermal conductivity. We also discuss Boltzmann transport modeling that indicates an optimum nanocrystal size, and the prospects for further reductions in the lattice thermal conductivity.


2021 ◽  
Vol 11 (4) ◽  
pp. 1459
Author(s):  
Soran M. Mamand

The Prasher analytical model was used for calculating the thermal conductivity of the embedded nanoparticles of Al2O3, CuO, ZnO, and SiO2 in conventional fluids, such as water and ethylene glycol. The values that were obtained were used in the nanofluid theoretical models for comparison with experimental data, where good agreement was obtained. Liang and Li’s theoretical model was also used to calculate the thermal conductivity of these nanoparticles, where the results agreed with those obtained using the Prasher model. The effect of the liquid nanolayer thickness around the nanoparticles that was used to enhance the effective thermal conductivity of nanofluids was explained. The role of the nanoparticles’ surface specularity parameter, which was size-dependent, was clarified. This theoretical trend provides a simple method for estimating the thermal conductivity of nanoparticles and nanofluids.


2020 ◽  
Vol 10 (5) ◽  
pp. 602-609
Author(s):  
Adil H. Awad

Introduction: A new approach for expressing the lattice thermal conductivity of diatomic nanoscale materials is developed. Methods: The lattice thermal conductivity of two samples of GaAs nanobeam at 4-100K is calculated on the basis of monatomic dispersion relation. Phonons are scattered by nanobeam boundaries, point defects and other phonons via normal and Umklapp processes. Methods: A comparative study of the results of the present analysis and those obtained using Callaway formula is performed. We clearly demonstrate the importance of the utilised scattering mechanisms in lattice thermal conductivity by addressing the separate role of the phonon scattering relaxation rate. The formulas derived from the correction term are also presented, and their difference from Callaway model is evident. Furthermore their percentage contribution is sufficiently small to be neglected in calculating lattice thermal conductivity. Conclusion: Our model is successfully used to correlate the predicted lattice thermal conductivity with that of the experimental observation.


2020 ◽  
Vol 6 (9) ◽  
pp. 5274-5280
Author(s):  
Sorout Shalini ◽  
Derek S. Frank ◽  
Ali H. Aldoukhi ◽  
Sami E. Majdalany ◽  
William W. Roberts ◽  
...  

2019 ◽  
Vol 52 (48) ◽  
pp. 485302 ◽  
Author(s):  
Dong-Xing Song ◽  
Yu-Feng Zhang ◽  
Wei-Gang Ma ◽  
Xing Zhang

2006 ◽  
Vol 86 (2) ◽  
pp. 211-218 ◽  
Author(s):  
Denise Russowski ◽  
Natasha Maurmann ◽  
Sandra Beatriz Rech ◽  
Arthur Germano Fett-Neto

2001 ◽  
Vol 90 (2) ◽  
pp. 292-301 ◽  
Author(s):  
I. M. Belousova ◽  
V. A. Grigor’ev ◽  
O. B. Danilov ◽  
A. G. Kalintsev ◽  
A. V. Kris’ko ◽  
...  

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