Relative importance of grain boundaries and size effects in thermal conductivity of nanocrystalline materials

Dong, Huicong; Wen, Bin; Melnik, Roderick

doi:10.1038/srep07037

Cited by 170 publications

(96 citation statements)

References 37 publications

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“…It is well known that thermal conductivity of crystalline materials is lower than that of the corresponding single crystal materials, and it decreases as grain sizes decrease; different theoretical models have been proposed, and a critical review can be found in [38]. Anyway, it is commonly assumed that the correlation between incoming and outgoing phonons is completely destroyed by the scattering occurring at an interface [39] (e.g., a grain boundary).…”

Section: Resultsmentioning

confidence: 99%

Laser Pulse Effects on Plasma-Sprayed and Bulk Tungsten

Montanari

Pakhomova

Pizzoferrato

et al. 2017

Metals

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Tungsten (W) is considered a promising plasma-facing material for protecting the divertor of the ITER (International Thermonuclear Experimental Reactor). The effects on W of transient thermal loads of high energy occurring in a tokamak under operative conditions have been simulated through a single laser pulse delivered by an Nd:YAG laser. Bulk and plasma-sprayed (PS) samples have been submitted to tests and successively examined via SEM (scanning electron microscopy) observations. In both types of materials, the laser pulse induces similar effects: (i) a crater forms in the spot central area; (ii) all around the area, the ejection and the movement of molten metal give rise to a ridge; (iii) in a more external area, the surface shows plates with jagged boundaries and cracks induced by thermal stresses; (iv) the pores present in the original material become preferred ablation sites. However, the affected surface area in PS samples is larger and asymmetric if compared to that of bulk material. Such a difference has been explained by considering how microstructural characteristics influence heat propagation from the irradiated spot, and it was found that grain size and shape play a decisive role.

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Section: Resultsmentioning

confidence: 99%

Laser Pulse Effects on Plasma-Sprayed and Bulk Tungsten

Montanari

Pakhomova

Pizzoferrato

et al. 2017

Metals

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“…There are many analytical models available in the literature that describe the effects of material geometry on κ, either in the presence of grain boundaries, 71,72,78,79 or pores. 75,76,80,81,82 These are based on simple geometrical considerations, and assume uniformity of the corresponding features, but in the case of non-uniformities, or in the presence of two or more types of nanosized features, their accuracy fades.…”

Section: Analytical Models -Extensions and Validationmentioning

confidence: 99%

Monte Carlo phonon transport simulations in hierarchically disordered silicon nanostructures

2018

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Hierarchical material nanostructuring is considered to be a very promising direction for high performance thermoelectric materials. In this work we investigate thermal transport in hierarchically nanostructured silicon. We consider the combined presence of nanocrystallinity and nanopores, arranged under both ordered and randomized positions and sizes, by solving the Boltzmann transport equation using the Monte Carlo method. We show that nanocrystalline boundaries degrade the thermal conductivity more drastically when the average grain size becomes smaller than the average phonon mean-free-path. The introduction of pores degrades the thermal conductivity even further. Its effect, however, is significantly more severe when the pore sizes and positions are randomized, as randomization results in regions of higher porosity along the phonon transport direction, which introduce significant thermal resistance. We show that randomization acts as a large increase in the overall effective porosity. Using our simulations, we show that existing compact nanocrystalline and nanoporous theoretical models describe thermal conductivity accurately under uniform nanostructured conditions, but overestimate it in randomized geometries. We propose extensions to these models that accurately predict the thermal conductivity of randomized nanoporous materials based solely on a few geometrical features. Finally, we show that the new compact models introduced can be used within Matthiessen's rule to combine scattering from different geometrical features within ~10% accuracy. . 3.7 Wm −1 K −1 for an average pore size of ~ 30 nm and grain sizes between 50 and 80 nm. 21 By reducing both pore and grain sizes, however, Basu et al. reported κ = 1.2 Wm −1 K −1 at 40% porosity in p-type silicon. 22 A recent work in SiGe nanomeshes, reported ultralow κ of 0.55 ± 0.10 Wm −1 K −1 for SiGe nanocrystalline nanoporous structures, a value well below the amorphous limit. 23 A significant amount of work can be found in the literature attempting to clarify these experimental observations. However, theoretical investigations of thermal conductivity in highly/hierarchically disordered nanostructures (which include not only crystalline boundaries, but also pores of random sizes placed at random positions) are very limited. Understanding the qualitative and quantitative details of such geometries on the thermal conductivity would allow the design of more efficient thermoelectrics and heat management materials in general. In this work, we solve the Boltzmann transport equation for phonons in disordered Si nanostructures using the Monte Carlo (MC) method. Monte Carlo, which can capture the details of geometry with relative accuracy, is widely employed to understand phonon transport in various nanostructures such as nanowires, 24,25,26 thin films, 27,28 nanoporous materials, 29,30,31,32,33 polycrystalline materials, 10,15,34,35,36 nanocomposites, 37,38 corrugated structures, 39,40,41,42 silicon-on-insulator devices, 43 etc. We consider geometries that include grai...

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“…This can enhance thermal transport in some devices based on nanostructures such as nano/microelectrical chips or improve efficiency of thermoelectric conversion. Besides some experimental attempts [21][22][23], theoretical modeling performs a significant role to predict the thermal conductivity of bulk silicon and also silicon nanostructures containing vacancies [24][25][26][27][28][29], impurities [30][31][32] and grain boundaries [33,34].…”

Section: Introductionmentioning

confidence: 99%