Universal Relation between Guest Free Space and Lattice Thermal Conductivity Reduction by Anharmonic Rattling in Type-I Clathrates

Suekuni, Koichiro; Yamamoto, Shûhei; Ávila, M. A.; Takabatake, Toshiro

doi:10.1143/jpsjs.77sa.61

Cited by 22 publications

(19 citation statements)

References 41 publications

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“…We also note that the guest off‐center motion is the primary factor responsible for the suppression of thermal conductivity 22, 17. It was revealed in Ref.…”

Section: Resultssupporting

confidence: 54%

“…High atomic weight in the framework can reduce the speed of sound in the material, and thereby decrease the thermal conductivity. At the same time, the studies indicated that the cage size increases as the majority cage atom changes from Si, Ge to Sn, which leads to the decrease of thermal conductivity [17]. It is also noteworthy that the lattice conductivity k L within each of these three series decreases with the decrease of the guest ionic radius r guest from Cs 1þ , Rb 1þ , Ba 2þ , Sr 2þ , to Eu 2þ [18,19].…”

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confidence: 99%

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Synthesis and first‐principles calculations of the structural and electronic properties of type‐I clathrates Sr₈Ga₁₆Sn_xGe_30 − x

Li¹,

Li²,

Deng³

et al. 2012

Physica Status Solidi (b)

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In the present work we report on the synthesis of type‐I clathrates Sr8Ga16SnxGe30 − x (0 ≤ x ≤ 12). To find out how the substitution of Sn for Ge affects structural stability and electronic structure, the structural and electronic properties for Sr8Ga16SnxGe30 − x (0 ≤ x ≤ 30) have been investigated by a first‐principles method based on the density‐functional theory (DFT). We found that the lattice constants of Sr8Ga16SnxGe30 − x series increase with increasing Sn content, which is consistent with X‐ray diffraction (XRD) results. Calculations indicate that the substitution of Sn for Ge leads to the change of the bulk modulus and the decrease of stability of the structure. It is found that these alloys are all indirect‐gap semiconductors and the bandgap decreases from about 0.36 eV in Sr8Ga16Ge30 to about 0.03 eV in Sr8Ga16Sn30 with increasing Sn content. The decrease of the bandgap is attributed to the increase of the free space for the Sr guest motion, which is accompanied by the guest's low‐energy modes and larger anharmonicity. These mean that an increase in the ratio of Sn‐to‐Ge can not only control the electrical properties of the materials, but also may reduce their thermal conductivity, suggesting that cage‐size tuning is one of the useful means to obtain high‐performance thermoelectric (TE) materials.

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“…We also note that the guest off‐center motion is the primary factor responsible for the suppression of thermal conductivity 22, 17. It was revealed in Ref.…”

Section: Resultssupporting

confidence: 54%

mentioning

confidence: 99%

Synthesis and first‐principles calculations of the structural and electronic properties of type‐I clathrates Sr₈Ga₁₆Sn_xGe_30 − x

Li¹,

Li²,

Deng³

et al. 2012

Physica Status Solidi (b)

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show abstract

“…High atomic weight in the framework can reduce the speed of sound in the material, and thereby decrease the thermal conductivity. At the same time, the studies indicated that the cage size increases as the majority cage atom changes from Si, Ge to Sn, which leads to the decrease of thermal conductivity 17. It is also noteworthy that the lattice conductivity κ L within each of these three series decreases with the decrease of the guest ionic radius r guest from Cs 1+ , Rb 1+ , Ba 2+ , Sr 2+ , to Eu 2+ 18, 19.…”

Section: Introductionmentioning

confidence: 88%

Concurrent workload mapping for multicore security systems

Soewito

Weng

2009

Concurrency and Computation

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Multicore based network processors are promising components to build real-time and scalable security systems to protect the networks and systems. The parallel nature of the processing system makes it challenging for application developers to concurrently program security systems for high performance. In this paper we present an automatic programming methodology that considers application complexity, traffic variation, and attack signatures update. In particular, our mapping algorithm concurrently takes advantage of parallelism in the level of tasks, applications, and packets to achieve optimal performance. We present results that show the effectiveness of the analysis, mapping, and the performance of the model methodology. Figure 1. Methodology for workload profiling and mapping.Multicore-based network processors (NPs) represent a major evolution in computing hardware technology. Multicore provides an application with more processing power for exploiting the inherent parallelism in network processing. Packets that belong to different network connections can be processed independently as a TCP/IP network gives no guarantees on packet orders.However, with the current software development tools and methodologies, it is challenging to program an application for an NP. The multiprocessor nature of an NP requires that the application developer partition the application and allocate it to processing resources manually. To achieve better load balancing for higher throughput, this typically requires that parts of the application are programmed in assembly and fine-tuned for performance.In this paper, we have presented a concurrent workload mapping methodology for multicore security systems in Figure 1. Our workload analyzer considers application complexity, traffic variation, and attacks signature update. Our mapping algorithm concurrently takes advantage of parallelism in tasks, applications, and packets to achieve high performance. Our proposed methodology can be Existing intermediate representations of an NP applications require programmers to manually partition applications (e.g. communication-oriented data flow representation in NP-Click) or do not consider run-time information (e.g. call graphs). We address this problem with a 'bottom-up' approach that differs from the above work insofar that we use profiling information from a simple uniprocessor implementation of an NP application. This profiling information is represented as an annotated directed acyclic graph (aDAG), which can be used to extract all the available parallelism in the application, to map the application to processing resources, and to model the run-time performance through an analytic performance model. This bottom-up approach of analyzing and mapping workloads promises to significantly reduce the complexity with which the application developer has to deal.Mapping algorithms for assigning task graphs to multiprocessors has been surveyed by Kwok and Ahmad [9]. Mapping tasks to conventional multiprocessors has been done by Austin and Sohi [10], is co...

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“…Suekuni et al studied the relationship between the lattice thermal conductivity of various Type I inorganic clathrates as a function of the cage radius and suggested that lattice thermal conductivity values scale well with the free space available for the guest ion in the cage. 83 Therefore, by appropriately choosing the guest and framework elements, the lattice thermal conductivity can be further tuned.…”

Section: Esr Identifi Cation and Quantitative Analysis Of Paramagnetimentioning

confidence: 99%

Inorganic and methane clathrates: Versatility of guest–host compounds for energy harvesting

Krishna

Koh

2015

MRS Energy & Sustainability

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Universal Relation between Guest Free Space and Lattice Thermal Conductivity Reduction by Anharmonic Rattling in Type-I Clathrates

Cited by 22 publications

References 41 publications

Synthesis and first‐principles calculations of the structural and electronic properties of type‐I clathrates Sr₈Ga₁₆Sn_xGe_30 − x

Synthesis and first‐principles calculations of the structural and electronic properties of type‐I clathrates Sr₈Ga₁₆Sn_xGe_30 − x

Concurrent workload mapping for multicore security systems

Inorganic and methane clathrates: Versatility of guest–host compounds for energy harvesting

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Universal Relation between Guest Free Space and Lattice Thermal Conductivity Reduction by Anharmonic Rattling in Type-I Clathrates

Cited by 22 publications

References 41 publications

Synthesis and first‐principles calculations of the structural and electronic properties of type‐I clathrates Sr8Ga16SnxGe30 − x

Synthesis and first‐principles calculations of the structural and electronic properties of type‐I clathrates Sr8Ga16SnxGe30 − x

Concurrent workload mapping for multicore security systems

Inorganic and methane clathrates: Versatility of guest–host compounds for energy harvesting

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Product

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Synthesis and first‐principles calculations of the structural and electronic properties of type‐I clathrates Sr₈Ga₁₆Sn_xGe_30 − x

Synthesis and first‐principles calculations of the structural and electronic properties of type‐I clathrates Sr₈Ga₁₆Sn_xGe_30 − x