Manipulating thermal conductivity through substrate coupling

We report a new approach to the thermal conductivity manipulation -- substrate coupling. Generally, the phonon scattering with substrates can decrease the thermal conductivity, as observed in recent experiments. However, we find that at certain regions, the coupling to substrates can increase the thermal conductivity due to a reduction of anharmonic phonon scattering induced by shift of the phonon band to the low wave vector. In this way, the thermal conductivity can be efficiently manipulated via coupling to different substrates, without changing or destroying the material structures. This idea is demonstrated by calculating the thermal conductivity of modified double-walled carbon nanotubes and also by the ice nanotubes coupled within carbon nanotubes.Comment: 5 figure

Finite temperature properties of clusters by replica exchange metadynamics: the water nonamer

We introduce an approach for the accurate calculation of thermal properties of classical nanoclusters. Based on a recently developed enhanced sampling technique, replica exchange metadynamics, the method yields the true free energy of each relevant cluster structure, directly sampling its basin and measuring its occupancy in full equilibrium. All entropy sources, whether vibrational, rotational anharmonic and especially configurational -- the latter often forgotten in many cluster studies -- are automatically included. For the present demonstration we choose the water nonamer (H2O)9, an extremely simple cluster which nonetheless displays a sufficient complexity and interesting physics in its relevant structure spectrum. Within a standard TIP4P potential description of water, we find that the nonamer second relevant structure possesses a higher configurational entropy than the first, so that the two free energies surprisingly cross for increasing temperature.Comment: J. Am. Chem. Soc. 133, 2535-2540 (2011

Thermal conductivity of epitaxial graphene nanoribbons on SiC: effect of substrate

We study the effect of SiC substrate on thermal conductivity of epitaxial graphene nanoribbons (GNRs) using the nonequilibrium molecular dynamics method. We show that the substrate has strong interaction with single-layer GNRs during the thermal transport, which largely reduces the thermal conductivity. The thermal conductivity characteristics of suspended GNRs are well preserved in the second GNR layers of bilayer GNR, which has a weak van der Waals interaction with the underlying structures. The out-of-plane phonon mode is found to play a critical role on the thermal conductivity variation of the second GNR layer induced by the underlying structures.Comment: 5 pages, 4 figures, 1 tabl

LMSFC: A Novel Multidimensional Index based on Learned Monotonic Space Filling Curves

The recently proposed learned indexes have attracted much attention as they can adapt to the actual data and query distributions to attain better search efficiency. Based on this technique, several existing works build up indexes for multi-dimensional data and achieve improved query performance. A common paradigm of these works is to (i) map multi-dimensional data points to a one-dimensional space using a fixed space-filling curve (SFC) or its variant and (ii) then apply the learned indexing techniques. We notice that the first step typically uses a fixed SFC method, such as row-major order and z-order. It definitely limits the potential of learned multi-dimensional indexes to adapt variable data distributions via different query workloads. In this paper, we propose a novel idea of learning a space-filling curve that is carefully designed and actively optimized for efficient query processing. We also identify innovative offline and online optimization opportunities common to SFC-based learned indexes and offer optimal and/or heuristic solutions. Experimental results demonstrate that our proposed method, LMSFC, outperforms state-of-the-art non-learned or learned methods across three commonly used real-world datasets and diverse experimental settings.Comment: Extended Version. Accepted by VLDB 202