Strain engineering for thermal conductivity of single-walled carbon nanotube forests

Jiang, Jin-Wu

doi:10.1016/j.carbon.2014.10.006

Cited by 15 publications

(19 citation statements)

References 41 publications

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“…The thermal conductivity of CNTs is dependent on defects, atomic structure relative to the rolling geometry of the graphite sheets, the size, length, and diameter of the tubes, in addition to the morphology, purification, and functionalization of CNTs (Shaikh et al 2007, Gong et al 2014, Jiang 2015, Cui et al 2016). However, the mechanism of thermal energy transport in CNTs is perceived to take place through phonon conductivity similar to other nonmetallic materials (Huang et al 2005, Cola et al 2009, Cola 2010.…”

Section: Thermal Conductivity Of Carbon-nanotube-based Pncsmentioning

confidence: 99%

Recently emerging trends in thermal conductivity of polymer nanocomposites

Idumah

Hassan²

2016

Reviews in Chemical Engineering

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Due to escalating power densities in electronics, information, communication, energy storage, aerospace, and automobile technologies, heat dissipation has become immensely essential for the efficient performance and reliability of photonic, electronics, optoelectronics, and other devices in order to proactively prevent premature failure due to overheating. The functionalization and synergistic inclusion of thermally conducting nanoparticles such as carbon derivatives and metallic and ceramic fillers into polymer matrices have resulted in development of thermally conducting polymer nanocomposites. This has enlarged the scope of application of these materials in areas hitherto restricted due to poor thermal conductivity and, therefore, opened broad windows of opportunities for polymer nanocomposites as emerging alternative replacements for metal components in various applications where effective heat dissipation is compulsory for device performance. Thus, this paper critically discusses globally emerging technologies applied in the development of thermally conductive polymer nanocomposites for various industrial applications.

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Section: Thermal Conductivity Of Carbon-nanotube-based Pncsmentioning

confidence: 99%

Recently emerging trends in thermal conductivity of polymer nanocomposites

Idumah

Hassan²

2016

Reviews in Chemical Engineering

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“…The deformation in the monolayer membranes can induce high local strains. 9,29 The effects of strains on the in-plane thermal conductivity of graphene, 47 silicene, 48 phosphorene 49 and other 2D materials [50][51][52] have been extensively studied. However, the effects of tensile strain on the interfacial thermal transport across hybrid sheets are still unclear, and hence the present study.…”

Section: Effects Of Tensile Strainmentioning

confidence: 99%

Thermal contact resistance across a linear heterojunction within a hybrid graphene/hexagonal boron nitride sheet

Yang

Zhang

Zeng

2016

Phys. Chem. Chem. Phys.

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Interfacial thermal conductance plays a vital role in defining the thermal properties of nanostructured materials in which heat transfer is predominantly phonon mediated. In this work, the thermal contact resistance (R) of a linear heterojunction within a hybrid graphene/hexagonal boron nitride (h-BN) sheet is characterized using non-equilibrium molecular dynamics (NEMD) simulations. The effects of system dimension, heat flux direction, temperature and tensile strain on the predicted R values are investigated. The spatiotemporal evolution of thermal energies from the graphene to the h-BN sheet reveals that the main energy carrier in graphene is the flexural phonon (ZA) mode, which also has the most energy transmissions across the interface. The calculated R decreases monotonically from 5.2 × 10(-10) to 2.2 × 10(-10) K m(2) W(-1) with system lengths ranging from 20 to 100 nm. For a 40 nm length hybrid system, the calculated R decreases by 42% from 4.1 × 10(-10) to 2.4 × 10(-10) K m(2) W(-1) when the system temperature increases from 200 K to 600 K. The study of the strain effect shows that the thermal contact resistance R between h-BN and graphene sheets increases with the tensile strain. Detailed phonon density of states (PDOS) is computed to understand the thermal resistance results.

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“…However, the ML predicted location of the band-gap was correct for every test case and its value was correct to about 0.05 eV or better, every time. The ability of the ML model to predict the electronic structure of low-dimensional materials as a function of imposed deformation opens up the use of such techniques for strain-engineering applications [56,57,61]. excellent and the post-processed ML model is also able to precisely predict the location of the band-gap (at η = 1 3 , ν = 2) as well as its value (0.128 eV from Helical DFT) to about 6% accuracy in this case.…”

Section: Prediction Of Nuclear Coordinates Energies and Band Structurementioning

confidence: 99%

“…We present here a machine learning model that can predict the electronic structure of quasi-one-dimensional materials as they are subjected to strains commensurate with their geometries. One of the key motivations of our work is that the complex interplay of electronic fields and mechanical deformations in low-dimensional materials is an active area of investigation in the literature [56][57][58][59][60][61][62], and therefore, it is desirable to have machine learning models where strain parameters can be mapped to electronic fields for such systems. Additionally, the techniques described here are likely to find use in the discovery of novel phases of low-dimensional chiral matter [63] and multiscale modeling [64].…”

Section: Introductionmentioning

confidence: 99%

Machine learning based prediction of the electronic structure of quasi-one-dimensional materials under strain

Pathrudkar,

Yu,

Ghosh

et al. 2022

Preprint

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We present a machine learning based model that can predict the electronic structure of quasione-dimensional materials while they are subjected to deformation modes such as torsion and extension/compression. The technique described here applies to important classes of materials systems such as nanotubes, nanoribbons, nanowires, miscellaneous chiral structures and nanoassemblies, for all of which, tuning the interplay of mechanical deformations and electronic fields -i.e., strain engineering -is an active area of investigation in the literature. Our model incorporates global structural symmetries and atomic relaxation effects, benefits from the use of helical coordinates to specify the electronic fields, and makes use of a specialized data generation process that solves the symmetry-adapted equations of Kohn-Sham Density Functional Theory in these coordinates. Using armchair single wall carbon nanotubes as a prototypical example, we demonstrate the use of the model to predict the fields associated with the ground state electron density and the nuclear pseudocharges, when three parameters -namely, the radius of the nanotube, its axial stretch, and the twist per unit length -are specified as inputs. Other electronic properties of interest, including the ground state electronic free energy, can be evaluated from these predicted fields with low-overhead post-processing, typically to chemical accuracy. Additionally, we show how the nuclear coordinates can be reliably determined from the predicted pseudocharge field using a clustering based technique. Remarkably, only about 120 data points are found to be enough to predict the three dimensional electronic fields accurately, which we ascribe to the constraints imposed by symmetry in the problem setup, the use of low-discrepancy sequences for sampling, and efficient representation of the intrinsic low-dimensional features of the electronic fields. We comment on the interpretability of our machine learning model and anticipate that our framework will find utility in the automated discovery of low-dimensional materials, as well as the multi-scale modeling of such systems.

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Strain engineering for thermal conductivity of single-walled carbon nanotube forests

Cited by 15 publications

References 41 publications

Recently emerging trends in thermal conductivity of polymer nanocomposites

Recently emerging trends in thermal conductivity of polymer nanocomposites

Thermal contact resistance across a linear heterojunction within a hybrid graphene/hexagonal boron nitride sheet

Machine learning based prediction of the electronic structure of quasi-one-dimensional materials under strain

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