Reducing thermal transport in electrically conducting polymers: Effects of ordered mixing of polymer chains

Pal, Souvik; Balasubramanian, Ganesh; Puri, Ishwar K.

doi:10.1063/1.4776676

Cited by 14 publications

(7 citation statements)

References 21 publications

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“…As the spectrum do not change much after 250 ps, we just focus the analysis on different time instants less than 250 ps when comparing different crosslinks cases. At the beginning of relaxation process (t=0 ps), the aromatic carbon atoms, C1, of bonded crosslinks have higher energy in the 1 st frequency (0-9 THz) and 2 nd frequency bands (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) than the same C1 atoms in the no-crosslink case (Fig. 32a).…”

Section: Atoms Of Crosslinksmentioning

confidence: 99%

Investigation of phonon transport and thermal boundary conductance at the interface of functionalized SWCNT and poly (ether-ketone)

Huang

Chen

Varshney

et al. 2016

Journal of Applied Physics

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Carbon nanostructures such as carbon nanotube (CNT), graphene, and carbon fibers can be used as fillers in amorphous polymers to improve their thermal properties. In this study, the effect of covalent bonding of CNT with poly(ether ketone) (PEK) on interfacial thermal interactions is investigated using non-equilibrium molecular dynamics simulations. The number of covalent bonds between (20, 20) CNT and PEK is varied in the range of 0–80 (0%–6.25%), and the thermal boundary conductance is computed. The analysis reveals that covalent functionalization of CNT atoms can enhance the thermal boundary conductance by an order of magnitude compared to the non-functionalized CNT-PEK interface at a high degree of CNT functionalization. Besides strengthening the thermal coupling, covalent functionalization is also shown to modify the phonon spectra of CNT. The transient spectral energy analysis shows that the crosslinks cause faster energy exchange from CNT to PEK in different frequency bands. The oxygen atom of hydroxyl group of PEK contributes energy transfer in the low frequency band, while aromatic and carbonyl carbon atoms play a more significant role in high frequency bands. In addition, by analyzing the relaxation time of the spectral temperature of different frequency bands of CNT, it is revealed that with increasing number of bonds, both lower frequency vibrational modes and higher frequency modes efficiently couple across the CNT-PEK interface and contribute in thermal energy transfer from CNT to the matrix.

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Section: Atoms Of Crosslinksmentioning

confidence: 99%

Investigation of phonon transport and thermal boundary conductance at the interface of functionalized SWCNT and poly (ether-ketone)

Huang

Chen

Varshney

et al. 2016

Journal of Applied Physics

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“…15 The effects of stretching and ordered mixing of polymer chains on the thermal conductivity of PA have also been studied by molecular dynamics simulations. 16,17 There have been a number of thermal conductivity measurements of PA sheets. For example, Moses and Denenstein reported a thermal conductivity measurement of pure and heavily doped PA sheets.…”

Section: Introductionmentioning

confidence: 99%

Iodine doping effects on the lattice thermal conductivity of oxidized polyacetylene nanofibers

Weathers

Matsushita

et al. 2013

Journal of Applied Physics

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Thermal transport in oxidized polyacetylene (PA) nanofibers with diameters in the range between 74 and 126 nm is measured with the use of a suspended micro heater device. With the error due to both radiation and contact thermal resistance corrected via a differential measurement procedure, the obtained thermal conductivity of oxidized PA nanofibers varies in the range between 0.84 and 1.24 W m À1 K À1 near room temperature, and decreases by 40%-70% after iodine doping. It is also found that the thermal conductivity of oxidized PA nanofibers increases with temperature between 100 and 350 K. Because of exposure to oxygen during sample preparation, the PA nanofibers are oxidized to be electrically insulating before and after iodine doping. The measurement results reveal that iodine doping can result in enhanced lattice disorder and reduced lattice thermal conductivity of PA nanofibers. If the oxidation issue can be addressed via further research to increase the electrical conductivity via doping, the observed suppressed lattice thermal conductivity in doped polymer nanofibers can be useful for the development of such conducting polymer nanostructures for thermoelectric energy conversion. V

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“…The prevailing question for polymer design is: how to select polymerization parameters for the production of materials with a specific set of material properties? Recently, there has been an emphasis on creating polymers with finely tuned phonon modes for acoustic metamaterials, [22] localization of vibrational modes, [23] thermal transport, [24] and fracture toughness. [6] But the changing diffusivity during polymerization and the complex relationship between fabrication parameters and the emergent material properties (e.g., phonon density of states) have been significant impediments so far.…”

mentioning

confidence: 99%

“…[18,19] Herein, we modeled polymerization using reaction-diffusion master equation (RDME) as a stochastic process consisting of initiation, propagation, crosslinking, and fluctuation events. Recently, there has been an emphasis on creating polymers with finely tuned phonon modes for acoustic metamaterials, [22] localization of vibrational modes, [23] thermal transport, [24] and fracture toughness. The radial distribution functions (RDFs) [21] and phonon modes of the stochastically created network characterizes the effect of composition and photocuring rates on complex emergent properties, like the "boson peak" in the vibrational density of states.…”

mentioning

confidence: 99%

“…The radial distribution functions (RDFs) and phonon modes of the stochastically created network characterizes the effect of composition and photocuring rates on complex emergent properties, like the “boson peak” in the vibrational density of states. Recently, there has been an emphasis on creating polymers with finely tuned phonon modes for acoustic metamaterials, localization of vibrational modes, thermal transport, and fracture toughness . So, our method serves as a computational map from processing conditions to material properties of photocured polymers.…”

mentioning

confidence: 99%

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Computational Design of Photocured Polymers Using Stochastic Reaction–Diffusion Simulation

Sarkar

Lin‐Gibson

2018

Advcd Theory and Sims

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Photopolymerization is widely used for creating materials for biomedical applications, lithography, and fast 3D fabrication. The resin composition and curing protocol during polymerization define a high-dimensional parameter space that dictates the reaction kinetics, network structures, and physical properties of polymerized materials. But a quantitative map from the input parameter space to the transient and final properties does not exist. Here, a computational method is presented to simulate network growth as a stochastic process using reaction-diffusion master equations. The evolving diffusivity of reacting species during polymerization is modeled using effective medium method, thereby making the rates of reaction and fluctuation events sensitive to the spatial configuration of a growing network. The numerical studies show that this method can correlate fabrication parameters to polymer properties like crosslink heterogeneity, relaxation time, phonon density of states, and bulk modulus, in silico. Hence, stochastic simulation of polymerization is broadly applicable to accelerate the design of polymeric materials with targeted physical properties for specific applications.Photopolymerization of networks affords a rapid, solvent-free approach to produce polymers and composites, which is used for fabricating microfluidic sensors, [1,2] biomedical implants, [3,4] and recently for fast 3D printing. [5] Two sets of parameters control the kinetics and properties of photopolymerization: 1) chemical composition of the resin (i.e., rate constants, interaction energies, of monomers and initiators), and 2) curing conditions (e.g., light source, curing time, temperature). The prevailing question for polymer design is: how to select polymerization parameters for the production of materials with a specific set of material properties? A quantitative answer to this question is the key for simulation-based design of polymers. [6] But the changing diffusivity during polymerization and the complex relationship between fabrication parameters and the emergent material properties (e.g., phonon density of states) have been significant impediments so far.Traditional simulations performed for polymerization kinetics use ordinary differential equations, called reaction rate equations (RREs), [6][7][8][9][10] which only determine the degree of double-bond conversion (DC) during the reaction; no information regarding spatial organization of the polymer network is obtained. Decrease in diffusivity is incorporated in RREs by treating the rate constants as empirical functions of DC, [7] which cannot account for differences in network structures developed at the same DC. So, results from RREs are insufficient for computing mechanical and thermal properties of the cured polymer. Previous efforts at stochastic simulation of polymerization omitted either the fluctuation of the network or the diminishing diffusivity. [11][12][13][14][15] Neglecting these details fail to simulate autoacceleration, post-cure polymerization, and the network structu...

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Reducing thermal transport in electrically conducting polymers: Effects of ordered mixing of polymer chains

Cited by 14 publications

References 21 publications

Investigation of phonon transport and thermal boundary conductance at the interface of functionalized SWCNT and poly (ether-ketone)

Investigation of phonon transport and thermal boundary conductance at the interface of functionalized SWCNT and poly (ether-ketone)

Iodine doping effects on the lattice thermal conductivity of oxidized polyacetylene nanofibers

Computational Design of Photocured Polymers Using Stochastic Reaction–Diffusion Simulation

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