Colloidal metal particles as probes of nanoscale thermal transport in fluids

Wilson, Orla M.; Hu, Xiaoyuan; Cahill, David G.

doi:10.1103/physrevb.66.224301

Cited by 285 publications

(298 citation statements)

References 31 publications

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“…Xue [74] found that the effects of the interfacial resistance, or the Kapitza resistance, between the nanotubes and the surrounding matrix can effectively reduce the thermal conductivity of the nanotubes by several orders of magnitude. Using the work of Huxtable et al [21], Wilson et al [75] and Xue [74] as justification, a phenomenological degradation of nanotube thermal conductivity by a factor of 20 is incorporated into the model. The data of Wang et al [23], Moisala et al [9], Hong & Tai [65], Biercuk et al [66], Cai & Song [24] and Xu et al [22] are selected to provide a broad spectrum of polymers and volume fractions for comparison.…”

Section: (Ii) Thermal Resultsmentioning

confidence: 99%

Estimation of the physical properties of nanocomposites by finite-element discretization and Monte Carlo simulation

Spanos

Elsbernd

Ward

et al. 2013

Phil. Trans. R. Soc. A.

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This paper reviews and enhances numerical models for determining thermal, elastic and electrical properties of carbon nanotube-reinforced polymer composites. For the determination of the effective stress–strain curve and thermal conductivity of the composite material, finite-element analysis (FEA), in conjunction with the embedded fibre method (EFM), is used. Variable nanotube geometry, alignment and waviness are taken into account. First, a random morphology of a user-defined volume fraction of nanotubes is generated, and their properties are incorporated into the polymer matrix using the EFM. Next, incremental and iterative FEA approaches are used for the determination of the nonlinear properties of the nanocomposite. For the determination of the electrical properties, a spanning network identification algorithm is used. First, a realistic nanotube morphology is generated from input parameters defined by the user. The spanning network algorithm then determines the connectivity between nanotubes in a representative volume element. Then, interconnected nanotube networks are converted to equivalent resistor circuits. Finally, Kirchhoff's current law is used in conjunction with FEA to solve for the voltages and currents in the system and thus calculate the effective electrical conductivity of the nanocomposite. The model accounts for electrical transport mechanisms such as electron hopping and simultaneously calculates percolation probability, identifies the backbone and determines the effective conductivity. Monte Carlo analysis of 500 random microstructures is performed to capture the stochastic nature of the fibre generation and to derive statistically reliable results. The models are validated by comparison with various experimental datasets reported in the recent literature.

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Section: (Ii) Thermal Resultsmentioning

confidence: 99%

Estimation of the physical properties of nanocomposites by finite-element discretization and Monte Carlo simulation

Spanos

Elsbernd

Ward

et al. 2013

Phil. Trans. R. Soc. A.

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“…(39). Due to the linearity of all the equations governing the heat release and diffusion in the system, the NP temperature at any time t after a series of N pulses at the repetition rate f is: Figure 8 plots the results of numerical simulations for two cases corresponding to two different NP radii.…”

Section: H Influence Of the Pulse Repetition Ratementioning

confidence: 99%

“…It has been demonstrated experimentally that an interface resistivity at the gold/water interface exists and can play a significant role in the heat release. [38][39][40][41] The interface resistivity can reach appreciable values when the liquid does not wet the solid. The wetting depends on the nature of the interface, and in particular a possible molecular coating.…”

Section: A Physical Systemmentioning

confidence: 99%

Femtosecond-pulsed optical heating of gold nanoparticles

2011

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We investigate theoretically and numerically the thermodynamics of gold nanoparticles immersed in water and illuminated by a femtosecond-pulsed laser at their plasmonic resonance. The spatiotemporal evolution of the temperature profile inside and outside is computed using a numerical framework based on a Runge-Kutta algorithm of the fourth order. The aim is to provide a comprehensive description of the physics of heat release of plasmonic nanoparticles under pulsed illumination, along with a simple and powerful numerical algorithm. In particular, we investigate the amplitude of the initial instantaneous temperature increase, the physical differences between pulsed and cw illuminations, the time scales governing the heat release into the surroundings, the spatial extension of the temperature distribution in the surrounding medium, the influence of a finite thermal conductivity of the gold/water interface, the influence of the pulse repetition rate of the laser, the validity of the uniform temperature approximation in the metal nanoparticle, and the optimum nanoparticle size (around 40nm) to achieve maximum temperature increase.

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“…External heat flux in the system, Q ext , is nearly proportional to linear thermal driving force, with a heat transfer coefficient, h, as the proportionality constant: (9) In this case the energy balance in Eq 5 simplifies to: (10) Introducing a dimensionless driving force temperature, θ, scaled using the maximum system temperature, T max , (11) and a sample system time constant τ s (12) which are substituted into eq 10 and rearranged to yield (13) When laser irradiation ceases, Q I +Q o =0 and the system cools, reducing eq 13 to (14) Eq 14 may be solved using the initial condition θ = 1 at t = 0 to give (15) During laser irradiation, Q I +Q o is finite and system temperature rises to a maximum value when external heat flux given by eq 9 equals heat input via laser transduction given by eq 6: (16) Substituting eq 16 into eq 13 gives (17) Eq 17 can be solved using the initial condition θ = 0 at t = 0 to give (18) …”

Section: Heat Transfer Equation For Sample Cell and Contentsmentioning

confidence: 99%

Microscale Heat Transfer Transduced by Surface Plasmon Resonant Gold Nanoparticles

2007

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Visible radiation at resonant frequencies is transduced to thermal energy by surface plasmons on gold nanoparticles. Temperature in ≤10-microliter aqueous suspensions of 20-nanometer gold particles irradiated by a continuous wave Ar+ ion laser at 514 nm increased to a maximum equilibrium value. This value increased in proportion to incident laser power and in proportion to nanoparticle content at low concentration. Heat input to the system by nanoparticle transduction of resonant irradiation equaled heat flux outward by conduction and radiation at thermal equilibrium. The efficiency of transducing incident resonant light to heat by microvolume suspensions of gold nanoparticles was determined by applying an energy balance to obtain a microscale heat-transfer time constant from the transient temperature profile. Measured values of transduction efficiency were increased from 3.4% to 9.9% by modulating the incident continuous wave irradiation.

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Colloidal metal particles as probes of nanoscale thermal transport in fluids

Cited by 285 publications

References 31 publications

Estimation of the physical properties of nanocomposites by finite-element discretization and Monte Carlo simulation

Estimation of the physical properties of nanocomposites by finite-element discretization and Monte Carlo simulation

Femtosecond-pulsed optical heating of gold nanoparticles

Microscale Heat Transfer Transduced by Surface Plasmon Resonant Gold Nanoparticles

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