Heat Transfer Effects in Nano-Aluminum Combustion

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1 Paper # 070DE-0104 Topic: Detonations, Explosions, and Supersonic Combustion 8 th U. S. National Combustion Meeting Organized by the Western States Section of the Combustion Institute and hosted by the University of Utah May 19-22, 2013 Heat Transfer Effects in Nano-Aluminum Combustion David Allen, Herman Krier,& Nick Glumac Department of Mechanical Engineering, University of Illinois Urbana-Champaign, 1206 W Green St. Urbana, IL Nano-aluminum combustion remains poorly understood, as evidenced by recent paradoxical measurement results. While nanoscale Al is often assumed to burn in the kinetic limit, optical temperature measurements of nano-aluminum particles combusting in a heterogeneous shock tube at pressures near 20 atm showed peak temperatures above 3000 K in ambient air at 1500 K. This temperature overshoot and the simultaneous burning times measurements greater than 100 μs cannot be described from an energy balance considering continuum heat transfer losses from the particle. A conservative estimate assuming all reaction energy is used for sensible heating of the aluminum particle requires complete combustion in less than 1 μs to reach peak temperatures of 3000 K suggesting deviation from continuum mechanics. Similar heat transfer effects are seen in laser induced incandescence (LII) experiments of various nano-particles. In the shock tube and LII experiments the particle Knudsen number is near unity and non-continuum heat transfer effects and slip conditions can be expected. Previous work by Igor Altman suggests a formula which predicts an upper limit on the energy accommodation coefficient (EAC) which characterizes the energy transfer between a gas particle and surface. A simple model considering surface combustion and an energy balance using various heat transfer loss mechanisms shows that an upper limit of for the EAC predicted by Altman s theory agrees well with experimental data. An EAC of produces a best fit of the thermal profile for varying nano-aluminum particle sizes and predicts the temperature overshoot at high pressures. The decrease in the EAC for nano-aluminum has significant implications in the application and modeling of nano-aluminum used in explosives and solid rocket motors where ideally the particle transfers energy to the surrounding atmosphere. In such cases of low EAC radiation plays a more significant role in particle heat transfer. 1. Introduction Nano-aluminum combustion is of interest because of its proven ability to increase the burn rate in propellants and its potential for use in enhancing explosives. Fine metal particles are known to be highly reactive. However, replacing micron sized aluminum particles with nano-aluminum in explosive applications has been met with contradictory results. In certain applications the use of nano-aluminum has been shown to increase the detonation velocity, while other studies have shown little effect [5]. The mechanism for nano-aluminum combustion is still not fully understood and is the subject of much debate [7,14,15]. For nano-scale particles in most environments the transition from continuum mechanics to the free molecular regime of particle and gas phase interaction occurs. This transition in particle interaction plays a potentially dominant role in the particle temperature and heat transfer characteristics, but has yet to be experimentally investigated in nano-aluminum combustion. A 20 nm aluminum particle is approximately 100x the size of a surrounding air molecule and Knudsen numbers greater than one are easily achievable at combustion temperatures and pressures. These effects are considered in laser induced incandescence experiments to determine particle size distributions of nano-particles in flows [8] and must also be considered when determining the transient thermal profile of a combusting nano-aluminum particle. Theoretical and experimental work performed by Altman suggests that at high particle and gas temperatures, nanoparticles have very small energy accommodation coefficients and become conductively isolated from the ambient gas [1, 2]. Radiation consequently becomes a more significant pathway for heat transfer in the low accommodation coefficient regime. The experimental work by Altman et al. was performed using laser irradiation to heat up nanoparticles generated in a flame. The energy accommodation coefficient was found to be near which agreed nicely with their theoretical upper estimate [2]. Nano-aluminum particle combustion may potentially experience a similar thermal isolation effect in many applications. With such low accommodation coefficients the heat transfer from the particle via

2 collision with gas molecules becomes inefficient, leading to particle temperatures much higher than those expected using continuum regime expressions. In this work we perform experiments on nano-aluminum combustion to monitor the particle temperature, burn rate, and emission spectra. High particle temperatures and longer burn times are expected for a transition to the free molecular regime accompanied by a low energy accommodation coefficient. For ultrafine particles, classical theory predicts that rapid heat transfer results in combustion temperatures that only minimally exceed the ambient temperature, even when common Knudsen number correlations are used for Nusselt number calculation [8]. Prediction of the particle temperature requires specification of the reaction rate (i.e. heat release rate) in addition to the heat transfer coefficient. In this research the experimental data on burning time and temperature are supported by a simple model of nano-aluminum combustion that employs as few limiting assumptions as possible, focusing only on the energy balance leading to particle temperature rise. Multiple heat transfer models are considered to determine the predicted transient particle temperature and to see if the nano-aluminum particle experiences thermal isolation from the surrounding gas. 2. Experimental Set-up The nano-aluminum particles were investigated using a heterogeneous shock tube described in detail in a previous publication [12]. The shock tube is capable of producing controlled high temperature and high pressure environments with various gas compositions. Temperatures greater than 4000 K and pressures above 30 atm are achievable behind the reflected shock with tests times near 2 μs. Low temperatures were used for this study to test nano-aluminum combustion which has been shown to ignite at temperatures below 2000 K [3]. The combustion of the nano-aluminum particles was monitored behind the reflected shock in order to achieve the high pressures desired. The pressure was varied between atm to determine the effect of the oxidizer concentration on the particle temperature and burn time. The shock tube has an 8.4 m driven section and an 8.9 cm diameter. A converging duel diaphragm system separates the high pressure helium gas from the oxidizing environment. The driver to driven pressure ratio is controlled to produce the desired shock strength. The velocity of the shock is measured using four piezoelectric pressure transducers at different axial locations. Figure 1 shows an example of the pressure transducer traces which give very accurate time of arrival measurements. The ambient temperature and pressure of the gases trailing the incident and reflected shock are calculated with the Gordon-McBride equilibrium code [13] using the known initial driven pressure, composition, and measured shock velocity. The test time of the shock tube can also be found from the pressure transducer traces and is typically near 2 ms. Figure 1 Example piezoelectric pressure transducer signals used as time of arrival measurements to calculate the shock velocity. 2

3 A schematic of the shock tube operation with radial injection and the end section with fiber optic view ports is shown in Figure 2. This end section allows for photodiode access at multiple axial locations. The end wall has a sapphire view port for further optical access. The fiber optic section is described in further detail in previous publication [3]. Each test was run with three photodiodes monitoring different axial locations centered at the location of aluminum particle stagnation behind the reflected shock. A nano-aluminum particle has a very small Stokes number, and therefore the particle accelerates quickly behind the incident shock and stagnates instantaneously behind the reflected shock. For this reason particle motion behind the reflected shock is neglected. Figure 2 Schematic of shock tube test section with fiber optic access Particles are injected radially into the test gas prior to diaphragm rupture using a pneumatically driven piston. The particles become entrained in the gas flow behind the incident shock and are swept towards the end-wall until the reflected shock stagnates them and they combust. Four particle classes were chosen to vary particle diameter while measuring burn time, temperature, and emission spectra. A Hitachi S-4700 high resolution scanning electron microscope (SEM) was used to accurately characterize the particle size distribution of each sample. Over 100 particle diameter measurements were made from each sample in order to obtain a distribution. Table 1 shows the number average and mass average particle diameters of each nominal sample powder. The 18 nm particles were not characterized because the resolution required to characterize these particles accurately was not achievable. Any distribution obtained would have been biased towards the larger particles which were readily resolved while the smaller particles below 18 nm would not have been accounted for. The highest resolution images achieved qualitatively showed the 18 nm particle distribution to be significantly smaller than the other sample distributions even though an accurate average could not be quantified. Particle Size (nm) Supplier Designation Particle Type Table 1 Particle Classes Number Average Particle Diameter (nm) Mass Average Particle Diameter (nm) 18 SkySpring Nanomaterials Uncharacterized Uncharacterized 50 SkySpring Nanomaterials NovaCentrix NovaCentrix Each sample had a large distribution of particle sizes. Figures 3-5 show the histograms of the three characterized samples and Figure 6 shows a corresponding SEM image for the 80 nm particles. All particles imaged were found to be highly spherical. Nano-aluminum has previously been shown to form both weak and strong agglomerates dependent on the manufacturing process. The images show the particles acquired have weak agglomeration and very little particle necking. The particles acquired from NovaCentrix are specified to be 80-90% aluminum and the 3

4 More Frequency Frequency Frequency oxide-coating thickness is nm. Both samples acquired from SkySpring Nanomaterials are 99.9% pure on a trace metals basis nm Histogram nm Histogram Particle Size (nm) Figure 3: Histogram of 50 nm SkySpring Nanomaterials Particles Particle Size (nm) Figure 4: Histogram of 80 nm NovaCentrix particles 110 nm Histogram Particle Size (nm) Figure 5: Histogram of 110 nm NovaCentrix particles Figure 6: SEM Image of 80 nm NovaCentrix particles Emission spectra were collected using an Ocean Optics spectrometer with a 10 µm inlet slit and a nm range. The spectral resolution of the spectrometer was approximately 1nm full-width-half-maximum (FWHM). The spectra were time integrated over the entire combustion event; a shutter was used to avoid emission from particles after the test time of the shock tube. The spectrometer was intensity calibrated using a tungsten calibration lamp with a filament temperature of 3200 K. Pyrometry was performed using a custom built 3-color pyrometer. A trifurcated fiber bundle images the light from the test section through a narrow slit. The trifurcated cable splits the light collected from the test into three separate branches. Each branch passes through a collimator, interference filter, and to a photomultiplier. The interference filters have a 10 nm FWHM bandpass centered at 705, 826, and 905nm. The shorter wavelengths are measured using Hamamatsu R928 photomultipliers; the 905 nm light is measured using a R photomultiplier with an infrared sensitive GaAs photocathode. Outputs of the photomultipliers are amplified using a Stanford Research Systems Quad preamplifier unit with a 300 MHz bandwidth. The time response of the pyrometry system is sub-microsecond. The system is calibrated using a tungsten calibration lamp. Noise level is typically 10-20% of the signal and the measurement uncertainty has previously been estimated to be ±150 K for micron sized particles [6]. 4

5 Intensity Photometric burning time measurements were made using photodiodes coupled to different axial locations using the fiber optics in the end section shown in Figure 2. Three Thor Labs PDA36A amplified photodiodes were used to capture the luminosity from each experiment. The emission was unfiltered because the spectroscopic measurements indicated no molecular emission. Time resolution of the photodiodes is sub-microsecond. 3. Experimental Results Previous measurements by Bazyn et al. have shown weak or no molecular aluminum monoxide (AlO) emission at pressures below 32 atm for nano-aluminum combustion [3]. Figure 7 shows the emission results from the current tests for 80 nm particles at 1550 K in an air. Emission spectra were similar for all sample sizes, and were integrated over the entire combustion event. Each spectrum showed only thermal radiation and no significant gas phase emission, indicating that the primary combustion mechanism involves surface reactions as hypothesized. If gaseous aluminum were present, atomic aluminum lines would likely be seen at and nm and banded aluminum monoxide (AlO) emission from the B-X transition would likely be seen in the range from nm. It is possible that the gas phase species are present but are not at high enough temperatures to cause detectable spontaneous emission. However, measurements by Lynch et al. showed no aluminum or aluminum monoxide absorption below 1500 K which further suggests that gas phases are not present in significant amounts [9]. The lack of gas phase emission is consistent with the a reaction model in which surface processes limit the combustion rate Wavelength (nm) Figure 7: The emission spectrum of 80 nm aluminum particles at 1500 ±50 K and 20 atm in 20% O 2 and 80% N 2 ambient conditions. Figure 8 shows a comparison of characteristic intensity plots in an air for each particle size. Multiple tests were run for each particle class and the average burn time can be seen in Table 2 using the 10%-90% area method to calculate the burn time. In order to calculate the 10%-90% area burn time the background intensity in the post combustion environment due to heated particles was subtracted off following the method described in a previous publication [3]. In short, a linear rise is fit from initial noise level to the background intensity level. The rise is assumed to occur from the onset of luminosity to the point of peak intensity where it then remains constant. There is some deviation from the average due to the large particle distribution and also due to the inherent uncertainty in determining the background intensity levels. More importantly, however, is that the luminosity traces show that as the average particle diameter increases, the photodiode trace becomes wider and the time it takes to reach peak intensity becomes longer. The burning time of the particles spans from μs consistently for all samples. It is important to consider the interpretation of the luminosity traces. The emission spectrum showed no evidence of molecular emission and therefore, the luminosity profiles being shown cannot be directly correlated the presence of aluminum monoxide as has previously been done. When monitoring molecular emission such as AlO the 5

6 Normalized Intensity assumption is that the luminosity trace corresponds to AlO presence which indicates combustion because AlO is a key intermediate species in the gas phase combustion process. However, in this case the luminosity is due to thermal radiation evidenced by the emission spectra. Therefore we are correlating burn time to particle temperature with a different assumption than that made when monitoring emission filtered around an aluminum monoxide band as is normally the case for micron sized particles [10]. 18nm 50nm 80nm 110nm Time (ms) Figure 8: Example photodiode traces of luminosity at 1500 ±50 K and 20 atm in 20% O 2 80% N 2 Table 2: Average particle burning time calculated using the 10%-90% area burn time method of calculation Environment 1 Burn Time Nominal Size (nm) Average (µs) Standard Dev (µs) The values for the burn time seen in Table 2 compare favorably with those seen previously by Bazyn et al. in similar conditions and are shorter than those seen in micron sized aluminum at higher temperature. Bazyn found burn times near 170 µs for 80 nm aluminum particles in 20% O 2 and 80% N 2 at higher pressures [3]. Micron sized aluminum particles do not ignite at 1500 K and therefore cannot be directly compared to the current data. The ignition temperature for micron sized particles is approximately 2300 K. At 2650 K and 8.5 atm in an air simulated environment the burn time for micron size particles in the shock tube was previously found to be greater than 200 μs, which is still greater than the values seen for nano-aluminum at much lower temperatures [10]. The three color pyrometry measurements indicated a rise in particle temperature above the ambient gas temperature. Multiple tests were performed for each particle size at 1500 K and 20 atm in an air simulated environment. Figure 9 shows a sample of the three filtered photomultiplier signals and the corresponding three color pyrometry temperature. Figure 10 shows the peak temperature calculated at the location of peak intensity plotted against number average particle diameter. The temperature rise at high pressures is due to the increase in local number density of oxygen at the surface of the particle which scales linearly with pressure. The average temperature remains relatively constant for all particle sizes. The scatter in the measurement is due to the combination of large measurement uncertainty (±150 K), high signal to noise ratio, and short luminosity time scales. 6

7 Temperature Intensity Temperature (K) Color Time (ms) Figure 9: Sample 705 nm, 826 nm, and 905 nm pyrometry signals and calculated three color temperature at ambient conditions of 1500 ±50 K and 20 atm in 20% O 2 and 80% N Peak Temperature vs. Particle Size Number Average Particle Diameter (nm) Figure 10: Peak temperature vs. particle size at 1500 ± 50 K and 20 atm in 20% O 2 80% N 2. The average peak temperature is between K for all cases. Bazyn et al. similarly found peak temperatures for nano-aluminum combustion near 2500 K for 8.5 atm pressures and 3500K for 32 atm pressures. The values of the peak temperatures at 20 atm found in these tests range between the values found by Bazyn. In a surfaceprocess limited combustion mechanism the peak temperature is linearly dependent on the pressure. The peak temperature increases with increasing pressure because of the larger number density of oxygen at the surface of the aluminum particle. A low pressure test performed at 3.5 atm gave a peak temperature of 2375 K which is again consistent with the trend predicted by the surface-process limited combustion model. The high peak temperatures suggest that the particles are experiencing free molecular regime heat transfer effects. If continuum heat transfer losses (Nu~2 for a stationary particle) are assumed, the nano-aluminum particle would need to fully combust in less than 1µs in order for the particle to reach 3300 K in the ambient conditions of the experiment. Clearly the luminosity traces suggest this is not the case. This finding indicates a significant transition to the free molecular regime of particle interaction which will be further considered in the following sections. 7

8 4. Model Description A nano-aluminum combustion model was developed to further investigate the heat transfer of the particles. The model is an energy balance of the particle that assumes a surface-process limited combustion mechanism and heat transfer through conduction to the ambient gas and radiation to the walls of the shock tube at 300 K. Figure 11 renders facets of the combustion model. The reaction surface is the initial surface and area of the particle and does not change because diffusion of oxidizer and fuel are assumed to occur much faster than the limiting surface process (i.e. surface diffusion or chemical reaction). Figure 11 Depiction of the surface process nano-aluminum combustion and heat transfer model The influx of heat to the particle is assumed to be due to the chemical reaction between the oxygen and aluminum following Equation 1 which releases 1.85(10-18 ) joules of energy for each collision of oxygen molecule resulting in reaction with the aluminum surface. The rate of reaction is calculated using the number density of oxygen at the particle surface (Eq. 2), thermal velocity (Eq. 3), the collision frequency (Eq. 4), and a sticking probability ϕ which is a variable parameter in the model. The sticking probability represents the percentage of collisions that react and has a value between zero and one. A sticking probability of zero represents no reaction, and a sticking probability of one indicates each collision results in chemical reaction. The sticking probability is assumed to remain constant during the combustion of the aluminum particle. Equations 2-6 represent the calculation for the heat due to the reaction. An effective area is used for the influx of heat to model the reduction in the amount of aluminum surface area at the reaction surface during combustion. The effective area is determined by calculating the surface area of the remaining mass of aluminum at each time step as if it were a sphere. The assumption is that the reaction occurs at the outer surface of the particle, but as the particle burns the amount of reactive area decreases as a function of time following Eq. 5. ( ) Eq. 1 Eq. 2 ( ) Eq. 3 ( ) Eq. 4 ( ) ( ) Eq. 5 Eq. 6 8

9 The conduction of heat to the surrounding gas is the key effect of interest in the present study. As previously mentioned, Altman predicts a decrease in the accommodation coefficient at high temperatures. Altman puts an upper limit on the accommodation coefficient following Eq 7, where is the Debye temperature of the solid. For the conditions in this study Eq. 7 puts the upper limit of the accommodation coefficient at approximately The heat transfer to the surroundings through conduction in the free molecular regime is calculated following Equation 8. Equation 9 shows the corresponding heat transfer equation assuming continuum mechanics where k is the thermal conductivity of the gas, Nu is the Nusselt number, and d is the particle diameter. The radiation to the walls follows the Stefan-Boltzmann law using Eq. 10 with an emissivity of 0.1 for alumina [11]. The temperature of the particle changes as shown in Eq. 11. ( ) Eq. 7 ( ) ( ) ( ) Eq. 8 ( ) ( ) ( ) Eq. 9 ( ) ( ) Eq. 10 ( ) ( ) Eq Discussion The model has two unknown parameters which must be fit to experimental data. These parameters are the sticking probability and the energy accommodation coefficient. The two unknowns are fit by comparing the model peak temperature and burning time predictions to those measured in the experiments. Figure 12 shows the model predictions graphically. The burn time calculated in the model is independent of the heat transfer of the particle with the assumptions provided and is plotted in black on the log scale. The particle is considered fully burned in the model once 90% of the original mass is reacted. A sticking probability of zero represents no reaction and therefore the burn time asymptotes towards infinity at this value. The particle temperature increases with increasing sticking probability as expected because a larger percentage of the collisions result in exothermic heat release. Seven potential temperature profiles are plotted for comparison, each using a different accommodation coefficient or heat transfer assumption. Five accommodation coefficients, a correlation for the Nusselt number deduced from laser induced incandescence on nano-particles [8], and a Nusselt number of 2 are considered. For a given sticking probability the burning time and particle temperature considering one of the seven heat transfer models satisfy the energy balance of the system. It is evident that an accommodation coefficient between is necessary in order to achieve burn times greater than 100 µs and peak temperatures of ~3300. For a sticking probability of and an energy accommodation coefficient of the burn time is 182 µs (Point A) and the temperature is 2728 K (Point B) as seen in Figure 12. The best fit values for the accommodation coefficient and sticking probability are and respectively. The values obtained for the accommodation coefficient here match extremely well with the value of found experimentally by Altman in previous work. These results clearly suggest that the nano-aluminum particles are experiencing thermal isolation from the ambient gas due to low energy accommodation coefficients. In order to achieve temperatures greater than 3000 K using a Nusselt number of two the model predicts a burn time less than 1 μs in order to satisfy the energy which is clearly not seen in experiment. 9

10 Burn Time ( s) Peak Temperature (K) Burn Time ( s) Peak Temperature (K) A Burn Time =0.001 =0.005 =0.05 =0.5 =1.00 Nu=0.3/Kn Nu= Burn Time =0.001 =0.005 =0.05 =0.5 =1.00 Nu=0.3/Kn Nu= B Sticking Probability Figure 12: Model results for burn time and peak temperature at ambient conditions of 1500 K and 20 atm for sticking probabilities between Sticking Probability Figure 13: Model results for burn time and peak temperature at ambient conditions of 1500 K and 20 atm for sticking probabilities between The luminosity of the particles can be further compared to the model by scaling the predicted transient temperature profile by continuum, ie. T 4, and normalizing it for comparison. Figures show the predicted luminosity compared to photodiode traces from the experiment at 1500 K and 20 atm in air. For these traces the best fit accommodation coefficient of and sticking probability of were chosen. The model matches the experimental results with very good accuracy. 10

11 Intensity (arb) Intensity (arb) Intensity (arb) Intensity (arb) 1.0 Experimental Model 18 nm Particles Experimental Model nm Particles Time ( s) Figure 14: Model radiation for 18 nm particles at 1500 K and 20 atm in air compared to experimental data Time ( s) Figure 15: Model radiation for 50 nm particles at 1500 K and 20 atm in air compared to experimental data 1.0 Experimental Model 80 nm Particles Experimental Model nm Particles Time ( s) Figure 16: Model radiation for 80 nm particles at 1500 K and 20 atm in air compared to experimental data Time ( s) Figure 17: Model radiation for 110 nm particles at 1500 K and 20 atm in air compared to experimental data The model predicts the peak temperature for each particle size to be approximately 3400 K for each particle size. The peak temperature does not change significantly in the model with particle size because all modes of heat transfer scale similarly. The heat loss scales with d 2 and the heat due to chemical reaction scales with the effective area, but the peak temperature occurs near the start of the particle reaction when the effective area is very near the initial area of the particle. The transient temperature profiles change with particle size due to the effective area approximation as seen in Figures The model prediction that the peak temperature remains constant independent of the particle diameter agrees with the experimental data shown in Figure 10 within some uncertainty. The comparison extends to lower pressures as well. The model predicts a peak temperature of 2600 K at a pressure of 3.5 atm using the accommodation coefficient (0.0035) and sticking probability (0.0009) as those used to fit the data at 20 atm. Pyrometry measurements found the temperature to be 2375K at 3.5 atm. The model slightly over predicts the temperature, but the accommodation coefficient used was fit to a system with the peak particle temperature of 3100 K and an ambient gas temperature of 1500 K. In the case of lower pressure, the peak temperature is lower and following Eq. 6, a lower particle temperature implies a higher accommodation coefficient. Scaling the accommodation coefficient by this difference in expected temperature brings it to a value of approximately Using this value further drops the predicted temperature to 2500 K which is closer to the experimental value of 2375 ±150 K. Clearly the accommodation coefficient may vary as the particle temperature changes during the combustion event, but here it is assumed constant. Figure 18 shows the model radiation of an 80 nm particle in an air simulated environment at 3.5 atm 11

12 Intensity (arb) and 1500 K using the scaled accommodation coefficient of and a sticking probability of as before. The model accurately explains the experimental photodiode trace. 1.0 Experiment Model Time ( s) Figure 18: Model radiation for 80 nm particles at 1500 K and 3.5 atm in air compared to experimental data using an accommodation coefficient of and a sticking factor of Conclusion It has been shown that in order for nano-aluminum particles to achieve the burning time and peak temperatures observed experimentally, the heat transfer of the particles occurs in the free molecular regime of particle and gas phase interaction. This effect was theoretically described by Altman and shown to be true in experiments using laser irradiation. In those experiments the energy accommodation coefficient was found to be approximately It has been shown here, that the best fit for the energy accommodation coefficient is , which agrees very well both with the experiments performed by Altman and the upper estimate described previously in Eq. 6. It is therefore concluded that at the high ambient temperatures recorded in the shock tube, the particles are experiencing thermal isolation from the surrounding gas. This effect has important consequences for nano-aluminum combustion. If the particles are conductively isolated from the ambient gas the increase in performance will likely be much less in rockets and explosives. Radiation from the particles to the ambient gas will be the primary mode of energy transfer. It is expected that similar results will arise for other metal condensed phase nano-particles combusting at high temperatures. Clearly, this result applies only to the solid-gas phase heat transfer interaction, although it does apply to condensed phase products of gaseous reactions if the particles are sufficiently small and the ambient temperature is sufficiently high. Acknowledgements This research was funded by DTRA under grant HDTRA The project manager is Dr. Suhithi Peiris. The Scanning Electron Micrographs were taken in the Frederick Seitz Materials Research Laboratory Central Facilities, University of Illinois, which are partially supported by the U.S. Department of Energy under Grant DEFG02-9-ER Special thanks to Ed Dreizin and Michael Zachariah for helpful discussion on the issue of temperature overshoot. Thanks also to Sam Goroshin for providing us with the references on thermal isolation which made this work possible. References 1. I. Altman, D. Lee, J. Song and M. Choi, Experimental estimate of energy accommodation coefficient at high temperature, The American Physical Society, vol. 64, no. 5,

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