Isotope Generated Electron Density in Silicon Carbide 

Direct Energy Converters 

by Marc Litz and Kara Blaine 

ARL-TR-3964 October 2006 

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ARL-TR-3964 October 2006 

Isotope Generated Electron Density in Silicon Carbide 

Direct Energy Converters 

Marc Litz and Kara Blaine 

Sensors and Electron Devices Directorate, ARL 

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Isotope Generated Electron Density in Silicon Carbide Direct Energy 

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14. ABSTRACT 

SiC has been investigated for use as a direct energy converter (DEC) for nuclear batteries. A solid-state model is being 

developed to calculate the electrical output of a diode into a resistively loaded circuit. This paper describes the use of a 

nuclear scattering code (MCNPX) to calculate the increased electron density that would be expected in a SiC material based 

on exposure to a Sr90 beta emitter. An incident beta (average 125 keV) generates on the order of 27k free electrons/cc per 

incident Sr90 electron. For each incident electron, and average of 9 keV is deposited in the SiC. The results of this effort will 

be fed into the Schottky device numerical model to calculate the predicted power from the device. 

15. SUBJECT TERMS

DEC, SiC, nuclear battery 

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Marc Litz 

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iii 

Contents 

List of Figures iv

1. Background 1

2. Materials 2

3. Numerical Simulations 3

3.1 Geometry.........................................................................................................................3 

3.2 Beta Source......................................................................................................................4 

4. Results 4

5. Conclusions 9

6. References 11

Appendix A. MCNPX Input Deck 13

Distribution List 15
iv 

List of Figures 

Figure 1. Schematic diagram of geometry of input deck of MCNPX. .......................................... 4 

Figure 2. Number density contour plot for (from left to right) (a) SiC, (b) Al, and (c) Si. The 

view is 5 mm by 0.15 mm, and the slice is 0.15 mm thick, representing 30-5 μm partiallayers. The binning is stored in such a way that each data point measures 250 μm by 8 μm 

by 150 μm. Closer inspection shows that the peak number density decreases by 

approximately 20% in each case............................................................................................ 5 

Figure 3. Number density plot for (from top to bottom) (a) SiC, (b) Al, and (c) Si. The data is 

taken along the central line of the layers, where each point represents 250 μm by 8 μm by 

150 μm. The data is shown with exponential fits. The point shown in the lower right of 

each plot is the exponential fit to the order of magnitude fall off from the starting point.... 6 

Figure 4. Peak electron number density (a) data gathered from the Number Density Plots. The 

exponential fit was was extrapolated to zero, and the zero points, or starting points of the 

materials were plotted with respect to the density of the material. Electron range (b) data 

shows the point at which the density is 10% of the value at the surface of the material........ 7 

Figure 5. Energy spectrum of number density of electrons depositing energy into layers 1 

through 3 of the 5 μm thick SiC layers.................................................................................. 8 

Figure 6. Input beta spectrum from Strontium 90 is peaked at 125 keV. This spectrum is the 

input modeled in the MCNPX simulation............................................................................... 9 
1 

1. Background 

Nuclear batteries can have a small but significant impact as a power source for long-lived 

sensors (1-3). For example, consider embedded sensors for bridges and foundations of large 

buildings where these sensors can provide information about the structural integrity (4,5). 

Unattended sensors for oceanic temperature measurement would need no maintenance during an 

increased operational lifetime. Unique opportunities are waiting for sensor applications that 

would benefit from keep-alive power lasting 30 years. 

Chemical power sources can store the total energy required, but electrical leakage and 

deterioration stops them from sustaining the requisite power over long lifetimes. Nuclear 

batteries offer a unique source to fill the niche. Even though the energy storage density of 

nuclear isotopes is six orders of magnitude greater than that possible in chemical bonds, the 

stigma associated with nuclear materials will require careful design and thoughtful application. 

Sensors embedded in structures, providing information useful to monitor structural integrity may 

be just the application to overcome. The negatives often associated with nuclear applications. 

MCNPX is a general-purpose Monte Carlo N-Particle code which supports 34 particle types, 

contains cross-section libraries, and includes the ability to use physics models for energies where 

tabular data are not available (6). MCNPX treats an arbitrary three-dimensional configuration of 

materials in geometric cells. Pointwise continuous-energy cross section data for scattering in 

materials are used. Source and tally structures with extensive statistical analysis of convergence 

are integral to the output parameters. Rapid convergence is enabled by a wide variety of 

variance reduction methods. MCNPX includes libraries for neutrons, photons, electrons, protons 

and photonuclear interactions. “Mesh” and “radiography” tallies are included for 2- and 

3-dimensional imaging purposes. An auxiliary program, GRIDCONV, converts the mesh and 

radiography tally as well as standard metal-file results for viewing by independent graphics 

packages. 

Monte Carlo algorithms calculate a statistically valid result of the most likely results of a set of 

operations (7). These algorithms are useful if you want to ask what will be the outcome of a 

series of events. This is particularly useful in complex series of molecular motions or a 

sequential series of events, as in photon scattering through a material. Each event has its own 

probability of occurrence. It can take days to run, in order to test as many events and outcomes 

as possible. These algorithms use random number generators to create the space for variety in 

the complexity and follow with variance reduction techniques to develop statistics that give 

guidance as to the value or weight of the answer. Credit for inventing the algorithm often goes 

to Stanislaw Ulam. Unlike Monte Carlo simulation, deterministic transport methods require that 

the phase-space be discretized in space, energy and angle, introducing a substantial degree of 

complication in terms of algorithm development, problem set-up, and requires a significant 
2 

amount of computer memory in order to store the spatial, angular and energy dependent flux 

information. The large demand placed on computer memory by deterministic transport codes 

has limited their use. However, with the rapid increase in computer processing power, memory 

capacity, and reduction in unit cost, three-dimensional, time-independent, deterministic transport 

codes are now becoming available as production codes (8). 

This paper describes the MCNPX simulations developed to determine the free electron density 

generated in bulk SiC layers. The β-particle source stimulation modeled in the numerical 

simulation is the spectrum of β emitted from the Sr90 isotope. The electron density gradient 

calculated in SiC is then used as initial conditions for a drift-diffusion numerical model of a 

Schottky diode. 

Section 1 provides the motivation for our efforts and discusses some of the applications that will 

benefit from the effort. Section 2 discusses SiC and the devices developed for this application. 

Section 3 details the geometry model input to the monte-carlo code. Sections 4 details the 

significant results and section 5 provides conclusions and impact of this part of the larger effort. 

2. Materials

Silicon Carbide (SiC) is a robust radiation-hard solid-state material that can be fabricated into 

many device formats. For these reasons it is worth pursuing as a direct energy converter (DEC) 

from radiation (alphas, betas, or gammas) into free electrons that can be used as a source of 

electrical current. A parametric study of both materials and device geometry is underway. 

Materials under investigation include silicon (Si -2.3 g/cc), SiC (3.2 g/cc), gallium nitride (GaN -

6.15 g/cc), and CVD Diamond (3.52 g/cc). Device geometries are not identical for all materials, 

however, PIN and Schottky geometries have been acquired. Both Si and SiC Schottky diodes 

are fabricated at the Army Research Laboratory. The diodes have been exposed to betas emitted 

from Strontium-90, with the voltage across the diode measured during controlled experiments. 

The irradiation of these devices from strontium-90 will be the focus of this paper. A more 

complete review of PIN diode structures will be part of future efforts. 

We composed the results of numerical simulations on Al, Si and SiC. While the Si and SiC 

simulations model devices were built and measured in experiments, the purpose of the aluminum 

(Al – 2.7 g/cc) is 1) to compare to the Si and SiC, and 2) serve as a reference material for 

electron range results. Tables of electron losses have been tabulated for a variety of materials 

including Al (9). The Berger and Seltzer report includes tables of range constants (g/cm2

) as a 

function of monochromatic beta energy. The incoming beta energy used as a comparison in our 

calculations is 150 keV betas, which is a good comparison for our 125 keV (average) beta 
3 

spectrum used in the MCNPX calculations. The range constant supplied in section 3.2 (9) must 

be multiplied by the density of the material used. The density of Al is shown in equation 1. 

cm m

cm g cm

g 1.3 10 130μ 2.71 3.64 10 2

2 3

2 × ⋅ = × = − − (1) 

The range of the aluminum hit by the constant energy 150 keV betas is shown to be 130 μm, as 

shown in equation 1, above, which is a good comparison to the 150 μm deep model used, which 

will be described in more detail in section 3.1. 

3. Numerical Simulations 

3.1 Geometry 

The MCNPX input deck that defines the geometry used for this study (see appendix A) created 

thirty rectangular parallelepipeds of material layers in a row, located 1 mm from a 0.5 mm in 

diameter beta source (see figure 1). The physical diode package, used in the experiments, 

measures 5 mm by 5 mm by 0.5 mm. However, our interest was to see the number of electrons 

generated in the top-most layers of Schottky diodes, which have a depletion layer of 

approximately 5 μm thick. Therefore, the layers used in the input deck measured 5 mm by 5 mm 

in the plane perpendicular to the beta source and 5 μm deep. All together, the thickness of the 

SiC totaled 150 μm to ensure a complete stopping range for the betas. 

It is essential to note that the diode interaction with the electrical circuit cannot be simulated 

within the MCNPX code. Instead, we are only modeling the SiC material and attempting to 

calculate how many free-electrons are being generated within as a function of depth in the 

material. A separate numerical solid-state code will be used in an additional study to analyze the 

electrical response of the diode with respect to an external circuit. This will be discussed in 

subsequent papers. 
4 

Figure 1. Schematic diagram of geometry of input deck of MCNPX. 

3.2 Beta Source 

The beta source coded into the MCNPX input deck (see appendix A) is an attempt to model a 

strontium-90 (Sr-90) beta emitter. There are several flaws with the MCNPX approximation, 

which are small, but necessary to recognize. Strontium-90 emits a beta spectrum up to 547 keV. 

However, the MCNPX input deck was simplified to an input spectrum of 500 keV, which peaks 

at 125 keV based on previous calculations. This was not a terrible assumption considering that 

the spectrum of electron energies emitted from the isotope is usually peaked at one third the 

maximum energy of the emitted beta (10). The beta input spectrum used to simulate the Sr90 

isotope is shown in figure 6. Furthermore, when low energy electrons enter the material, fewer 

additional electrons are generated, though the energy of the electrons may be more suitable to be 

captured by the depletion region and converted into usable power in a circuit. 

4. Results 

Contour plots of electron number density (#/cc), shown in figure 2, were generated for each of 

the three materials being evaluated: SiC, Al, and Si. The calculation using MCNPX gives 

results of energy deposited with units of MeV/g. The number density is calculated from these 

original results by a) multiplying by material density and b) dividing by the average particle 

energy (125 keV). The number density data was calcualted by recording MCNPX mesh 

diagnostic over a volume that spanned 5 mm by 0.15 mm by 0.15 mm. The SiC layer volume 

modeled in the MCNPX input deck occupies a total of 5 mm by 5 mm by 0.15 mm. In order to 

get a number density at the center of the beam, the mesh diagnostic was implemented to 
5 

calculate the energy deposited over a much smaller volume (at the beam center) than the entire 

layer. The binning of the mesh data cell was defined to be 250 μm by 8 μm by 150 μm. By 

taking a smaller mesh size, a more accurate description was obtained. The results of the contour 

plots show that the number density at the center of the beam is 27 k electrons/cc/incident 

electron. 

 

 a) b) c) 

Figure 2. Number density contour plot for (from left to right) (a) SiC, (b) Al, and 

(c) Si. The view is 5 mm by 0.15 mm, and the slice is 0.15 mm thick, 

representing 30-5 μm partial-layers. The binning is stored in such a way 

that each data point measures 250 μm by 8 μm by 150 μm. Closer 

inspection shows that the peak number density decreases by approximately 

20% in each case. 

Number density plots (figure 3) show a 1-dimensional horizontal cut across the contour plots of 

figure 2. They curves show more quantitatively, the number density values along the center-line 

(beam-line) in 1-dimension, compared to what is pictured in 2-D in the contour plots of figure 2. 

With the addition of a trend line to each data set, the number density is shown to decrease 

exponentially as electrons propagate the layers of SiC. Variation from the line may result from 

poor statistics, which, though satisfactory, could be improved with more histories numerically 

calculated (and longer run times). The statistical result of the mesh calculation is well below 

10% which provides a high degree of certainty. 

2000 2000 2000

2000 2000 000 4000 4000

4000

4000 4000 4000

6000 6000

6000

6000 6000

8000 8000

8000 8000

10000

10000

10000

12000

12000

1400

1400 160

18000

2000 2000 2000

2000 2000 2000 4000 4000 4000

4000 4000 4000

6000 6000

6000

6000 6000

8000 8000

8000

8000

10000

10000

10000

12000

12000

14000

14000

16000 18000 2000

5000 5000

5000

5000 5000

1000

10000

10000

15000

15000

20000

5000um, Δ 250μm 

 150um, Δ150μm 

 150um, Δ 5μm 

 X 

 Z 

 Y 
6 

Number Density Along Center Line of Silicon Carbide Layers

(165, 2.91E+03)

y = 29357e-0.014x

0.0E+00

5.0E+03

1.0E+04

1.5E+04

2.0E+04

2.5E+04

3.0E+04

3.5E+04

0 40 80 120 160 200 240

Depth into SiC (um)

Number Density (#/cc)

(a) 

Number Denisty along Center Line of Aluminum Layers

(197, 2.30E+03)

y = 23034e-0.0117x

0.0E+00

5.0E+03

1.0E+04

1.5E+04

2.0E+04

2.5E+04

0 40 80 120 160 200 240

Depth into Al (um)

Number Density

(b) 

Number Denisty Along Center Line of Silicon Layers

(209, 2.18E+03)

y = 21763e-0.011x

0.0E+00

5.0E+03

1.0E+04

1.5E+04

2.0E+04

2.5E+04

0 40 80 120 160 200 240

Depth into Si (um)

Number Density (#/cc)

(c) 

Figure 3. Number density plot for (from top to bottom) (a) SiC, (b) Al, and 

(c) Si. The data is taken along the central line of the layers, where 

each point represents 250 μm by 8 μm by 150 μm. The data is shown 

with exponential fits. The point shown in the lower right of each 

plot is the exponential fit to the order of magnitude fall off from the 

starting point. 
7 

We expected that the number of free electrons generated in the materials would be correlated to 

material density. They are not, however, linearly dependent on material density. With only 

three data points to compare, it is left for a future study, to better understand this result. One way 

to understand this discrepancy, is that not only was the material density changing, but so were 

the atomic scatterers. The density varied during the three calculations from 2.3, 2.7, and 

3.2 g/cc, while the atomic number of the materials also changed from Si(atomic mass 28.1), 

Al(atomic mass 26.9), and SiC. A linear approximation is still attempted to fit the density of free 

electrons generated by the Sr90 betas with changing density of the DEC material. This linear fit 

to the data is shown below in figure 4. 

The electron range of each of the three DEC materials is plotted (figure 4) as a function of the 

DEC material density. The electron range here is defined as 10% of initial number density at the 

surface. Before extrapolation, all the materials looked like they would have a similar fall off 

point, though closer inspection revealed that the number density has a linear relationship with 

respect to material density, just as the peak number density does. However, more data would be 

needed for a better evaluation. 

Peak Number Density

y = 8610.2x + 1183.3

2.0E+04

2.1E+04

2.2E+04

2.3E+04

2.4E+04

2.5E+04

2.6E+04

2.7E+04

2.8E+04

2.9E+04

3.0E+04

2 2.5 3 3.5

Density of Material (g/cc)

Number Density (#/cc)

Electron Range

y = -49.508x + 325.66

150

160

170

180

190

200

210

220

2 2.5 3 3.5

Density of Material (g/cc)

Depth into Material (um)

(a) (b) 

Figure 4. Peak electron number density (a) data gathered from the Number Density Plots. The 

exponential fit was was extrapolated to zero, and the zero points, or starting points of the 

materials were plotted with respect to the density of the material. Electron range 

(b) data shows the point at which the density is 10% of the value at the surface of the material. 

The spectrum of the electrons depositing energy in a layer is shown in figure 5. This graph 

clearly shows that the lower energy electrons are responsible for depositing most of the energy in 

the system. The spectrum shown is unlike the shape of the input Sr90 spectrum. The beta 

spectrum bombarding the SiC layers is shown in figure 6. The comparison of input beta 
8 

spectrum, and spectrum of electrons depositing energy in the material shows that the higher 

energy betas tend to travel through the material without leaving much energy behind. This 

would lead us to believe that the scattered, lower-energy (<100 keV) electrons are more 

significant in providing power to an external circuit. 

0 0 .1 0 .2 0 .3 0 .4 0 .5 0 .6 0 .7

1 0 - 3

1 0 - 2

1 0 - 1

1 0 0

1 0 1

1 0 2 T a lly S ix P lo t: C o n ve rte d to # /c c

E (M e V )

#/cc

Figure 5. Energy spectrum of number density of electrons depositing energy into layers 1 through 3 of the 

5 μm thick SiC layers. 
9 

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

1 0 -2

1 0 -1

1 0 0 Th e E n e rg y D is trib u tio n o f th e B e ta S o u rc e

E [M eV ]

Energy Deposited [#/particle]

E nergy input

Figure 6. Input beta spectrum from Strontium 90 is peaked at 125 keV. This spectrum is the input modeled 

in the MCNPX simulation. 

5. Conclusions 

The number density result calculated in MCNPX is the number of electrons generated for each 

incident electron. The Sr90 source used in our experiments is physically located at NSWCCarderock. It has an activity of 7mCi. This corresponds to 25e7 beta emissions per second (Bq). 

The resulting number density of electrons generated as a result of our experimental setup is then 

calculated in the SiC converter experiments to be 675e10/cc/sec. 

The range for betas emitted from Strontium 90 has been modeled using MCNPX. The results 

show that 150 μm of SiC stops all electrons emitted. A parametric study of target material was 

performed using MCNPX. Input decks for this numerical investigation differ only by material 

(Al, Si, and SiC) and their corresponding naturally occurring density. These results compare 

well with values generated by Berger and Selter (8) in which they tabulated the values of 

stopping range in Al, Si and Cu. 

Diagnostics available in the MCNPX simulations were compared for consistency. Surface flux 

results (tally 2) compare well with volume flux results (tally 4), in the limit as the thickness of 

the volume decreased to a surface. 
10 

When calculating the energy deposited in the material, tally 6 generates a result over a complete 

cell. When a more localized or discretized value is required (i.e., the value at the center of the 

beam), using the mesh command and identifying smaller effective volume regions works well 

and compares accurately to the averaged value over the larger volume of the cell. The value 

calculated for energy deposited per incident electron per gram of material is 22.6 MeV/g. The 

layer dimension is 5 mm x 5 mm x 5 μm, or 1.25e-4 cc. The density of SiC is 3.2 g/cc 

corresponding to a layer mass of 4e-4 g. The resulting energy deposited is 9.04 keV. This 

represents 7% of the peak incident-electron-energy (125 keV), deposited in a 5 um layer of SiC. 

In a prototype isotope battery design, foils will be sandwiched in-between two SiC DEC devices. 

In order to better understand the efficiency that would be expected from an isotope foil above the 

SiC DEC layer, we performed a broad beam simulation. The incident beam radius was varied 

[0.5, 1, 2, 5 mm]. Beams diameters, less than 2 mm, generated electron showers completely 

within the 5 mm side-length of the devices under test. The energy deposited in the first 5 μm 

layer was calculated to be 22.6 MeV/g. 2.26% of the incident energy is deposited in the first 

layer. This result is identical to the previous pencil beam simulations. 

These nuclear scattering calculations do not take into account the electrical circuit properties of a 

diode (i.e., source impedance feeding a load impedance). The numerical simulations described 

above are meant to determine an initial electron density, or density of carriers for initial 

conditions into a circuit-based model. The initial conditions are used in a study, already 

underway, to model the drift and diffusion in diode structures. The results will be reported in our 

next technical report of the investigation. 

The follow-on circuit modeling is expected to provide current and voltage numerical results and 

insight into the electron flow within the semiconductor materials that convert the emitted-beta 

energy into electrical current for a long-lived power source. The results of the circuit models 

being developed will be compared to measurements. With electron density initial conditions (the 

result of this investigation), a circuit model, and measurements in-hand, an optimized DEC for a 

nuclear battery design is expected as a result in the near future. 
11 

6. References 

1. Blanchard, James P. Stretching the Boundaries of Nuclear Technology. The Bridge 32.4 

(2002): 29-34. 

2. Ahearne, John F. The Future of Nuclear Energy. Editorial. The Bridge 31.3 (2001). 

3. Bugliarello, George. Our Energy Future. Editorial. The Bridge 32.2: 3-4. 

4. Lal, Amit; Blanchard, James. The Daintiest Dynamos. IEEE Spectrum Sept. 2004, 36-41. 

5. Lal, A.; Blanchard, James. By Harvesting Energy From Radioactive Specks, Nuclear 

Micrbatteries Could Power Tomorrow’s Microelectromechanical Marvels-And May Be Your 

Cellphone Too. IEEE Spectrum September 2004, 36-41. 

6. Briesmeister, J. F. ed. MCNPTM-A General Monte Carlo N-Particle Transport Code, LA12625-M, Version 4B (March 1997). 

7. Metropolis, Nicholas; Ulam, Stanislaw. The Monte Carlo method. Journal of the American 

Statistical Association 1949, 44 (247), 335-341. 

8. Kodeli, I. Progress on Computational Tools for 3D Sensitivity and Uncertainty Analsyis. 

OCED-NEA Seminar on 3D Deterministic Radiation Transport Computer Programs 

Features, Applications and Perspectives, Paris (2-3 Dec. 1996). 

9. Berger, Martin J.; Seltzer, Stephen M. Tables of Energy Losses and Ranges of Electrons and 

Positrons. National Aeronautics and Space Administration. Scientific and Technical 

Information Division, 1964, p 64. 

10. Cross, W. G.; Ing, H.; Freedman, N. A short atlas of beta-ray spectra. Phys. Med. Biol., 28

(11), 1251-1260. 

Other Related Manuscripts: 

11. Nickel: Human Health Fact Sheet. Argonne National Laboratory, EVS, August 2005. 

12. Energy Conversion Vol 13, pp. 117-127, Pergamon Press, 1973 

13. Silicon CarbideAlphavoltaic Battery, Rybicki, George, 25th PVSC, May 13-17, 1996, 

Washington, D.C. 

14. Si, H.; Lal, A.; Blanchard, J.; Henderson, D. Self-reciprocating radioisotope-powered 

cantilever. J. Appl. Phys. 2002, 92 (2), 1122-7. 

15. Pfann, W. G.; van Roosbroeck, V. Radioactive and Photoelectric p-n Junction Power 

Sources. J. Appl. Phys. 1954, 25 (11), 1422-34. 
12 

16. Olsen, L. C. Betavoltaic energy Conversion. Energy Conversion 1973, 13, 117-27. 

17. Spitsyn, A. B.; Prelas, M. A.; Tompson, Robert V.; Ghosh, T. K. Impurity Removal from 4-

H SIC using reversed Field Enhanced Diffusion with Optical Activation, Accepted for 

Publication in the Journal of Wide Band-Gap Materials, May 2002. 

18. Physics of radiation Effects in Crystals, eds. R. A. Johnson and A. N. Orlov, North-Holland, 

New York, 1986. 

19. Yamaguchi, M.; Yang, M.; Imaizumi, M.; Matsuda, S.; Kawasaki, O.; Hisamatsu, T. Type 

conversion in irradiated silicon diodes. Appl. Phys. Lett. 1997, 70, 2165. 

20. Prelas, M.; Charlson, E. J.; Meese, J.; Popovici, G.; Stacy, T. Diamond Photovoltaic Energy 

Conversion, Second International Conference on the Application of Diamond Films and 

Related Materials, Editors M. Yoshikawa, M. Murakawa, Y. Tzeng and W. A. Yarbrough, 

MY Tokyo, Pages 5-12 (1993). 

21. Prelas, M.; Ghosh T.; Tompson, R. Direct Conversion of Radioisotope Energy to Electricity, 

Year 3 Report for DOE contract, DE FG07-001D13927, August 2003. 

22. Brown, P. M. Betavoltaic batteries. Journal of New Energy 2001, 5 (4), 35-42. 

23. Brown, P. Betavoltaic batteries. New England News 1999, 6 (11), 2-3. 
13 

Appendix A. MCNPX Input Deck 

The input deck shown below is one of the series of numerical simulations evaluated. 

sicm3 

Model of Silicon Carbide with Sr source 

c (MCNPX scaled in cm) 

c 

c -------------------------------------------------------- 

c This is a model of the way electrons would be generated 

c in the SiC after being hit by electrons from a strontium source. 

c -------------------------------------------------------- 

c 

c +------------------------------------------------------+ 

c | | 

c | Cell Cards | 

c | | 

c +------------------------------------------------------+ 

c # mat density surface data 

c SiC 

 1 3 -3.2 -1 imp:e 1 u=1 $SiC layer closest to source 

 2 3 -3.2 -2 imp:e 1 u=1 $2nd SiC layer 

 3 3 -3.2 -3 imp:e 1 u=1 $3rd SiC layer 

 4 3 -3.2 -4 imp:e 1 u=1 $4th SiC layer 

 5 3 -3.2 -5 imp:e 1 u=1 $5th SiC layer 

 6 3 -3.2 -6 imp:e 1 u=1 $6th SiC layer 

 7 3 -3.2 -7 imp:e 1 u=1 $7th SiC layer 

 8 3 -3.2 -8 imp:e 1 u=1 $8th SiC layer 

 9 3 -3.2 -9 imp:e 1 u=1 $9th SiC layer 

 10 3 -3.2 -10 imp:e 1 u=1 $10th SiC layer 

 11 3 -3.2 -11 imp:e 1 u=1 $11th SiC layer 

 12 3 -3.2 -12 imp:e 1 u=1 $12th SiC layer 

 13 3 -3.2 -13 imp:e 1 u=1 $13th SiC layer 

 14 3 -3.2 -14 imp:e 1 u=1 $14th SiC layer 

 15 3 -3.2 -15 imp:e 1 u=1 $15th SiC layer 

 16 3 -3.2 -16 imp:e 1 u=1 $16th SiC layer 

 17 3 -3.2 -17 imp:e 1 u=1 $17th SiC layer 

 18 3 -3.2 -18 imp:e 1 u=1 $18th SiC layer 

 19 3 -3.2 -19 imp:e 1 u=1 $19th SiC layer 

 20 3 -3.2 -20 imp:e 1 u=1 $20th SiC layer 

 21 3 -3.2 -21 imp:e 1 u=1 $21th SiC layer 

 22 3 -3.2 -22 imp:e 1 u=1 $22th SiC layer 

 23 3 -3.2 -23 imp:e 1 u=1 $23th SiC layer 

 24 3 -3.2 -24 imp:e 1 u=1 $24th SiC layer 

 25 3 -3.2 -25 imp:e 1 u=1 $25th SiC layer 

 26 3 -3.2 -26 imp:e 1 u=1 $26th SiC layer 

 27 3 -3.2 -27 imp:e 1 u=1 $27th SiC layer 

 28 3 -3.2 -28 imp:e 1 u=1 $28th SiC layer 

 29 3 -3.2 -29 imp:e 1 u=1 $29th SiC layer 

 30 3 -3.2 -30 imp:e 1 u=1 $30th SiC layer 

c 

c air encasing 

 31 2 -0.00129 -31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 & 

 20 21 22 23 24 25 26 27 28 29 30 imp:e 1 u=1 

c 

 32 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 & 

 18 19 20 21 22 23 24 25 26 27 28 29 30 imp:e 1 u=1 

 33 0 31 imp:e 1 u=1 

c room 

 34 2 -0.00129 -32 imp:e 1 fill=1 

 35 0 32 imp:e 0 $outside world 

 

c +------------------------------------------------------+ 

c | | 

c | Surface Cards | 

c | | 

c +------------------------------------------------------+ 

c # type params 

c SiC 

 1 rpp 708.75 709.25 493.9995 494 117.25 117.75 $SiC layer closest to source 

 2 rpp 708.75 709.25 493.9990 493.9995 117.25 117.75 $2nd SiC layer 

 3 rpp 708.75 709.25 493.9985 493.9990 117.25 117.75 $3rd SiC layer 

 4 rpp 708.75 709.25 493.9980 493.9985 117.25 117.75 $4th SiC layer 

 5 rpp 708.75 709.25 493.9975 493.9980 117.25 117.75 $5th SiC layer 

 6 rpp 708.75 709.25 493.9970 493.9975 117.25 117.75 $6th SiC layer 

 7 rpp 708.75 709.25 493.9965 493.9970 117.25 117.75 $7th SiC layer 

 8 rpp 708.75 709.25 493.9960 493.9965 117.25 117.75 $8th SiC layer 

 9 rpp 708.75 709.25 493.9955 493.9960 117.25 117.75 $9th SiC layer 

10 rpp 708.75 709.25 493.9950 493.9955 117.25 117.75 $10th SiC layer 

11 rpp 708.75 709.25 493.9945 493.9950 117.25 117.75 $11th SiC layer 

12 rpp 708.75 709.25 493.9940 493.9945 117.25 117.75 $12th SiC layer 

13 rpp 708.75 709.25 493.9935 493.9940 117.25 117.75 $13th SiC layer 

14 rpp 708.75 709.25 493.9930 493.9935 117.25 117.75 $14th SiC layer 

15 rpp 708.75 709.25 493.9925 493.9930 117.25 117.75 $15th SiC layer 

16 rpp 708.75 709.25 493.9920 493.9925 117.25 117.75 $16th SiC layer 

17 rpp 708.75 709.25 493.9915 493.9920 117.25 117.75 $17th SiC layer 

18 rpp 708.75 709.25 493.9910 493.9915 117.25 117.75 $18th SiC layer 

19 rpp 708.75 709.25 493.9905 493.9910 117.25 117.75 $19th SiC layer 

20 rpp 708.75 709.25 493.9900 493.9905 117.25 117.75 $20th SiC layer 

21 rpp 708.75 709.25 493.9895 493.9900 117.25 117.75 $21th SiC layer 

22 rpp 708.75 709.25 493.9890 493.9895 117.25 117.75 $22th SiC layer 

23 rpp 708.75 709.25 493.9885 493.9890 117.25 117.75 $23th SiC layer 

24 rpp 708.75 709.25 493.9880 493.9885 117.25 117.75 $24th SiC layer 

25 rpp 708.75 709.25 493.9875 493.9880 117.25 117.75 $25th SiC layer 

26 rpp 708.75 709.25 493.9870 493.9875 117.25 117.75 $26th SiC layer 

27 rpp 708.75 709.25 493.9865 493.9870 117.25 117.75 $27th SiC layer 

28 rpp 708.75 709.25 493.9860 493.9865 117.25 117.75 $28th SiC layer 

29 rpp 708.75 709.25 493.9855 493.9860 117.25 117.75 $29th SiC layer 

30 rpp 708.75 709.25 493.9850 493.9855 117.25 117.75 $30th SiC layer 

c lead block to encase silicon carbide 

31 rpp 708.5 709.5 493.850 494 117 118 

c room dimensions 

32 rpp 0 1419 0 1984 0 466 

c +------------------------------------------------------+ 

c | | 

c | Material Cards | 

c | | 

c +------------------------------------------------------+ 

c m# isotope percent & 

c m1 82000 1.0 $lead 11.34 g/cc 

 m2 8016 .3 & 

 7014 .7 $air 0.00129 g/cc 

 m3 6012 .43 & 

 14028 .57 $SiC 3.2 g/cc 

c m4 14028 1.0 $Si 2.3 g/cc 

c m13 6012 1.0 $C 2.3 g/cc 

c m5 90000 1.0 $Sr 

c 

c +------------------------------------------------------+ 

c | | 

c | Source Definition | 

c | | 

c +------------------------------------------------------+ 

sdef & 

 dir=1 & 

 erg= d2 & 

 vec=0 -1 0 & 

 par=e & 

 rad=d1 & 

 pos=709 497 117.5 

c 

 si1 0 .05 

 sp1 -21 

c 

c energy distribution taken from now strontium (old: 280kv-8ma-12may-aft.spe) 

 si2 0.000916 0.00549 0.0412 0.0833 0.125 0.167 0.208 0.25 0.291 0.333 & 

 0.375 0.417 0.459 0.5 

 sp2 0 1000 3405 4692 4768 3900 2775 2234 1725 1331 & 

 1007 764 556 378 

c 

c si3 h .707 1 

c sp3 d 0 1 

c 
14 

c +------------------------------------------------------+ 

c | | 

c | Data Cards | 

c | | 

c +------------------------------------------------------+ 

mode e 

nps 10000 $10Mh~9hrs 

c 

c +------------------------------------------------------+ 

c | | 

c | Tallies | 

c | | 

c +------------------------------------------------------+ 

c 

c 

fc2 test 

f2:e 1.3 

e2 0 98i .51 

c 

c fc4 electron flux averaged over the cell in particles/cm^2 

c f4:e 1 2 3 

c e4 0 98i .51 

c 

c 

c this tally may not work (or be correct) because it is with electrons 

c fc6 electron energy deposition averaged over the cell in MeV/g 

c f6:e 1 2 3 

c e6 0 98i .51 

c 

c fc8 electron energy deposition in MeV 

c *f8:e 1 2 3 

c e8 0 1e-5 97i .51 

c 

c +------------------------------------------------------+ 

c | | 

c | Mesh | 

c | | 

c +------------------------------------------------------+ 

c 

tmesh 

c 

c (a)radial meshes (b)zaxis(symmetry) (c)angles counterclockwise 

c 

c flux=#/cm2 $dose=rem/hr $pedep=MeV/cm3 

c 

c flux: flux 

c pedep: total energy deposition/unit vol 

c dose: dose over cross planes 

c 

rmesh1:e flux pedep popul dose 

c layer 1 SiC 

cora1 708.75 18i 709.25 

corb1 493.9995 494 

corc1 117.25 18i 117.75 

c 

rmesh21:e flux pedep popul dose 

c layer 1 SiC 

cora21 708.75 18i 709.25 

corb21 493.9995 18i 494 

corc21 117.25 117.75 

c 

rmesh31:e flux pedep popul dose 

c layer 1 SiC 

cora31 708.75 709.25 

corb31 493.9995 18i 494 

corc31 117.25 18i 117.75 

c 

c 

rmesh41:e flux pedep popul dose 

c layer 2 SiC 

cora41 708.75 18i 709.25 

corb41 493.999 493.9995 

corc41 117.25 18i 117.75 

c 

rmesh51:e flux pedep popul dose 

c layer 2 SiC 

cora51 708.75 18i 709.25 

corb51 493.999 18i 493.9995 

corc51 117.25 117.75 

c 

rmesh61:e flux pedep popul dose 

c layer 2 SiC 

cora61 708.75 709.25 

corb61 493.999 18i 493.9995 

corc61 117.25 18i 117.75 

c 

c 

rmesh71:e flux pedep popul dose 

c layer 3 SiC 

cora71 708.75 18i 709.25 

corb71 493.9985 493.999 

corc71 117.25 18i 117.75 

c 

rmesh81:e flux pedep popul dose 

c layer 3 SiC 

cora81 708.75 18i 709.25 

corb81 493.9985 18i 493.999 

corc81 117.25 117.75 

c 

rmesh91:e flux pedep popul dose 

c layer 3 SiC 

cora91 708.75 709.25 

corb91 493.9985 18i 493.999 

corc91 117.25 18i 117.75 

c 

c 

rmesh101:e flux pedep popul dose 

c all SiC layers (30) 

cora101 708.75 18i 709.25 

corb101 493.985 25i 494 

corc101 117.4925 117.5075 

c 

endmd 

 
15 

Distribution List

ADMNSTR 

DEFNS TECHL INFO CTR 

ATTN DTIC-OCP (ELECTRONIC COPY) 

8725 JOHN J KINGMAN RD STE 0944 

FT BELVOIR VA 22060-6218 

DARPA 

ATTN IXO S WELBY 

3701 N FAIRFAX DR 

ARLINGTON VA 22203-1714 

OFC OF THE SECY OF DEFNS 

ATTN ODDRE (R&AT) 

THE PENTAGON 

WASHINGTON DC 20301-3080 

US ARMY TRADOC 

BATTLE LAB INTEGRATION & TECHL 

DIRCTRT 

ATTN ATCD-B 

10 WHISTLER LANE 

FT MONROE VA 23651-5850 

SMC/GPA 

2420 VELA WAY STE 1866 

EL SEGUNDO CA 90245-4659 

COMMANDING GENERAL 

US ARMY AVN & MIS CMND 

ATTN AMSAM-RD W C MCCORKLE 

REDSTONE ARSENAL AL 35898-5000 

US ARMY INFO SYS ENGRG CMND 

ATTN AMSEL-IE-TD F JENIA 

FT HUACHUCA AZ 85613-5300 

US ARMY SIMULATION TRAIN & 

INSTRMNTN CMND 

ATTN AMSTI-CG M MACEDONIA 

12350 RESEARCH PARKWAY 

ORLANDO FL 32826-3726 

US GOVERNMENT PRINT OFF 

DEPOSITORY RECEIVING SECTION 

ATTN MAIL STOP IDAD J TATE 

732 NORTH CAPITOL ST., NW 

WASHINGTON DC 20402 

US ARMY RSRCH LAB 

ATTN AMSRD-ARL-CI-OK-TP 

 TECHL LIB T LANDFRIED (2 COPIES) 

BLDG 4600 

ABERDEEN PROVING GROUND MD 

 21005-5066 

DIRECTOR 

US ARMY RSRCH LAB 

ATTN AMSRD-ARL-RO-EV 

 W D BACH 

PO BOX 12211 

RESEARCH TRIANGLE PARK NC 27709 

ECOPULSE INC 

7844 VERVAIN CT 

SPRINGFIELD VA 22150 

US ARMY RSRCH LAB 

ATTN AMSRD-ARL-D J M MILLER 

ATTN AMSRD-ARL-CI-OK-T 

 TECHL PUB (2 COPIES ) 

ATTN AMSRD-ARL-CI-OK-TL 

 TECHL LIB (2 COPIES) 

ATTN AMSRD-ARL-SE-DE 

 M LITZ (10 COPIES) 

ATTN AMSRD-ARL-SE-DE 

 K BLAINE (10 COPIES) 

ATTN AMSRD-ARL-SE-DB 

 G MERKEL 

ATTN IMNE-ALC-IMS 

 MAIL & RECORDS MGMT 

ADELPHI MD 20783-1197 
16 

INTENTIONALLY LEFT BLANK.