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High-resolution alpha spectrometry from thick sources
Radiation Measurements 39 (2005) 565 – 568 www.elsevier.com/locate/radmeas
Short communication
High-resolution alpha spectrometry from thick sources R. Pöllänen, T. Siiskonen∗ , P. Vesterbacka STUK-Radiation and Nuclear Safety Authority, P.O. Box 14, FIN-00881 Helsinki, Finland Received 11 November 2004; accepted 11 November 2004
Abstract Measurements, Monte Carlo simulations and imaging were used to study the degradation of the alpha particle energy spectrum caused by thick samples and thickness variations. Large local inhomogeneities were detected from the samples prepared by membrane filtration. Simulations were able to correctly produce alpha particle spectra. © 2004 Elsevier Ltd. All rights reserved. Keywords: Alpha spectrometry; Monte Carlo method; Quality control
1. Introduction Source characteristics, especially the sample matrix thickness, have a notable influence on the alpha particle energy spectrum in high-resolution alpha spectrometry. If the material layer of the source is thick, the peaks in the spectrum have a wide tail on the low-energy side, making nuclide identification difficult or even totally preventing it. Thus, carefully designed quality control procedures are needed in the laboratory to ensure the optimal quality of samples. The influence of source characteristics on the alpha particle energy spectra has been reported in a number of papers. For example, resolution and peak tailing were recently investigated as a function of sample thickness (Martin and Hancock, 2004). Sánchez et al. (2002) studied microscopic source properties by scanning-probe microscopy. They observed major inhomogeneities and concluded that materials and techniques used in the preparation of sources need to be improved. This paper reports on the effect of thick sources on alpha particle energy spectra investigated by alpha spectrometry. The variation in source thickness was demonstrated by ∗ Corresponding author. Tel.: +358 9 759 88 318;
fax: +358 9 759 88 433. E-mail addresses: roy.pollanen@stuk.fi (R. Pöllänen), teemu.siiskonen@stuk.fi (T. Siiskonen). 1350-4487/$ - see front matter © 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.radmeas.2004.11.003
imaging and a Monte Carlo code was used to simulate the spectra obtained from samples of varying thickness. Simulations were used to explain the anomalies observed in the measured spectra.
2. Monte Carlo code for simulating alpha particle energy spectra The main algorithms of the Monte Carlo simulation code were described by Siiskonen and Pöllänen (2004), who concentrated on the calculation of geometrical detection efficiency and energy loss in the absorbing sheets and aerosol particles. Here, emphasis was placed on the simulation of thick sources. The code was extended to take the thickness variations of the source into account. The starting point in the simulations was a cylindrical source with height H and radius RS . The thickness of the cylinder can be subjected to random fluctuation that is assumed to follow a Gaussian distribution with a user-given standard deviation . The resulting source thickness H (r) is limited to 0 H (r) H + a,
(1)
where r is the radial position inside the source and a is a user-given parameter (we have used a = 3). This upper limit for source thickness prevents impossibly large thicknesses.
R. Pöllänen et al. / Radiation Measurements 39 (2005) 565 – 568 0
4. Results
2
Measurements were taken using a passivated implanted planar silicon (PIPS) detector with an active area of 450 mm2 and the FWHM less than 20 keV. The source–detector distance was 16 mm. Monte Carlo simulation gave a value of 0.086 (one sigma error is 1%) for the geometrical detection efficiency. The mass per unit area was calculated from the measured spectra (Fig. 2). Simulations performed for an ideal cylindrical alpha particle source of thickness 0.9 mg/cm2 (thin solid line in Fig. 3) gave sharp-edged peaks that differed considerably from the
0 2 4 6 8
238
U
10
234
U
Maximum source thickness 12 0
2
4 6 Radial distance (mm)
8
10
Fig. 1. Simulated distributions of emission points. The distance from the emission point to the source surface and the radial distance are in the vertical and horizontal axes, respectively. The upper part of the picture refers to the case of an ideal cylindrical source with a thickness of 2 m. In the lower part the same source is subjected to thickness fluctuations with = 3 m. The cutoff parameter a = 3 which gives a maximum thickness of 11 m.
Number of counts (logarithmic scale)
Distance to source surface (micrometers)
566
2
2.3 mg/cm
0.90 mg/cm2
0.38 mg/cm2
U
6.8 µg/cm2 1
A fixed number of emission points were uniformly distributed inside the source (see the upper part of Fig. 1). For a cylindrical source the sampling is performed as described in Siiskonen and Pöllänen (2004). The rejection method is used for a source with a = 0. Random numbers are then uniformly generated within the cylinder r RS , 0 z H +a. Points falling above the source surface are discarded.
235
2
3 Energy (MeV)
4
5
Fig. 2. Measured alpha particle spectra from samples composed of natural U with different thicknesses. The peaks on the left and right correspond to 238 U and 234 U, respectively. The vertical axis gives the relative number of counts. In the channel containing the maximum number of counts, the total is more than 100 in each spectrum. The channel width is 10.7 keV. The spectrum having the narrowest peaks represents a typical thin source obtained from routine sample preparation procedure.
3. Source preparation 250
0.90 mg/cm2
238
U
234
U
200 Number of counts
Samples of different thickness and containing natural uranium were prepared using membrane filtration (Sill, 1987; Liberman and Moghissi, 1968). In STUK this method is routinely used to prepare thin samples for alpha particle spectrometry (Perfler et al., 1999). Uranium was separated from the tracer solution by ion exchange. Uranium was eluted from the ion exchange column using dilute hydrochloric acid which was allowed to evaporate to dryness. The residue was dissolved in 1 M HCl and CeCl3 was added with the amount of cerium in the solution being 50 g. After cerium addition, uranium was reduced to the oxidation state +4 by using TiCl4 . The counting planchet was prepared by precipitating uranium onto a 0.1 m Metricel polypropylene membrane using concentrated HF. The effective precipitation area of the filter was 350 mm2 . In terms of mass the final sample mainly consisted of isotope 238 U. For the thick samples, the contribution of 234 U, 235 U and lighter elements to the total mass of the thick samples was small.
150 100 50 0 2
2.5
3
3.5 Energy (MeV)
4
4.5
5
Fig. 3. Measured (thick solid line with markers, same as in Fig. 2) and simulated (other lines) alpha particle energy spectra from the sample containing 0.9 mg/cm2 uranium. The thin solid line represents the simulated spectrum from an ideal cylindrical source. Local thickness variations with = 1.25 m (see text) are assumed for the dashed line.
R. Pöllänen et al. / Radiation Measurements 39 (2005) 565 – 568 300
567
18mm 4.7mm
Colour index
250 200 150 100 50 0
Fig. 4. On the left, a light microscope photograph of the surface of the sample containing 0.9 mg/cm2 uranium. The photograph covers an area of width 4.8 mm. On the right, two cross sections of a pixel chart from the same sample using storage phosphor equipment. The cross sections correspond to bisectors of length 4.7 and 18 mm of the circular sample. The sample diameter is 20.5 mm and the pixel width is 70 m.
measured shapes (thick solid line in Fig. 3). A plateau was visible in the simulated spectrum and originating from alpha particles entering nearly perpendicularly to the detector from a sample of uniform thickness. However, it was absent in the measured spectrum. In addition, the low-energy tail was considerably longer in the measured spectrum. Similar behaviour was observed in other spectra measured from thick samples. The above-mentioned discrepancies could only arise from thickness inhomogeneities of the samples, since the alpha particle energy loss in the medium was shown to be correctly treated in the Monte Carlo code (Siiskonen and Pöllänen, 2004). A light microscope was used to visualise the distribution of (radioactive) material on the filter surface. Notable inhomogeneities were observed (Fig. 4). Material on the surface appeared to be clustered rather than evenly distributed. The size of the clusters was approximately 10–30 m. To further illustrate the inhomogeneity, the samples were placed in contact with storage phosphor imaging plates normally used in dental X-ray radiography (Brettle et al., 1996; Macdonald, 2001). The plates store a fraction of the radiation energy that is released in the form of luminescence by using laser scanning. A computer-controlled system manages the generated image, i.e. a chart of pixels with different colour indexes (index 0 corresponds to black and index 255 to white). The darker the pixels, the greater the thickness of the uranium layer on the filter surface. Microscale thickness variations were visualised using pixel chart cross-sections (Fig. 4). However, they only qualitatively represent variations, since the width of a pixel exceeds the typical size of a cluster by a factor of at least two. Monte Carlo simulations were performed to explain the anomalies between the measured and simulated spectra. By using a value of = 1.25 m (which is equivalent to the nominal thickness, obtained from the sample activity, of the cylindrical source multiplied by 1.5) for the local thickness variation, the spectrum presented as a dashed line in Fig. 3 was obtained. The parameter was the only free parameter in the present simulation, with others being fixed.
The simulations revealed that the same multiplication factor, i.e. 1.5 times the nominal thickness, was also valid for other samples of this study. Unfortunately, could not be quantitatively estimated with the experimental methods used in this work. However, observations (Fig. 4) indicated that the order of magnitude was correct.
5. Conclusions Major thickness variations were detected in the samples prepared by the membrane filtration method. However, this preparation method is not intented for thick samples. The amount of radioactive material used here was 100 times greater than that used in routine analyses. Nevertheless, sample preparation techniques could be further improved. This is especially important in quality control. The Monte Carlo code for simulating alpha particle spectra was extended to include local source thickness variations. The code is able to explain deviations between measured spectra and those obtained from simulations of ideal cylindrical samples.
Acknowledgements Ms. Tarja Ilander, Ms. Pia Kontturi and Mr. Reko Simola are acknowledged for their technical assistance.
References Brettle, D.S., Workman, A., Ellwood, R.P., Launders, J.H., Horner, K., Davies, R.M., 1996. The imaging performance of a storage phosphor system for dental radiography. Br. J. Radiol. 69, 256–261. Liberman, R., Moghissi, A.A., 1968. Co-precipitation technique for alpha spectroscopic determination of uranium and thorium and plutonium. Health Phys. 15, 359–362. Macdonald, R., 2001. Digital imaging for dentists. Aust. Dental J. 46, 301–305.
568
R. Pöllänen et al. / Radiation Measurements 39 (2005) 565 – 568
Martin, P., Hancock, G.J., 2004. Peak resolution and tailing in alpha-particle spectrometry for environmental samples. Appl. Radiat. Isot. 61, 161–165. Perfler, R., Staubmann, K., Salonen, L., Huikuri, P., Jaakkola, T., Lehto, J., Vaaramaa, K., Irlweck, K., Katzlberger, Ch., Schönhofer, F., Kralik, C., 1999. Removal of uranium, radium, lead and polonium from private ground water wells using ion exchange methods. Final Report WP 5 (27.07.1999) prepared for European Commission under TENAWA project.
Sánchez, A.M., Sánchez, M.J.N., Montero, M.P.R., Vilas, A.M., 2002. Study of inhomogeneities in sources prepared for particle spectrometry using scanning probe microscopy. Appl. Radiat. Isot. 56, 31–36. Siiskonen, T., Pöllänen, R., 2004. Simulation of alpha particle spectra from aerosol samples. Appl. Radiat. Isot. 60, 947–953. Sill, C.W., 1987. Precipitation of actinides as fluorides or hydroxides for high resolution alpha spectrometry. Nucl. Chem. Waste Manage. 7, 201–215.
Short communication
High-resolution alpha spectrometry from thick sources R. Pöllänen, T. Siiskonen∗ , P. Vesterbacka STUK-Radiation and Nuclear Safety Authority, P.O. Box 14, FIN-00881 Helsinki, Finland Received 11 November 2004; accepted 11 November 2004
Abstract Measurements, Monte Carlo simulations and imaging were used to study the degradation of the alpha particle energy spectrum caused by thick samples and thickness variations. Large local inhomogeneities were detected from the samples prepared by membrane filtration. Simulations were able to correctly produce alpha particle spectra. © 2004 Elsevier Ltd. All rights reserved. Keywords: Alpha spectrometry; Monte Carlo method; Quality control
1. Introduction Source characteristics, especially the sample matrix thickness, have a notable influence on the alpha particle energy spectrum in high-resolution alpha spectrometry. If the material layer of the source is thick, the peaks in the spectrum have a wide tail on the low-energy side, making nuclide identification difficult or even totally preventing it. Thus, carefully designed quality control procedures are needed in the laboratory to ensure the optimal quality of samples. The influence of source characteristics on the alpha particle energy spectra has been reported in a number of papers. For example, resolution and peak tailing were recently investigated as a function of sample thickness (Martin and Hancock, 2004). Sánchez et al. (2002) studied microscopic source properties by scanning-probe microscopy. They observed major inhomogeneities and concluded that materials and techniques used in the preparation of sources need to be improved. This paper reports on the effect of thick sources on alpha particle energy spectra investigated by alpha spectrometry. The variation in source thickness was demonstrated by ∗ Corresponding author. Tel.: +358 9 759 88 318;
fax: +358 9 759 88 433. E-mail addresses: roy.pollanen@stuk.fi (R. Pöllänen), teemu.siiskonen@stuk.fi (T. Siiskonen). 1350-4487/$ - see front matter © 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.radmeas.2004.11.003
imaging and a Monte Carlo code was used to simulate the spectra obtained from samples of varying thickness. Simulations were used to explain the anomalies observed in the measured spectra.
2. Monte Carlo code for simulating alpha particle energy spectra The main algorithms of the Monte Carlo simulation code were described by Siiskonen and Pöllänen (2004), who concentrated on the calculation of geometrical detection efficiency and energy loss in the absorbing sheets and aerosol particles. Here, emphasis was placed on the simulation of thick sources. The code was extended to take the thickness variations of the source into account. The starting point in the simulations was a cylindrical source with height H and radius RS . The thickness of the cylinder can be subjected to random fluctuation that is assumed to follow a Gaussian distribution with a user-given standard deviation . The resulting source thickness H (r) is limited to 0 H (r) H + a,
(1)
where r is the radial position inside the source and a is a user-given parameter (we have used a = 3). This upper limit for source thickness prevents impossibly large thicknesses.
R. Pöllänen et al. / Radiation Measurements 39 (2005) 565 – 568 0
4. Results
2
Measurements were taken using a passivated implanted planar silicon (PIPS) detector with an active area of 450 mm2 and the FWHM less than 20 keV. The source–detector distance was 16 mm. Monte Carlo simulation gave a value of 0.086 (one sigma error is 1%) for the geometrical detection efficiency. The mass per unit area was calculated from the measured spectra (Fig. 2). Simulations performed for an ideal cylindrical alpha particle source of thickness 0.9 mg/cm2 (thin solid line in Fig. 3) gave sharp-edged peaks that differed considerably from the
0 2 4 6 8
238
U
10
234
U
Maximum source thickness 12 0
2
4 6 Radial distance (mm)
8
10
Fig. 1. Simulated distributions of emission points. The distance from the emission point to the source surface and the radial distance are in the vertical and horizontal axes, respectively. The upper part of the picture refers to the case of an ideal cylindrical source with a thickness of 2 m. In the lower part the same source is subjected to thickness fluctuations with = 3 m. The cutoff parameter a = 3 which gives a maximum thickness of 11 m.
Number of counts (logarithmic scale)
Distance to source surface (micrometers)
566
2
2.3 mg/cm
0.90 mg/cm2
0.38 mg/cm2
U
6.8 µg/cm2 1
A fixed number of emission points were uniformly distributed inside the source (see the upper part of Fig. 1). For a cylindrical source the sampling is performed as described in Siiskonen and Pöllänen (2004). The rejection method is used for a source with a = 0. Random numbers are then uniformly generated within the cylinder r RS , 0 z H +a. Points falling above the source surface are discarded.
235
2
3 Energy (MeV)
4
5
Fig. 2. Measured alpha particle spectra from samples composed of natural U with different thicknesses. The peaks on the left and right correspond to 238 U and 234 U, respectively. The vertical axis gives the relative number of counts. In the channel containing the maximum number of counts, the total is more than 100 in each spectrum. The channel width is 10.7 keV. The spectrum having the narrowest peaks represents a typical thin source obtained from routine sample preparation procedure.
3. Source preparation 250
0.90 mg/cm2
238
U
234
U
200 Number of counts
Samples of different thickness and containing natural uranium were prepared using membrane filtration (Sill, 1987; Liberman and Moghissi, 1968). In STUK this method is routinely used to prepare thin samples for alpha particle spectrometry (Perfler et al., 1999). Uranium was separated from the tracer solution by ion exchange. Uranium was eluted from the ion exchange column using dilute hydrochloric acid which was allowed to evaporate to dryness. The residue was dissolved in 1 M HCl and CeCl3 was added with the amount of cerium in the solution being 50 g. After cerium addition, uranium was reduced to the oxidation state +4 by using TiCl4 . The counting planchet was prepared by precipitating uranium onto a 0.1 m Metricel polypropylene membrane using concentrated HF. The effective precipitation area of the filter was 350 mm2 . In terms of mass the final sample mainly consisted of isotope 238 U. For the thick samples, the contribution of 234 U, 235 U and lighter elements to the total mass of the thick samples was small.
150 100 50 0 2
2.5
3
3.5 Energy (MeV)
4
4.5
5
Fig. 3. Measured (thick solid line with markers, same as in Fig. 2) and simulated (other lines) alpha particle energy spectra from the sample containing 0.9 mg/cm2 uranium. The thin solid line represents the simulated spectrum from an ideal cylindrical source. Local thickness variations with = 1.25 m (see text) are assumed for the dashed line.
R. Pöllänen et al. / Radiation Measurements 39 (2005) 565 – 568 300
567
18mm 4.7mm
Colour index
250 200 150 100 50 0
Fig. 4. On the left, a light microscope photograph of the surface of the sample containing 0.9 mg/cm2 uranium. The photograph covers an area of width 4.8 mm. On the right, two cross sections of a pixel chart from the same sample using storage phosphor equipment. The cross sections correspond to bisectors of length 4.7 and 18 mm of the circular sample. The sample diameter is 20.5 mm and the pixel width is 70 m.
measured shapes (thick solid line in Fig. 3). A plateau was visible in the simulated spectrum and originating from alpha particles entering nearly perpendicularly to the detector from a sample of uniform thickness. However, it was absent in the measured spectrum. In addition, the low-energy tail was considerably longer in the measured spectrum. Similar behaviour was observed in other spectra measured from thick samples. The above-mentioned discrepancies could only arise from thickness inhomogeneities of the samples, since the alpha particle energy loss in the medium was shown to be correctly treated in the Monte Carlo code (Siiskonen and Pöllänen, 2004). A light microscope was used to visualise the distribution of (radioactive) material on the filter surface. Notable inhomogeneities were observed (Fig. 4). Material on the surface appeared to be clustered rather than evenly distributed. The size of the clusters was approximately 10–30 m. To further illustrate the inhomogeneity, the samples were placed in contact with storage phosphor imaging plates normally used in dental X-ray radiography (Brettle et al., 1996; Macdonald, 2001). The plates store a fraction of the radiation energy that is released in the form of luminescence by using laser scanning. A computer-controlled system manages the generated image, i.e. a chart of pixels with different colour indexes (index 0 corresponds to black and index 255 to white). The darker the pixels, the greater the thickness of the uranium layer on the filter surface. Microscale thickness variations were visualised using pixel chart cross-sections (Fig. 4). However, they only qualitatively represent variations, since the width of a pixel exceeds the typical size of a cluster by a factor of at least two. Monte Carlo simulations were performed to explain the anomalies between the measured and simulated spectra. By using a value of = 1.25 m (which is equivalent to the nominal thickness, obtained from the sample activity, of the cylindrical source multiplied by 1.5) for the local thickness variation, the spectrum presented as a dashed line in Fig. 3 was obtained. The parameter was the only free parameter in the present simulation, with others being fixed.
The simulations revealed that the same multiplication factor, i.e. 1.5 times the nominal thickness, was also valid for other samples of this study. Unfortunately, could not be quantitatively estimated with the experimental methods used in this work. However, observations (Fig. 4) indicated that the order of magnitude was correct.
5. Conclusions Major thickness variations were detected in the samples prepared by the membrane filtration method. However, this preparation method is not intented for thick samples. The amount of radioactive material used here was 100 times greater than that used in routine analyses. Nevertheless, sample preparation techniques could be further improved. This is especially important in quality control. The Monte Carlo code for simulating alpha particle spectra was extended to include local source thickness variations. The code is able to explain deviations between measured spectra and those obtained from simulations of ideal cylindrical samples.
Acknowledgements Ms. Tarja Ilander, Ms. Pia Kontturi and Mr. Reko Simola are acknowledged for their technical assistance.
References Brettle, D.S., Workman, A., Ellwood, R.P., Launders, J.H., Horner, K., Davies, R.M., 1996. The imaging performance of a storage phosphor system for dental radiography. Br. J. Radiol. 69, 256–261. Liberman, R., Moghissi, A.A., 1968. Co-precipitation technique for alpha spectroscopic determination of uranium and thorium and plutonium. Health Phys. 15, 359–362. Macdonald, R., 2001. Digital imaging for dentists. Aust. Dental J. 46, 301–305.
568
R. Pöllänen et al. / Radiation Measurements 39 (2005) 565 – 568
Martin, P., Hancock, G.J., 2004. Peak resolution and tailing in alpha-particle spectrometry for environmental samples. Appl. Radiat. Isot. 61, 161–165. Perfler, R., Staubmann, K., Salonen, L., Huikuri, P., Jaakkola, T., Lehto, J., Vaaramaa, K., Irlweck, K., Katzlberger, Ch., Schönhofer, F., Kralik, C., 1999. Removal of uranium, radium, lead and polonium from private ground water wells using ion exchange methods. Final Report WP 5 (27.07.1999) prepared for European Commission under TENAWA project.
Sánchez, A.M., Sánchez, M.J.N., Montero, M.P.R., Vilas, A.M., 2002. Study of inhomogeneities in sources prepared for particle spectrometry using scanning probe microscopy. Appl. Radiat. Isot. 56, 31–36. Siiskonen, T., Pöllänen, R., 2004. Simulation of alpha particle spectra from aerosol samples. Appl. Radiat. Isot. 60, 947–953. Sill, C.W., 1987. Precipitation of actinides as fluorides or hydroxides for high resolution alpha spectrometry. Nucl. Chem. Waste Manage. 7, 201–215.