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Geant4 Monte Carlo simulations for sensitivity investigations of an experimental facility for the measurement of tritium surface contaminations by BIXS
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Geant4 Monte Carlo simulations for sensitivity investigations of an experimental facility for the measurement of tritium surface contaminations by BIXS Marco Röllig ∗ , Beate Bornschein, Sylvia Ebenhöch, Florian Priester Karlsruhe Institute of Technology, Tritium Laboratory Karlsruhe (ITEP-TLK), P.O. Box 3640, 76021 Karlsruhe, Germany
a r t i c l e
i n f o
Article history: Received 28 August 2015 Received in revised form 26 January 2016 Accepted 4 February 2016 Available online xxx Keywords: BIXS Beta induced X-ray spectrometry Tritium Sorption Surface contamination Waste management
a b s t r a c t A new experimental facility was built at the Tritium Laboratory Karlsruhe (TLK) to investigate tritium surface contamination on arbitrary solid samples. With the TRIADE facility samples can be exposed to tritium in a pressure range of 10−7 –102 Pa and a temperature range of 170–470 K. Surface contamination which accumulate on the sample by adsorption or absorption processes of T2 are determined by -induced X-ray spectrometry (BIXS). To quantify the amount of surface contamination and to study systematic effects of the setup Monte Carlo simulations have been performed. Detection efficiencies for tritium adsorption on all inner surfaces of the setup were determined. From that, a minimum detectable activity on the sample surface of 600 Bq/cm2 has been calculated which can be translated into a minimum measurable surface contamination of 5.5 × 10−4 monolayer for ideal (100) gold surfaces. First tritium measurements with a gold-coated beryllium sample at exposure pressures of 5 × 10−3 Pa showed an increasing background signal. After a dosage of p × t = (1.6 ± 0.1) × 10−2 Pa h the background signal was 1.10 ± 0.04 cps. Assuming an ideal plane (100) gold surface the results found can be translated into a surface contamination of ≈5.4 × 104 Bq/cm2 . Further measurements must be performed as a linear increase of the count rate with the applied dosage was observed. © 2016 Elsevier B.V. All rights reserved.
1. Introduction Surface contamination processes of materials exposed to tritium are of scientific interest in fusion technology [1,2] and for the KArlsruhe TRItium Neutrino (KATRIN) experiment [3]. In KATRIN [4], a BIXS system [5] is used for activity monitoring of the gaseous tritium source with an unprecedented precision of 0.1% [3]. An essential part of the BIXS system is a gold-coated beryllium window which protects the detector from the T2 environment. Significant tritium adsorption on that window can make the detector blind for activity changes in the tritium source on the 0.1% level. Also tritium adsorption on the stainless steel walls of the KATRIN tritium source can cause significant background in the BIXS system. Therefore, a new experimental facility was built at the Tritium Laboratory Karlsruhe (TLK) for the investigation of surface contaminations on arbitrary solid materials. The TRIADE
∗ Corresponding author. E-mail address: [email protected] (M. Röllig).
(Tritium Adsorption Desorption) experimental facility is able to measure tritium adsorption, absorption and desorption in a temperature range of 170–470 K and a pressure range of 10−7 –102 Pa. Activity measurements in TRIADE are based on the BIXS method. First tritium measurements were performed with a gold-coated beryllium sample at exposure pressures of up to 5 × 10−3 Pa. The aim of this work is the interpretation of the measured tritium data in terms of surface contamination in Bq/cm2 on the sample. Therefore, the detection efficiency of the BIXS system for tritium adsorption on the sample is a necessary input. This must be determined by Monte Carlo simulations. Also systematic effects shall be studied as the influence of • tritium adsorption on the inner surfaces of the sample chamber, • tritium absorption in the bulk of the sample and • small shifts in the detector position on the measured detector signal. Finally, from the Monte Carlo simulation results the minimum measurable surface contamination of TRIADE, for a gold-coated beryllium sample, shall be calculated.
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Please cite this article in press as: M. Röllig, et al., Geant4 Monte Carlo simulations for sensitivity investigations of an experimental facility for the measurement of tritium surface contaminations by BIXS, Fusion Eng. Des. (2016), http://dx.doi.org/10.1016/j.fusengdes.2016.02.018
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of -electrons. The produced X-rays can be transmitted through the gold-coated beryllium window and measured by the detector. Due to the geometry of the system, the detection of X-rays emitted from the sample and the beryllium window should be more likely than for other sources inside the sample chamber. Significant signal contributions from the residual gas between the sample and the beryllium window are avoided by tritium partial pressures below 1 Pa. The total X-ray intensity scales with the amount of tritium which can only be determined by comparison with Monte Carlo simulation results. 3. First tritium measurements
Fig. 1. Setup of the core system of the TRIADE experimental facility.
2. Experimental description The TRIADE setup is mounted inside a fume hood at TLK. Due to TLK regulations the maximum allowed activity inside the fume hood is 1010 Bq, which allows maximum tritium partial pressures of about 100 Pa inside the sample chamber. The BIXS setup of TRIADE is explained based on the CAD drawing of the core system as shown in Fig. 1. A stainless steel cylinder with an inner diameter of 100 mm and a length of 107 mm is used as sample chamber. A second vacuum chamber with an inner diameter of 150 mm houses the X-ray detector electronics. Between both chambers is an intermediate flange with an inner bore (∅ 38 mm) which houses the X-ray detector. The sample chamber and the detector chamber are separated by a gold-coated beryllium window (∅ 20 mm) which prevents tritium contamination of the detector. The detector chamber and the detector electronics chamber are separated by a flange which is connected to the detector finger. Continuous pumping of the detector volume reduces the mechanical load on the 100 m thick beryllium window in case of an evacuated sample chamber. The X-ray detector is an Amptek® X-123SDD Silicon Drift Detector (SDD) system. According to the manufacturers data the 500 m thick SDD has an area size of 25 mm2 which are collimated to an active area of 17 mm2 . A 12.5 m thick beryllium window is in front of the SDD. The SDD, the collimator and the beryllium window are mounted into a stainless steel capsule. A gold-coated copper disk with a diameter of 94 mm is used as sample holder. Samples with a diameter of 60–70 mm can be fixed to the sample holder by a gold-coated copper holder ring. The geometry of the system is designed in a way, that the X-ray detector has only a direct line of sight to the sample surface and the surface of the gold-coated beryllium window. The coating thicknesses for each component are listed in Table 1. Tritium measurements are based on the BIXS method. In case of significant tritium adsorption on any of the surfaces inside the sample chamber, X-rays are produced during the absorption process
Table 1 Overview of the Au coatings of the core components. Different materials of the X-ray window are noted separately. Part
Process
Au thickness
Sample Sample holder Blind flange Intermediate flange X-ray window SS X-ray window Be Recipient Bolts
Sputtered Sputtered Sputtered Sputtered Sputtered Sputtered Electro-plated Electro-plated
100 nm 1 m 1 m 1 m 1 m 100 nm 5 m 5 m
First tritium measurements were performed with the TRIADE facility and a gold-coated beryllium sample. In two tritium measurement campaigns the sample was repeatedly exposed to tritium partial pressures of up to 5 × 10−3 Pa. The detector count rate after measurement campaign one and an applied dosage of p × t = (1.6 ± 0.1) × 10−2 Pa h was 1.10 ± 0.04 cps. After measurement campaign two and an applied dosage of p × t = (0.9 ± 0.1) × 10−2 Pa h a count rate of 0.92 ± 0.04 cps was measured. Between the measurement campaigns a bake-out at 200 ◦ C was performed. In both measurement campaigns a linear increase in count rate with the applied dosage was observed. All measurements were performed at a temperature of 33.2 ± 0.8 ◦ C. 4. Monte Carlo simulations The Monte Carlo simulations were performed with Geant4.9.6p02 Penelope physics [6]. For the simulation, the geometry of the sample chamber shown in Fig. 1 was simplified to the geometry as shown in Fig. 2. Nine different simulations were performed to determine the respective detection efficiencies with a total number of 158 runs, each with 109 simulated primary events. For each run a different random seed was used to make the results statistically independent. In each simulation the primary particles were electrons. Their energy spectrum was defined as a user specified histogram with a bin number of 186 and linear interpolation between the data points. It was specified according to the -spectrum of tritium and primary particles were emitted isotropically. Sources were defined as cylindrical volume source (No. 1, Table 2) or circular shaped 2D sources (Nos. 2–9, Table 2). An overview is given in Table 2. The detection efficiencies for tritium adsorbed on the sample surface (No. 6, Table 2) and the gold-coated beryllium window (No. 5, Table 2) were simulated to quantify the amount of tritium on the sample surface from the measured X-ray intensity. Signal contributions from background sources, as residual gas in the sample chamber (No. 1, Table 2) or tritium adsorbed on inner surfaces of the sample chamber (Nos. 2–4, Table 2), must also be quantified. For future tritium measurements with an uncoated beryllium sample, the detection efficiency for tritium adsorbed on a bare beryllium surface was simulated (No. 7, Table 2). As there is also the possibility for tritium absorption in the bulk, the detection efficiencies for tritium in five different bulk depths of the sample have been simulated (No. 8, Table 2). It is already reported that geometrical uncertainties can have a strong influence on Monte Carlo simulation results of BIXS systems [7]. Therefore, the influence of small shifts in the detector position on the detection efficiency, for tritium adsorbed on the sample, has been investigated (No. 9, Table 2). 5. Discussions of the results Simulation results for the detection efficiencies are given in Table 3. Due to the small distance to the detector and the direct
Please cite this article in press as: M. Röllig, et al., Geant4 Monte Carlo simulations for sensitivity investigations of an experimental facility for the measurement of tritium surface contaminations by BIXS, Fusion Eng. Des. (2016), http://dx.doi.org/10.1016/j.fusengdes.2016.02.018
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Fig. 2. Left: Simulated geometry of the TRIADE core setup. Right: The enlarged SDD geometry. Since the collimator material is unknown it was replaced by zirconium in the simulation. Drawings are not in scale.
Table 2 Overview of the MC-simulations. In each run 109 -electron events were simulated. In simulation No. 1 only the gas column between the sample surface and the goldcoated beryllium window was simulated to quantify the influence of residual gas. No.
Description
No. of runs
1 2 3 4 5 6 7 8 9
Residual gas Be window CF flange Sample ring holder Recipient wall Be entrance window Au sample surface Be sample surface Au sample bulk SDD position ±2 mm
2 10 10 2 2 23 2 5×8 10 × 4
Table 3 Detection efficiencies (DE) for adsorbed tritium on different surfaces, absorbed tritium in the sample bulk and gaseous tritium in the recipient. Description
DE (cps/Bq)
Area (cm2 )
Residual gas Be window CF flange Sample ring holder Recipient wall Be entrance window Au sample surface Be sample surface Au sample bulk 20 nm Au sample bulk 40 nm Au sample bulk 60 nm Au sample bulk 80 nm Au sample bulk 100 nm
(8.05 ± 0.63) × 10−8 (1.20 ± 0.09) × 10−7 (1.20 ± 0.07) × 10−8 (6.00 ± 1.73) × 10−9 (1.61 ± 0.03) × 10−6 (3.12 ± 0.06) × 10−7 (8.60 ± 0.66) × 10−8 (3.88 ± 0.10) × 10−7 (3.92 ± 0.10) × 10−7 (3.71 ± 0.10) × 10−7 (2.98 ± 0.09) × 10−7 (1.56 ± 0.04) × 10−7
– 35.3 41.1 344.7 3.1 28.3 28.3 28.3 28.3 28.3 28.3 28.3
background sources could be strongly reduced by an external magnetic field. One monolayer of tritium on all simulated inner surfaces of the sample chamber, including the sample surface, would induce a detector signal of 22.2 ± 0.8 cps. This is well above the intrinsic detector background of (4.11 ± 0.13) × 10−3 cps of the used X-ray detector in the TRIADE setup. The minimum mean count rate of the detector in a measurement time of 1000 s which can be reported, is calculated according to the Currie equation [8] to 12.1 × 10−3 cps. Assuming the same coverage on all inner surfaces of the sample chamber, this result can be translated into a minimum detectable activity of TRIADE for tritium contamination on the sample surface of 600 Bq/cm2 . Assuming an ideal plane (100) gold sample surface, the minimum measurable surface contamination in terms of monolayer is 5.5 × 10−4 monolayer. Statistical errors are neglected in this calculation due to the unquantifiable systematical errors of the underlying assumptions. Residual gas in the recipient contributes rather weakly to the detector signal. Assuming a tritium partial pressure of 10−1 Pa, the expected count rate would be 0.35 ± 0.02 cps. This source of background can not be suppressed by an external magnetic field but is low enough to allow tritium measurements at tritium partial pressures of 1 Pa with sub-monolayer sensitivity. A significant background contribution can be caused by tritium absorbed in the bulk. Tritium depth profiling by BIXS on a m scale has been reported in the literature [9], [10] but it is regarded as very difficult on a 100 nm scale. Simulated spectral shapes for a
line of sight, the detection efficiencies for adsorbed tritium are highest for the sample and the gold-coated beryllium window. Assuming one monolayer of tritium coverage on ideal (100) gold surfaces, the detector signal contribution from tritium adsorbed on the gold-coated beryllium window surface would be 5.4 ± 0.1 cps. The detector signal contribution from tritium adsorbed on the sample surface would be 9.5 ± 0.1 cps and is therefore dominant. This is due to the larger surface area. Background sources (Nos. 2–4, Table 3) contribute to the detector signal by 7.3 ± 0.8 cps, assuming also one monolayer and ideal (100) gold surfaces. The dominant source of background is adsorbed tritium on the gold-coated stainless steel flange of the beryllium window with a fraction of 4.6 ± 0.3 cps. Even if there is no direct line of sight, due to the large backscattering probability of -electrons from the gold surface of the stainless steel flange, electrons emitted from there can hit the sample surface. X-rays produced there have a rather large detection probability. These contributions from
Detection efficiency (cps/Bq/ΔE)
1.6E-7
Au Mα
Sample surface Sample bulk 20 nm Sample bulk 40 nm Sample bulk 60 nm Sample bulk 80 nm Sample bulk 100 nm 1σ error bars
1.2E-7 8.0E-8 4.0E-8 2.0E-8 1.5E-8 1.0E-8 5.0E-9 0.0 0
5
10 Energy (keV)
15
Fig. 3. Bremsstrahlung intensity and spectral shape depending on the sample bulk depth of absorbed tritium.
Please cite this article in press as: M. Röllig, et al., Geant4 Monte Carlo simulations for sensitivity investigations of an experimental facility for the measurement of tritium surface contaminations by BIXS, Fusion Eng. Des. (2016), http://dx.doi.org/10.1016/j.fusengdes.2016.02.018
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175
X-ray window surface Sample surface
DE(x)/DE(0) (%)
150 125 100 75 50
-2
-1
0 1 Detector shift (mm)
2
Fig. 4. Detection efficiency (DE) depending on the SDD position. Error bars indicating 1 statistical uncertainties. Values are normalized to the zero position of the SDD as denoted in Table 3.
bulk depth of 100 nm are shown in Fig. 3. All spectra show the typical spectral shape of a continuous Bremsstrahlung part superimposed by characteristic fluorescence lines [11]. As even in the first 10 nm of the bulk significant amounts of tritium could accumulate, a distinction between adsorbed and absorbed tritium by analysis of the spectral shape is not possible with the TRIADE setup. The detection efficiencies for tritium absorbed in the sample bulk, as noted in Table 3, are in the same order as for adsorbed tritium. Further systematic uncertainties can emerge from deviations of the simulated geometry compared to the actual geometry of the setup. As a crucial part, the influence of small shifts in the detector position has been studied as shown in Fig. 4. Both the detection efficiency for adsorbed tritium on the beryllium window and on the sample surface increase for shorter distances. In the worst case, the simulated detection efficiencies can vary up to 30% for shifts in the detector position between ±2 mm. 6. Conclusions The Monte Carlo simulation results for the detection efficiency clearly show, that the TRIADE facility is able to measure tritium surface contamination on a gold-coated sample with a sub-monolayer sensitivity. However, due to the measurement principle absorbed tritium is indistinguishable from adsorbed tritium. Therefore TRIADE can only give an upper limit for tritium adsorption on the sample. A handle for the discrimination between adsorbed and absorbed tritium could come from the time response of the detector count rate during tritium exposure. As absorption and adsorption are different physical processes a different time response is expected here. This is part of future studies. Tritium contamination of arbitrary solid samples can be quantified if the contamination of the sample under tritium exposure is significant higher than for gold. Literature data implies, that this is the case for most materials [12]. In that case count rate contributions from the gold-coated surfaces can simply be neglected. If the tritium contamination on the sample is of the same order of magnitude as on the gold-coated surfaces of the inner recipient, then it strongly depends on the level of repeatability of the
tritium measurements with gold to subtract the expected count rate contribution as background. This is also part of future studies. Systematic uncertainties can also emerge from several background sources within the sample volume. Even if tritium adsorption on the sample surface causes the dominant contribution to the detector signal, in case of a gold-coated sample, tritium adsorption on other surfaces within the sample chamber causing significant systematic uncertainties. An external magnetic field which guides the -electrons could help to reduce this effect. During first tritium measurements at exposure pressures of 5 × 10−3 Pa a maximum count rate of 1.10 ± 0.04 cps was measured. Assuming an ideal plane (100) gold surface and identical adsorption behavior on all gold-coated surfaces in the sample chamber this can be translated into a surface contamination of ≈0.05 monolayer or ≈5.4 × 104 Bq/cm2 . This must be regarded as a momentary upper limit as a linear increase of the detector count rate with applied dosage p × t was measured. Further measurements with TRIADE will show if a saturation level in the surface contamination can be reached and what the time response is. With that, TRIADE will provide the necessary input data for KATRIN and the qualification of its BIXS system. Further measurements with fusion relevant material samples will be published in the near future. Acknowledgements A part of this work was supported by the Helmholtz Association of National Research Centers. The authors would also like to thank all their colleagues from TLK for the support during this work. References [1] M. Nishikawa, T. Takeishi, Y. Kawamura, Y. Takagi, Y. Matsumoto, Tritium mass balance in the piping system of a fusion reactor, Fusion Technol. 21 (2) (1992) 878–882. [2] , in: Tritium handling issues in fusion reactor materials, J. Nucl. Mater. 417 (1–3) (2011) 545–550. [3] M. Babutzka, M. Bahr, J. Bonn, B. Bornschein, A. Dieter, G. Drexlin, K. Eitel, S. Fischer, F. Glück, S. Grohmann, M. Hötzel, T.M. James, W. Käfer, M. Leber, B. Monreal, F. Priester, M. Röllig, M. Schlösser, U. Schmitt, F. Sharipov, M. Steidl, M. Sturm, H.H. Telle, N. Titov, in: Monitoring of the operating parameters of the KATRIN windowless gaseous tritium source, New J. Phys. 14 (10) (2012) 103046. [4] K. Collaboration, Katrin Design Report 2004, FZKA Report 7090. [5] M. Matsuyama, K. Watanabe, K. Hasegawa, in: Tritium assay in materials by the Bremsstrahlung counting method, Fusion Eng. Des. 39–40 (1998) 929–936. [6] S.A., et al., in: Geant4 – a simulation toolkit, Nucl. Instrum. Methods Phys. Res. Sect. A: Accel. Spectrom. Detect. Assoc. Equip. 506 (3) (2003) 250–303. [7] L. Mao, Z. An, Q. Wu, H. Sun, H. Chen, X. Zhou, in: Effect of geometrical parameters uncertainty of {BIXS} experimental setup for tritium analysis, Nucl. Instrum. Methods Phys. Res. Sect. B: Beam Interact. Mater. Atoms 289 (2012) 52–55. [8] L.A. Currie, in: Limits for qualitative detection and quantitative determination. application to radiochemistry, Anal. Chem. 40 (3) (1968) 586–593. [9] Z. An, Q. Hou, J. Long, in: Reconstruction of depth distribution of tritium in materials by -ray induced X-ray spectrometry, Nucl. Instrum. Methods Phys. Res. Sect. B: Beam Interact. Mater. Atoms 266 (16) (2008) 3643–3646. [10] W. Zhang, H. Sun, F. Zeng, L. Mao, Q. Wu, J. Zhu, Z. An, in: Tritium analysis in titanium films by the {BIXS} method, Nucl. Instrum. Methods Phys. Res. Sect. B: Beam Interact. Mater. Atoms 275 (2012) 20–23. [11] M. Röllig, F. Priester, M. Babutzka, J. Bonn, B. Bornschein, G. Drexlin, S. Ebenhöch, E.W. Otten, M. Steidl, M. Sturm, in: Activity monitoring of a gaseous tritium source by beta induced x-ray spectrometry, Fusion Eng. Des. 88 (6) (2013) 1263–1266. [12] M. Okada, M. Nakamura, K. Moritani, T. Kasai, in: Dissociative adsorption of hydrogen on thin au films grown on Ir1 1 1, Surf. Sci. 523 (3) (2003) 218–230.
Please cite this article in press as: M. Röllig, et al., Geant4 Monte Carlo simulations for sensitivity investigations of an experimental facility for the measurement of tritium surface contaminations by BIXS, Fusion Eng. Des. (2016), http://dx.doi.org/10.1016/j.fusengdes.2016.02.018
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Contents lists available at ScienceDirect
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Geant4 Monte Carlo simulations for sensitivity investigations of an experimental facility for the measurement of tritium surface contaminations by BIXS Marco Röllig ∗ , Beate Bornschein, Sylvia Ebenhöch, Florian Priester Karlsruhe Institute of Technology, Tritium Laboratory Karlsruhe (ITEP-TLK), P.O. Box 3640, 76021 Karlsruhe, Germany
a r t i c l e
i n f o
Article history: Received 28 August 2015 Received in revised form 26 January 2016 Accepted 4 February 2016 Available online xxx Keywords: BIXS Beta induced X-ray spectrometry Tritium Sorption Surface contamination Waste management
a b s t r a c t A new experimental facility was built at the Tritium Laboratory Karlsruhe (TLK) to investigate tritium surface contamination on arbitrary solid samples. With the TRIADE facility samples can be exposed to tritium in a pressure range of 10−7 –102 Pa and a temperature range of 170–470 K. Surface contamination which accumulate on the sample by adsorption or absorption processes of T2 are determined by -induced X-ray spectrometry (BIXS). To quantify the amount of surface contamination and to study systematic effects of the setup Monte Carlo simulations have been performed. Detection efficiencies for tritium adsorption on all inner surfaces of the setup were determined. From that, a minimum detectable activity on the sample surface of 600 Bq/cm2 has been calculated which can be translated into a minimum measurable surface contamination of 5.5 × 10−4 monolayer for ideal (100) gold surfaces. First tritium measurements with a gold-coated beryllium sample at exposure pressures of 5 × 10−3 Pa showed an increasing background signal. After a dosage of p × t = (1.6 ± 0.1) × 10−2 Pa h the background signal was 1.10 ± 0.04 cps. Assuming an ideal plane (100) gold surface the results found can be translated into a surface contamination of ≈5.4 × 104 Bq/cm2 . Further measurements must be performed as a linear increase of the count rate with the applied dosage was observed. © 2016 Elsevier B.V. All rights reserved.
1. Introduction Surface contamination processes of materials exposed to tritium are of scientific interest in fusion technology [1,2] and for the KArlsruhe TRItium Neutrino (KATRIN) experiment [3]. In KATRIN [4], a BIXS system [5] is used for activity monitoring of the gaseous tritium source with an unprecedented precision of 0.1% [3]. An essential part of the BIXS system is a gold-coated beryllium window which protects the detector from the T2 environment. Significant tritium adsorption on that window can make the detector blind for activity changes in the tritium source on the 0.1% level. Also tritium adsorption on the stainless steel walls of the KATRIN tritium source can cause significant background in the BIXS system. Therefore, a new experimental facility was built at the Tritium Laboratory Karlsruhe (TLK) for the investigation of surface contaminations on arbitrary solid materials. The TRIADE
∗ Corresponding author. E-mail address: [email protected] (M. Röllig).
(Tritium Adsorption Desorption) experimental facility is able to measure tritium adsorption, absorption and desorption in a temperature range of 170–470 K and a pressure range of 10−7 –102 Pa. Activity measurements in TRIADE are based on the BIXS method. First tritium measurements were performed with a gold-coated beryllium sample at exposure pressures of up to 5 × 10−3 Pa. The aim of this work is the interpretation of the measured tritium data in terms of surface contamination in Bq/cm2 on the sample. Therefore, the detection efficiency of the BIXS system for tritium adsorption on the sample is a necessary input. This must be determined by Monte Carlo simulations. Also systematic effects shall be studied as the influence of • tritium adsorption on the inner surfaces of the sample chamber, • tritium absorption in the bulk of the sample and • small shifts in the detector position on the measured detector signal. Finally, from the Monte Carlo simulation results the minimum measurable surface contamination of TRIADE, for a gold-coated beryllium sample, shall be calculated.
http://dx.doi.org/10.1016/j.fusengdes.2016.02.018 0920-3796/© 2016 Elsevier B.V. All rights reserved.
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of -electrons. The produced X-rays can be transmitted through the gold-coated beryllium window and measured by the detector. Due to the geometry of the system, the detection of X-rays emitted from the sample and the beryllium window should be more likely than for other sources inside the sample chamber. Significant signal contributions from the residual gas between the sample and the beryllium window are avoided by tritium partial pressures below 1 Pa. The total X-ray intensity scales with the amount of tritium which can only be determined by comparison with Monte Carlo simulation results. 3. First tritium measurements
Fig. 1. Setup of the core system of the TRIADE experimental facility.
2. Experimental description The TRIADE setup is mounted inside a fume hood at TLK. Due to TLK regulations the maximum allowed activity inside the fume hood is 1010 Bq, which allows maximum tritium partial pressures of about 100 Pa inside the sample chamber. The BIXS setup of TRIADE is explained based on the CAD drawing of the core system as shown in Fig. 1. A stainless steel cylinder with an inner diameter of 100 mm and a length of 107 mm is used as sample chamber. A second vacuum chamber with an inner diameter of 150 mm houses the X-ray detector electronics. Between both chambers is an intermediate flange with an inner bore (∅ 38 mm) which houses the X-ray detector. The sample chamber and the detector chamber are separated by a gold-coated beryllium window (∅ 20 mm) which prevents tritium contamination of the detector. The detector chamber and the detector electronics chamber are separated by a flange which is connected to the detector finger. Continuous pumping of the detector volume reduces the mechanical load on the 100 m thick beryllium window in case of an evacuated sample chamber. The X-ray detector is an Amptek® X-123SDD Silicon Drift Detector (SDD) system. According to the manufacturers data the 500 m thick SDD has an area size of 25 mm2 which are collimated to an active area of 17 mm2 . A 12.5 m thick beryllium window is in front of the SDD. The SDD, the collimator and the beryllium window are mounted into a stainless steel capsule. A gold-coated copper disk with a diameter of 94 mm is used as sample holder. Samples with a diameter of 60–70 mm can be fixed to the sample holder by a gold-coated copper holder ring. The geometry of the system is designed in a way, that the X-ray detector has only a direct line of sight to the sample surface and the surface of the gold-coated beryllium window. The coating thicknesses for each component are listed in Table 1. Tritium measurements are based on the BIXS method. In case of significant tritium adsorption on any of the surfaces inside the sample chamber, X-rays are produced during the absorption process
Table 1 Overview of the Au coatings of the core components. Different materials of the X-ray window are noted separately. Part
Process
Au thickness
Sample Sample holder Blind flange Intermediate flange X-ray window SS X-ray window Be Recipient Bolts
Sputtered Sputtered Sputtered Sputtered Sputtered Sputtered Electro-plated Electro-plated
100 nm 1 m 1 m 1 m 1 m 100 nm 5 m 5 m
First tritium measurements were performed with the TRIADE facility and a gold-coated beryllium sample. In two tritium measurement campaigns the sample was repeatedly exposed to tritium partial pressures of up to 5 × 10−3 Pa. The detector count rate after measurement campaign one and an applied dosage of p × t = (1.6 ± 0.1) × 10−2 Pa h was 1.10 ± 0.04 cps. After measurement campaign two and an applied dosage of p × t = (0.9 ± 0.1) × 10−2 Pa h a count rate of 0.92 ± 0.04 cps was measured. Between the measurement campaigns a bake-out at 200 ◦ C was performed. In both measurement campaigns a linear increase in count rate with the applied dosage was observed. All measurements were performed at a temperature of 33.2 ± 0.8 ◦ C. 4. Monte Carlo simulations The Monte Carlo simulations were performed with Geant4.9.6p02 Penelope physics [6]. For the simulation, the geometry of the sample chamber shown in Fig. 1 was simplified to the geometry as shown in Fig. 2. Nine different simulations were performed to determine the respective detection efficiencies with a total number of 158 runs, each with 109 simulated primary events. For each run a different random seed was used to make the results statistically independent. In each simulation the primary particles were electrons. Their energy spectrum was defined as a user specified histogram with a bin number of 186 and linear interpolation between the data points. It was specified according to the -spectrum of tritium and primary particles were emitted isotropically. Sources were defined as cylindrical volume source (No. 1, Table 2) or circular shaped 2D sources (Nos. 2–9, Table 2). An overview is given in Table 2. The detection efficiencies for tritium adsorbed on the sample surface (No. 6, Table 2) and the gold-coated beryllium window (No. 5, Table 2) were simulated to quantify the amount of tritium on the sample surface from the measured X-ray intensity. Signal contributions from background sources, as residual gas in the sample chamber (No. 1, Table 2) or tritium adsorbed on inner surfaces of the sample chamber (Nos. 2–4, Table 2), must also be quantified. For future tritium measurements with an uncoated beryllium sample, the detection efficiency for tritium adsorbed on a bare beryllium surface was simulated (No. 7, Table 2). As there is also the possibility for tritium absorption in the bulk, the detection efficiencies for tritium in five different bulk depths of the sample have been simulated (No. 8, Table 2). It is already reported that geometrical uncertainties can have a strong influence on Monte Carlo simulation results of BIXS systems [7]. Therefore, the influence of small shifts in the detector position on the detection efficiency, for tritium adsorbed on the sample, has been investigated (No. 9, Table 2). 5. Discussions of the results Simulation results for the detection efficiencies are given in Table 3. Due to the small distance to the detector and the direct
Please cite this article in press as: M. Röllig, et al., Geant4 Monte Carlo simulations for sensitivity investigations of an experimental facility for the measurement of tritium surface contaminations by BIXS, Fusion Eng. Des. (2016), http://dx.doi.org/10.1016/j.fusengdes.2016.02.018
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Fig. 2. Left: Simulated geometry of the TRIADE core setup. Right: The enlarged SDD geometry. Since the collimator material is unknown it was replaced by zirconium in the simulation. Drawings are not in scale.
Table 2 Overview of the MC-simulations. In each run 109 -electron events were simulated. In simulation No. 1 only the gas column between the sample surface and the goldcoated beryllium window was simulated to quantify the influence of residual gas. No.
Description
No. of runs
1 2 3 4 5 6 7 8 9
Residual gas Be window CF flange Sample ring holder Recipient wall Be entrance window Au sample surface Be sample surface Au sample bulk SDD position ±2 mm
2 10 10 2 2 23 2 5×8 10 × 4
Table 3 Detection efficiencies (DE) for adsorbed tritium on different surfaces, absorbed tritium in the sample bulk and gaseous tritium in the recipient. Description
DE (cps/Bq)
Area (cm2 )
Residual gas Be window CF flange Sample ring holder Recipient wall Be entrance window Au sample surface Be sample surface Au sample bulk 20 nm Au sample bulk 40 nm Au sample bulk 60 nm Au sample bulk 80 nm Au sample bulk 100 nm
(8.05 ± 0.63) × 10−8 (1.20 ± 0.09) × 10−7 (1.20 ± 0.07) × 10−8 (6.00 ± 1.73) × 10−9 (1.61 ± 0.03) × 10−6 (3.12 ± 0.06) × 10−7 (8.60 ± 0.66) × 10−8 (3.88 ± 0.10) × 10−7 (3.92 ± 0.10) × 10−7 (3.71 ± 0.10) × 10−7 (2.98 ± 0.09) × 10−7 (1.56 ± 0.04) × 10−7
– 35.3 41.1 344.7 3.1 28.3 28.3 28.3 28.3 28.3 28.3 28.3
background sources could be strongly reduced by an external magnetic field. One monolayer of tritium on all simulated inner surfaces of the sample chamber, including the sample surface, would induce a detector signal of 22.2 ± 0.8 cps. This is well above the intrinsic detector background of (4.11 ± 0.13) × 10−3 cps of the used X-ray detector in the TRIADE setup. The minimum mean count rate of the detector in a measurement time of 1000 s which can be reported, is calculated according to the Currie equation [8] to 12.1 × 10−3 cps. Assuming the same coverage on all inner surfaces of the sample chamber, this result can be translated into a minimum detectable activity of TRIADE for tritium contamination on the sample surface of 600 Bq/cm2 . Assuming an ideal plane (100) gold sample surface, the minimum measurable surface contamination in terms of monolayer is 5.5 × 10−4 monolayer. Statistical errors are neglected in this calculation due to the unquantifiable systematical errors of the underlying assumptions. Residual gas in the recipient contributes rather weakly to the detector signal. Assuming a tritium partial pressure of 10−1 Pa, the expected count rate would be 0.35 ± 0.02 cps. This source of background can not be suppressed by an external magnetic field but is low enough to allow tritium measurements at tritium partial pressures of 1 Pa with sub-monolayer sensitivity. A significant background contribution can be caused by tritium absorbed in the bulk. Tritium depth profiling by BIXS on a m scale has been reported in the literature [9], [10] but it is regarded as very difficult on a 100 nm scale. Simulated spectral shapes for a
line of sight, the detection efficiencies for adsorbed tritium are highest for the sample and the gold-coated beryllium window. Assuming one monolayer of tritium coverage on ideal (100) gold surfaces, the detector signal contribution from tritium adsorbed on the gold-coated beryllium window surface would be 5.4 ± 0.1 cps. The detector signal contribution from tritium adsorbed on the sample surface would be 9.5 ± 0.1 cps and is therefore dominant. This is due to the larger surface area. Background sources (Nos. 2–4, Table 3) contribute to the detector signal by 7.3 ± 0.8 cps, assuming also one monolayer and ideal (100) gold surfaces. The dominant source of background is adsorbed tritium on the gold-coated stainless steel flange of the beryllium window with a fraction of 4.6 ± 0.3 cps. Even if there is no direct line of sight, due to the large backscattering probability of -electrons from the gold surface of the stainless steel flange, electrons emitted from there can hit the sample surface. X-rays produced there have a rather large detection probability. These contributions from
Detection efficiency (cps/Bq/ΔE)
1.6E-7
Au Mα
Sample surface Sample bulk 20 nm Sample bulk 40 nm Sample bulk 60 nm Sample bulk 80 nm Sample bulk 100 nm 1σ error bars
1.2E-7 8.0E-8 4.0E-8 2.0E-8 1.5E-8 1.0E-8 5.0E-9 0.0 0
5
10 Energy (keV)
15
Fig. 3. Bremsstrahlung intensity and spectral shape depending on the sample bulk depth of absorbed tritium.
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X-ray window surface Sample surface
DE(x)/DE(0) (%)
150 125 100 75 50
-2
-1
0 1 Detector shift (mm)
2
Fig. 4. Detection efficiency (DE) depending on the SDD position. Error bars indicating 1 statistical uncertainties. Values are normalized to the zero position of the SDD as denoted in Table 3.
bulk depth of 100 nm are shown in Fig. 3. All spectra show the typical spectral shape of a continuous Bremsstrahlung part superimposed by characteristic fluorescence lines [11]. As even in the first 10 nm of the bulk significant amounts of tritium could accumulate, a distinction between adsorbed and absorbed tritium by analysis of the spectral shape is not possible with the TRIADE setup. The detection efficiencies for tritium absorbed in the sample bulk, as noted in Table 3, are in the same order as for adsorbed tritium. Further systematic uncertainties can emerge from deviations of the simulated geometry compared to the actual geometry of the setup. As a crucial part, the influence of small shifts in the detector position has been studied as shown in Fig. 4. Both the detection efficiency for adsorbed tritium on the beryllium window and on the sample surface increase for shorter distances. In the worst case, the simulated detection efficiencies can vary up to 30% for shifts in the detector position between ±2 mm. 6. Conclusions The Monte Carlo simulation results for the detection efficiency clearly show, that the TRIADE facility is able to measure tritium surface contamination on a gold-coated sample with a sub-monolayer sensitivity. However, due to the measurement principle absorbed tritium is indistinguishable from adsorbed tritium. Therefore TRIADE can only give an upper limit for tritium adsorption on the sample. A handle for the discrimination between adsorbed and absorbed tritium could come from the time response of the detector count rate during tritium exposure. As absorption and adsorption are different physical processes a different time response is expected here. This is part of future studies. Tritium contamination of arbitrary solid samples can be quantified if the contamination of the sample under tritium exposure is significant higher than for gold. Literature data implies, that this is the case for most materials [12]. In that case count rate contributions from the gold-coated surfaces can simply be neglected. If the tritium contamination on the sample is of the same order of magnitude as on the gold-coated surfaces of the inner recipient, then it strongly depends on the level of repeatability of the
tritium measurements with gold to subtract the expected count rate contribution as background. This is also part of future studies. Systematic uncertainties can also emerge from several background sources within the sample volume. Even if tritium adsorption on the sample surface causes the dominant contribution to the detector signal, in case of a gold-coated sample, tritium adsorption on other surfaces within the sample chamber causing significant systematic uncertainties. An external magnetic field which guides the -electrons could help to reduce this effect. During first tritium measurements at exposure pressures of 5 × 10−3 Pa a maximum count rate of 1.10 ± 0.04 cps was measured. Assuming an ideal plane (100) gold surface and identical adsorption behavior on all gold-coated surfaces in the sample chamber this can be translated into a surface contamination of ≈0.05 monolayer or ≈5.4 × 104 Bq/cm2 . This must be regarded as a momentary upper limit as a linear increase of the detector count rate with applied dosage p × t was measured. Further measurements with TRIADE will show if a saturation level in the surface contamination can be reached and what the time response is. With that, TRIADE will provide the necessary input data for KATRIN and the qualification of its BIXS system. Further measurements with fusion relevant material samples will be published in the near future. Acknowledgements A part of this work was supported by the Helmholtz Association of National Research Centers. The authors would also like to thank all their colleagues from TLK for the support during this work. References [1] M. Nishikawa, T. Takeishi, Y. Kawamura, Y. Takagi, Y. Matsumoto, Tritium mass balance in the piping system of a fusion reactor, Fusion Technol. 21 (2) (1992) 878–882. [2] , in: Tritium handling issues in fusion reactor materials, J. Nucl. Mater. 417 (1–3) (2011) 545–550. [3] M. Babutzka, M. Bahr, J. Bonn, B. Bornschein, A. Dieter, G. Drexlin, K. Eitel, S. Fischer, F. Glück, S. Grohmann, M. Hötzel, T.M. James, W. Käfer, M. Leber, B. Monreal, F. Priester, M. Röllig, M. Schlösser, U. Schmitt, F. Sharipov, M. Steidl, M. Sturm, H.H. Telle, N. Titov, in: Monitoring of the operating parameters of the KATRIN windowless gaseous tritium source, New J. Phys. 14 (10) (2012) 103046. [4] K. Collaboration, Katrin Design Report 2004, FZKA Report 7090. [5] M. Matsuyama, K. Watanabe, K. Hasegawa, in: Tritium assay in materials by the Bremsstrahlung counting method, Fusion Eng. Des. 39–40 (1998) 929–936. [6] S.A., et al., in: Geant4 – a simulation toolkit, Nucl. Instrum. Methods Phys. Res. Sect. A: Accel. Spectrom. Detect. Assoc. Equip. 506 (3) (2003) 250–303. [7] L. Mao, Z. An, Q. Wu, H. Sun, H. Chen, X. Zhou, in: Effect of geometrical parameters uncertainty of {BIXS} experimental setup for tritium analysis, Nucl. Instrum. Methods Phys. Res. Sect. B: Beam Interact. Mater. Atoms 289 (2012) 52–55. [8] L.A. Currie, in: Limits for qualitative detection and quantitative determination. application to radiochemistry, Anal. Chem. 40 (3) (1968) 586–593. [9] Z. An, Q. Hou, J. Long, in: Reconstruction of depth distribution of tritium in materials by -ray induced X-ray spectrometry, Nucl. Instrum. Methods Phys. Res. Sect. B: Beam Interact. Mater. Atoms 266 (16) (2008) 3643–3646. [10] W. Zhang, H. Sun, F. Zeng, L. Mao, Q. Wu, J. Zhu, Z. An, in: Tritium analysis in titanium films by the {BIXS} method, Nucl. Instrum. Methods Phys. Res. Sect. B: Beam Interact. Mater. Atoms 275 (2012) 20–23. [11] M. Röllig, F. Priester, M. Babutzka, J. Bonn, B. Bornschein, G. Drexlin, S. Ebenhöch, E.W. Otten, M. Steidl, M. Sturm, in: Activity monitoring of a gaseous tritium source by beta induced x-ray spectrometry, Fusion Eng. Des. 88 (6) (2013) 1263–1266. [12] M. Okada, M. Nakamura, K. Moritani, T. Kasai, in: Dissociative adsorption of hydrogen on thin au films grown on Ir1 1 1, Surf. Sci. 523 (3) (2003) 218–230.
Please cite this article in press as: M. Röllig, et al., Geant4 Monte Carlo simulations for sensitivity investigations of an experimental facility for the measurement of tritium surface contaminations by BIXS, Fusion Eng. Des. (2016), http://dx.doi.org/10.1016/j.fusengdes.2016.02.018