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17 pages, 11667 KiB  
Article
Silicon Drift Detectors for the Measurement and Reconstruction of Beta Spectra
by Andrea Nava, Leonardo Bernardini, Matteo Biassoni, Tommaso Bradanini, Marco Carminati, Giovanni De Gregorio, Carlo Fiorini, Giulio Gagliardi, Peter Lechner, Riccardo Mancino and Chiara Brofferio
Sensors 2024, 24(24), 8202; https://doi.org/10.3390/s24248202 - 22 Dec 2024
Viewed by 485
Abstract
The ASPECT-BET project, or An sdd-SPECTrometer for BETa decay studies, aims to develop a novel technique for the precise measurement of forbidden beta spectra in the 10 keV–1 MeV range. This technique employs a Silicon Drift Detector (SDD) as the main spectrometer with [...] Read more.
The ASPECT-BET project, or An sdd-SPECTrometer for BETa decay studies, aims to develop a novel technique for the precise measurement of forbidden beta spectra in the 10 keV–1 MeV range. This technique employs a Silicon Drift Detector (SDD) as the main spectrometer with the option of a veto system to reject events exhibiting only partial energy deposition in the SDD. A precise understanding of the spectrometer’s response to electrons is crucial for accurately reconstructing the theoretical shape of the beta spectrum. To compute this response, GEANT4 simulations optimized for low-energy electron interactions are used and validated with a custom-made electron gun. In this article we present the performance of these simulations in reconstructing the electron spectra measured with SDDs of a 109Cd monochromatic source, both in vacuum and in air. The allowed beta spectrum of a 14C source was also measured and analyzed, proving that this system is suitable for the application in ASPECT-BET. Full article
Show Figures

Figure 1

Figure 1
<p>47-pixel SDD matrix used for all the measurements here reported (<b>left</b>). Scheme of the 47 pixels: only the 7 red ones were acquired (<b>right</b>).</p>
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<p>Vacuum chamber in the Milano-Bicocca laboratory. The main detectors operated in this setup, a 47-pixel SDD matrix and a Pixet, are indicated. The e-gun, attached to a xy-movable stage, is also highlighted.</p>
Full article ">Figure 3
<p>(<b>Left</b>): picture of the e-gun. (<b>Center</b>): anode with 7 LEDs used to illuminate the cathode and the aluminum cylinder with UV light. (<b>Right</b>): gold-coated cathode used to collimate the electron beam. The aluminum cylinder, where electrons are produced, is visible in its center.</p>
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<p>(<b>Left</b>): CAD drawing of the e-gun. The most important components are highlighted. (<b>Right</b>): COMSOL simulation of the electron beam produced with the e-gun.</p>
Full article ">Figure 5
<p>(<b>Top</b>): measurement of the e-gun beam spot performed with the Pixet. A beam size of ∼0.5 mm and a rate <math display="inline"><semantics> <msup> <mn>10</mn> <mn>4</mn> </msup> </semantics></math> electrons/s were found. (<b>Bottom</b>): measurement of a 10 keV electron spectrum acquired with an SDD. Only the central part of the pixel was hit with the e-gun beam. The best fit of the spectrum done with the detector model is also shown.</p>
Full article ">Figure 6
<p>CAD schematics of the setup used in the measurements. Section of source and detector (<b>top</b>), and side view (<b>bottom</b>).</p>
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<p>M1 Data acquired with the SDD main pixel in a 3-h measurement. The energy of the X-ray peaks and the tag of the different IC electrons are shown.</p>
Full article ">Figure 8
<p>Data-MC comparison for different values of the effective Mylar thickness (<b>top</b>). Reduced <math display="inline"><semantics> <msup> <mi>χ</mi> <mn>2</mn> </msup> </semantics></math> as a function of the effective Mylar thickness (<b>bottom</b>). The best-fit value of 7.2 μm is extracted through a parabolic fit.</p>
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<p>Best fit of MC prediction to the data set acquired with the <sup>109</sup>Cd source in vacuum. The fit is done only in the non-shaded area.</p>
Full article ">Figure 10
<p>Comparison of MC prediction to the dataset acquired with the <sup>109</sup>Cd source in air. The comparison is done only in the non-shaded area.</p>
Full article ">Figure 11
<p>Data acquired with the main SDD in a 6 h measurement using a <sup>14</sup>C source (<b>top</b>). <math display="inline"><semantics> <msup> <mi>χ</mi> <mn>2</mn> </msup> </semantics></math> as a function of the effective Mylar thickness for the two models: the Fermi theory prediction and the one including the experimental shape factor (<b>bottom</b>). The best fit for the effective Mylar thickness is 4.5 μm.</p>
Full article ">Figure 12
<p>Fit to the data set acquired with the <sup>14</sup>C source in vacuum using the MC prediction. The fit is performed only in the non-shaded area. The theoretical input is also shown.</p>
Full article ">Figure 13
<p>Fits obtained by varying <math display="inline"><semantics> <mi>λ</mi> </semantics></math> (<b>top</b>), the baseline resolution <math display="inline"><semantics> <mi>σ</mi> </semantics></math> (<b>center</b>) and the charge cloud width in Si (<b>bottom</b>).</p>
Full article ">Figure 13 Cont.
<p>Fits obtained by varying <math display="inline"><semantics> <mi>λ</mi> </semantics></math> (<b>top</b>), the baseline resolution <math display="inline"><semantics> <mi>σ</mi> </semantics></math> (<b>center</b>) and the charge cloud width in Si (<b>bottom</b>).</p>
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<p>Fits obtained by varying the production cut for secondaries in GEANT4 (<b>top</b>) or the physics list used (<b>bottom</b>) in the simulations.</p>
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13 pages, 2277 KiB  
Article
Modelling Potential Candidates for Targeted Auger Therapy
by Conor M. J. Buchanan, Eric O. Aboagye, Lee J. Evitts, Michael J. D. Rushton and Tim A. D. Smith
Biophysica 2024, 4(4), 711-723; https://doi.org/10.3390/biophysica4040046 - 18 Dec 2024
Viewed by 521
Abstract
Targeted Auger emitters are being considered as a cancer treatment owing to the high linear energy transfer of Auger electrons. When targeted to cancers, this allows for a highly efficient treatment with a low risk of damage to surrounding healthy tissue. The purpose [...] Read more.
Targeted Auger emitters are being considered as a cancer treatment owing to the high linear energy transfer of Auger electrons. When targeted to cancers, this allows for a highly efficient treatment with a low risk of damage to surrounding healthy tissue. The purpose of this study was to determine the most DNA-damaging Auger emitters from a range of radionuclides, some of which are clinically utilised. A Monte Carlo method-based software (Geant4-DNA version 10.7) was used to determine the energy deposition and number of DNA double-strand breaks from Auger (and internal conversion) electrons imposed on a tetranucleosome. The Auger emitters, 119Sb and 103Pd, have similar or slightly greater damaging properties compared to 123I, 111In, and 89Zr. 193mPt demonstrated the greatest therapeutic potency. Whilst 125I was highly damaging, its relatively long half-life (60 days) makes it less desirable for clinical use. Geant4-DNA modelling identified the radionuclide 193mPt as being highly favourable for use in radiotherapy. Full article
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Figure 1
<p>A visual representation of targeted Auger therapy showing the radiopharmaceutical being taken up by a cancer cell into the cell nucleus where Auger electrons target the cell DNA.</p>
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<p>Detailed illustration of Auger electron emission following either electron capture or internal conversion processes.</p>
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<p>Track length, projected length, and penetration of incident electrons on a 1m radius sphere of liquid water.</p>
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<p>The simulated dose on the tetranucleosome from five prospective Auger-emitting radionuclides. Each is split to show the contribution from Auger electrons, conversion electrons, and β<sup>−</sup> particles.</p>
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<p>The equivalent dose applied on the tetranucleosome by four radionuclides currently used in nuclear medicine showing the contributions from Auger, conversion electrons, and β<sup>−</sup> particles.</p>
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<p>The number of double-strand breaks induced by low-energy electrons of increasing energy.</p>
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<p>The number of double-strand breaks induced in the tetranucleosome showing contributions from Auger electrons, conversion electrons, and β<sup>−</sup> particles emitted from novel radionuclides.</p>
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<p>The number of double-strand breaks induced on the tetranucleosome by radionuclides currently used in nuclear medicine showing contributions from Auger electrons, conversion electrons, and β<sup>−</sup> particles.</p>
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16 pages, 6075 KiB  
Article
A Comparative Study of Neutron Shielding Performance in Al-Based Composites Reinforced with Various Boron-Containing Particles for Radiotherapy: A Monte Carlo Simulation
by Shiyan Yang, Yupeng Yao, Hanlong Wang and Hai Huang
Nanomaterials 2024, 14(21), 1696; https://doi.org/10.3390/nano14211696 - 23 Oct 2024
Viewed by 894
Abstract
This study aimed to assess and compare the shielding performance of boron-containing materials for neutrons generated in proton therapy and used in boron neutron capture therapy (BNCT). Five composites, including AlB2, Al-B4C, Al-TiB2, Al-BN, and Al-TiB2 [...] Read more.
This study aimed to assess and compare the shielding performance of boron-containing materials for neutrons generated in proton therapy and used in boron neutron capture therapy (BNCT). Five composites, including AlB2, Al-B4C, Al-TiB2, Al-BN, and Al-TiB2-BN, were selected as shielding materials, with concrete used as a benchmark. The mass fraction of boron compounds in these materials ranged from 10% to 50%. The Monte Carlo toolkit Geant4 was employed to calculate shielding parameters, including neutron ambient dose equivalent, dose values, and macroscopic cross-section. Results indicated that, compared to concrete, these boron-containing materials more effectively absorb thermal neutrons. When the boron compound exceeds 30 wt.%, these materials exhibit better shielding performance than concrete of the same thickness for neutrons generated by protons. For a given material, its shielding capability increases with boron content. Among the five materials when the material thickness and boron compound content are the same, the shielding performance for neutrons generated by protons, from best to worst, is as follows: Al-TiB2, Al-B4C, AlB2, Al-TiB2-BN, and Al-BN. For BNCT, the shielding performance from best to worst is in the following order: Al-B4C, AlB2, Al-TiB2, Al-TiB2-BN, and Al-BN. The results of this study provide references and guidelines for the selection and optimization of neutron shielding materials in proton therapy and BNCT facilities. Full article
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Figure 1
<p>(<b>a</b>) Schematic diagram of the simulation setup for secondary neutrons produced by protons. (<b>b</b>) Three proton SOBPs with different modulation ranges were used as primary sources.</p>
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<p>Secondary neutron spectral fluence produced by protons with different energies recorded at various detector angles. All figures share the same legend.</p>
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<p>Neutron energy spectrum from the p-Li reaction at a proton energy of 2.8 MeV.</p>
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<p>The neutron spectral fluence recorded in the ICRU sphere after passing through different shielding materials of various thicknesses. The first row shows the energy spectra of the three studied neutrons at the source plan.</p>
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<p>Elastic and inelastic collision cross sections of neutrons in different elements that constitute the shielding materials.</p>
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<p>Neutron ambient dose equivalent values for different shielding materials. The left, middle, and right columns show the results for n-BNCT, n-SOBP1, and n-SOBP3, respectively. Each row uses the same legend.</p>
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<p>Dose distribution of different particles after passing through various thicknesses of concrete for the three studied neutron spectra.</p>
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<p>Comparison of total dose distribution for different materials at varying thicknesses and boron contents. The left, middle, and right columns present the results for n-BNCT, n-SOBP1, and n-SOBP3, respectively. Each row uses the same legend.</p>
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<p>Neutron macroscopic cross-section for various materials with different boron contents at 10 cm for n-BNCT (<b>top row</b>) and 50 cm thickness for n-SOBP1 (<b>middle row</b>) and n-SOBP3 (<b>bottom row</b>). The gray line represents the results of concrete at the corresponding thickness. Each column uses the same legend.</p>
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11 pages, 1991 KiB  
Article
Research on B4C/PEEK Composite Material Radiation Shielding
by Hongxia Li, Hongping Guo, Hui Tu, Xiao Chen and Xianghua Zeng
Polymers 2024, 16(20), 2902; https://doi.org/10.3390/polym16202902 - 15 Oct 2024
Viewed by 710
Abstract
There are various types of charged particles in the space environment, which can cause different types of radiation damage to materials and devices, leading to on-orbit failures and even accidents for spacecraft. Developing lightweight and efficient radiation-shielding materials is an effective approach to [...] Read more.
There are various types of charged particles in the space environment, which can cause different types of radiation damage to materials and devices, leading to on-orbit failures and even accidents for spacecraft. Developing lightweight and efficient radiation-shielding materials is an effective approach to improving the inherent protection of spacecraft. The protective performance of different materials against proton and electron spectra in the Earth’s radiation belts is evaluated using a Geant4 simulation. Based on the simulation results, suitable hardening components were selected to design composite materials, and B4C/PEEK composites with different B4C contents were successfully prepared. The experimental results demonstrate that the simulated and experimental results for the electron, proton and neutron shielding performance of the B4C/PEEK composites are consistent. These composites exhibit excellent radiation shielding capabilities against electrons, protons and neutrons, and the radiation protection performance improves with increasing B4C content in the B4C/PEEK composite materials. Full article
(This article belongs to the Special Issue Advances in Functional Polymer Nanocomposites)
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Figure 1
<p>Differential cumulative flux spectra of LEO orbital protons protected by different materials and the same mass thickness: (<b>a</b>) differential cumulative flux spectra after different material and thickness protection; (<b>b</b>) proton protection effect of different materials with the same mass thickness.</p>
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<p>Differential cumulative flux spectra of secondary neutrons and gamma rays after LEO orbital protons pass through protective layers of different thicknesses: (<b>a</b>) secondary neutrons; (<b>b</b>) <span class="html-italic">γ</span> rays.</p>
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<p>The differential cumulative flux spectra of the second gamma of GEO orbital electrons through 2 mm protective layer of different materials.</p>
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<p>Simulation calculation results of 1 MeV electron and 16 MeV proton protection rate of materials: (<b>a</b>) protective effect of different materials; (<b>b</b>) electronic protective effect of B<sub>4</sub>C/PEEK at different B<sub>4</sub>C addition levels; and (<b>c</b>) 16 MeV proton protective effect of B<sub>4</sub>C/PEEK at different B<sub>4</sub>C addition levels.</p>
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<p>Preparation flow diagram of B<sub>4</sub>C/PEEK composite materials.</p>
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<p>Distribution curves of film dose tablets along the thickness of B<sub>4</sub>C/PEEK under electron irradiation.</p>
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<p>Proton transmittance of B<sub>4</sub>C/PEEK composites changes with energy: (<b>a</b>) 10 wt% B<sub>4</sub>C/PEEK, (<b>b</b>) 20 wt% B<sub>4</sub>C/PEEK.</p>
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<p>Neutron protection curves of B<sub>4</sub>C/PEEK composite materials with different B<sub>4</sub>C addition amounts: (<b>a</b>) variation curves of neutron count and wavelength; (<b>b</b>) change curves of wavelength and protection effect.</p>
Full article ">
23 pages, 7773 KiB  
Article
Search for True Ternary Fission in Reaction 40Ar + 208Pb
by Md Ashaduzzaman, Antonio Di Nitto, Emanuele Vardaci, Giovanni La Rana, Pia Antonella Setaro, Tathagata Banerjee, Antonio Vanzanella and Giuseppe Alifano
Appl. Sci. 2024, 14(18), 8522; https://doi.org/10.3390/app14188522 - 21 Sep 2024
Viewed by 806
Abstract
True ternary fission, the fission of a nucleus into three fragments of nearly equal mass, is an elusive and poorly known process influenced by shell effects. An increase in the probability of this process with respect to binary fission, which is very low [...] Read more.
True ternary fission, the fission of a nucleus into three fragments of nearly equal mass, is an elusive and poorly known process influenced by shell effects. An increase in the probability of this process with respect to binary fission, which is very low in spontaneous and neutron-induced fission, has been envisaged. Heavy-ion-induced reactions are adopted due to the possibility of an increase in the fissility parameter and the excitation energy of the compound nuclei. Nuclei with mass number around A = 250, accessible in heavy-ion-induced reactions, are favorable and should be investigated. It is still debated if the process takes place in a single step, direct ternary fission, or in a two step, sequential ternary fission. The purpose of this work is to define experimental conditions and observables that allow the disentangling of the products from the direct and sequential ternary fission, as well as from the usual most probable binary fission. This step is essential for gaining insights into the ternary fission dynamics and the binary to ternary fission competition. The method proposed here is for simulating the kinematics of the ternary and binary fission processes to compute the energy distributions and angular correlations of direct and sequential ternary fission products, as well as those of binary fission. The reaction taken as a benchmark is 40Ar + 208Pb at 230 MeV and is supposed to form the 248Fm* compound nucleus. The simulation results have been filtered by considering the response function of a multi-coincidence detection system virtually constructed using the Geant4 simulation toolkit. The simulations support the possibility of separating the products of different multimodal fission decays with the proposed setup that consequently represents an effective tool to obtain insights into ternary fission from the observables selected. Full article
(This article belongs to the Section Applied Physics General)
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Figure 1
<p>In-plane kinematic plots of direct ternary fission (DTF-<b>top</b>) and sequential ternary fission(STF-<b>bottom</b>). In the DTF a simultaneous formation of the <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>A</mi> <mn>2</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <msub> <mi>A</mi> <mn>3</mn> </msub> </semantics></math> fragments occurs. In STF the first binary scission produces the <math display="inline"><semantics> <msub> <mi>A</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>A</mi> <mn>23</mn> </msub> </semantics></math> fragments, the subsequent binary scission of the <math display="inline"><semantics> <msub> <mi>A</mi> <mn>23</mn> </msub> </semantics></math> fragment produces the final products <math display="inline"><semantics> <msub> <mi>A</mi> <mn>2</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>A</mi> <mn>3</mn> </msub> </semantics></math>.</p>
Full article ">Figure 2
<p>Dalitz plot of the potential energies for collinear (<b>a</b>) and equatorial (<b>b</b>) ternary fragmentations of <sup>248</sup>Fm (Z = 100). The potential energies are calculated as a function of the charge numbers with the constraint for the fragment masses <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mn>1</mn> </msub> <mo>≥</mo> <msub> <mi>A</mi> <mn>2</mn> </msub> <mo>≥</mo> <msub> <mi>A</mi> <mn>3</mn> </msub> </mrow> </semantics></math>.</p>
Full article ">Figure 3
<p>Potential Energy of a ternary fragmentation involving two out of three double magic nuclei in collinear and equatorial configurations.</p>
Full article ">Figure 4
<p>Kinetic energies of light fragments <math display="inline"><semantics> <msub> <mi>E</mi> <mn>3</mn> </msub> </semantics></math> presented as a function of <math display="inline"><semantics> <msub> <mi>E</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>E</mi> <mn>2</mn> </msub> </semantics></math> by assuming DTF (<b>a</b>) and STF (<b>b</b>) mechanisms. The calculations have been performed with the conditions: <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mn>1</mn> </msub> <mo>≥</mo> <msub> <mi>A</mi> <mn>2</mn> </msub> <mo>≥</mo> <msub> <mi>A</mi> <mn>3</mn> </msub> </mrow> </semantics></math>, emission of <math display="inline"><semantics> <msub> <mi>A</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>A</mi> <mn>2</mn> </msub> </semantics></math> on the opposite side with respect to the beam direction and wide range for the <math display="inline"><semantics> <msub> <mi>A</mi> <mn>1</mn> </msub> </semantics></math> energies. See the text for more details.</p>
Full article ">Figure 5
<p>Emission angles of light fragment <math display="inline"><semantics> <msub> <mi>θ</mi> <mn>3</mn> </msub> </semantics></math> as a function of the heavy (<math display="inline"><semantics> <msub> <mi>θ</mi> <mn>1</mn> </msub> </semantics></math>) and medium (<math display="inline"><semantics> <msub> <mi>θ</mi> <mn>2</mn> </msub> </semantics></math>) mass angles in the laboratory system calculated considering the DTF (<b>a</b>) and STF (<b>b</b>) mechanisms. In the calculations, <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mn>1</mn> </msub> <mo>≥</mo> <msub> <mi>A</mi> <mn>2</mn> </msub> <mo>≥</mo> <msub> <mi>A</mi> <mn>3</mn> </msub> </mrow> </semantics></math> and two of the three fragments are doubly magic nuclei. See the text for more details.</p>
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<p>Kinetic energies of the product of <sup>132</sup>Sn + <sup>68</sup>Zn + <sup>48</sup>Ca tripartition. The products of the DTF and STF mechanisms are shown in the left and right panels, respectively.</p>
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<p>Angular correlations of the <sup>132</sup>Sn + <sup>68</sup>Zn + <sup>48</sup>Ca tripartition. The DTF (blue) and STF (pink) angular correlations <math display="inline"><semantics> <msub> <mi>θ</mi> <mn>2</mn> </msub> </semantics></math> vs. <math display="inline"><semantics> <msub> <mi>θ</mi> <mn>3</mn> </msub> </semantics></math> are presented for the fixed heavy fragment angle <math display="inline"><semantics> <msub> <mi>θ</mi> <mn>1</mn> </msub> </semantics></math> described at the top of each panel.</p>
Full article ">Figure 8
<p>DTF angular correlations of the <sup>132</sup>Sn + <sup>68</sup>Zn + <sup>48</sup>Ca tripartition for <math display="inline"><semantics> <msub> <mi>θ</mi> <mn>2</mn> </msub> </semantics></math> = <math display="inline"><semantics> <mrow> <mo>−</mo> <msub> <mi>θ</mi> <mn>1</mn> </msub> </mrow> </semantics></math>.</p>
Full article ">Figure 9
<p>Energy correlations of the <sup>132</sup>Sn + <sup>68</sup>Zn + <sup>48</sup>Ca tripartition. The DTF (blue) and STF (pink) energy correlations <math display="inline"><semantics> <msub> <mi>E</mi> <mn>2</mn> </msub> </semantics></math> vs. <math display="inline"><semantics> <msub> <mi>E</mi> <mn>3</mn> </msub> </semantics></math> are presented for the fixed heavy fragment angle <math display="inline"><semantics> <msub> <mi>θ</mi> <mn>1</mn> </msub> </semantics></math> described at the top of each panel.</p>
Full article ">Figure 10
<p>Energies and angles of the light fragments from the <sup>132</sup>Sn + <sup>68</sup>Zn + <sup>48</sup>Ca tripartition. Under the condition of angle symmetric emission of the medium and heavy fragment at <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>1</mn> </msub> <mo>=</mo> <mo>−</mo> <msub> <mi>θ</mi> <mn>2</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>70</mn> <mo>°</mo> </mrow> </semantics></math>, the angular and energy distributions of the <sup>48</sup>Ca fragments are shown in panels (<b>a</b>,<b>b</b>), while the angle vs. energy correlations are shown in panel (<b>c</b>), assuming both DTF (black) and STF (pink) processes. For details on energy, angular intervals, and steps considered in calculations, see the text.</p>
Full article ">Figure 11
<p>Angular correlations of BF events. The distributions of fragment 2 (<math display="inline"><semantics> <msub> <mi>θ</mi> <mn>2</mn> </msub> </semantics></math>) vs. fragment 1 (<math display="inline"><semantics> <msub> <mi>θ</mi> <mn>1</mn> </msub> </semantics></math>) for symmetric (blue line) and asymmetric fission (orange line). The green boxes represent the two angular ranges covered by the detection setup proposed in <a href="#sec2dot4-applsci-14-08522" class="html-sec">Section 2.4</a>.</p>
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<p>Schematic drawing of the detection apparatus consisting of three different detector types. The blue line superimposed on the z-axis (blue arrow) identifies the beam direction. See the text for more details.</p>
Full article ">Figure 13
<p>Velocity distributions of coincident events (<math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>2</mn> </msub> <mo>=</mo> <mo>−</mo> <msub> <mi>θ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>70</mn> <mo>°</mo> </mrow> </semantics></math>). The spectra at <math display="inline"><semantics> <msub> <mi>θ</mi> <mn>1</mn> </msub> </semantics></math> (<b>a</b>) and <math display="inline"><semantics> <msub> <mi>θ</mi> <mn>2</mn> </msub> </semantics></math> (<b>b</b>) correspond to the heavier and middle mass fragments in the case of TF, respectively. In asymmetric BF the heavier fragment is measured at <math display="inline"><semantics> <msub> <mi>θ</mi> <mn>1</mn> </msub> </semantics></math>. The location of the coincident events in the two-dimensional matrix <math display="inline"><semantics> <msub> <mi>v</mi> <mn>1</mn> </msub> </semantics></math> vs. <math display="inline"><semantics> <msub> <mi>v</mi> <mn>2</mn> </msub> </semantics></math> are shown in panel (<b>c</b>).</p>
Full article ">Figure 14
<p>Energy spectra measured with the two stages telescope detector at <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>90</mn> <mo>°</mo> <mo>±</mo> <mn>15</mn> <mo>°</mo> </mrow> </semantics></math>. The spectra are simulated for the thin <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (<b>a</b>) and thick <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>s</mi> </mrow> </msub> </semantics></math> (<b>b</b>) detectors in the case of the 4 processes taken into account.</p>
Full article ">Figure 15
<p>Energy-velocity matrix of fragments emitted at <math display="inline"><semantics> <mrow> <mn>60</mn> <mo>°</mo> <mo>±</mo> <mn>5</mn> <mo>°</mo> </mrow> </semantics></math>.</p>
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<p><math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> <mo>−</mo> <mi>E</mi> </mrow> </semantics></math> matrix of nuclei with A = 48 and different atomic numbers. The width of the distribution in <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> depends mainly on the energy resolution of the detectors. Here we assume for both detectors an energy resolution (FWHM) of 900 keV.</p>
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18 pages, 2273 KiB  
Article
Optimization of the Pixel Design for Large Gamma Cameras Based on Silicon Photomultipliers
by Carolin Wunderlich, Riccardo Paoletti and Daniel Guberman
Sensors 2024, 24(18), 6052; https://doi.org/10.3390/s24186052 - 19 Sep 2024
Viewed by 818
Abstract
Most single-photon emission computed tomography (SPECT) scanners employ a gamma camera with a large scintillator crystal and 50–100 large photomultiplier tubes (PMTs). In the past, we proposed that the weight, size and cost of a scanner could be reduced by replacing the PMTs [...] Read more.
Most single-photon emission computed tomography (SPECT) scanners employ a gamma camera with a large scintillator crystal and 50–100 large photomultiplier tubes (PMTs). In the past, we proposed that the weight, size and cost of a scanner could be reduced by replacing the PMTs with large-area silicon photomultiplier (SiPM) pixels in which commercial SiPMs are summed to reduce the number of readout channels. We studied the feasibility of that solution with a small homemade camera, but the question on how it could be implemented in a large camera remained open. In this work, we try to answer this question by performing Geant4 simulations of a full-body SPECT camera. We studied how the pixel size, shape and noise could affect its energy and spatial resolution. Our results suggest that it would be possible to obtain an intrinsic spatial resolution of a few mm FWHM and an energy resolution at 140 keV close to 10%, even if using pixels more than 20 times larger than standard commercial SiPMs of 6 × 6 mm2. We have also found that if SiPMs are distributed following a honeycomb structure, the spatial resolution is significantly better than if using square pixels distributed in a square grid. Full article
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<p>Scheme showing the different LASiP configurations (1–8).</p>
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<p>A simulated FFI using LASiP 2 without noise: (<b>a</b>) raw reconstruction, (<b>b</b>) after spatial linearity correction and (<b>c</b>) after uniformity correction. The blue dashed lines show the LASiP edges.</p>
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<p>Collected charge (relative to the total number of collected photons) as a function of the number of SiPMs used to collect the charge.</p>
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<p>Energy resolution as a function of the number of LASiPs used to collect the charge (LASiP 1–4) under different SiPM noise levels. The top <span class="html-italic">x</span> axis shows the total number of SiPMs employed.</p>
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<p>Reconstructed image of a capillary source at <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mo>−</mo> <mn>12</mn> <mo>,</mo> <mn>0</mn> <mo>,</mo> <mn>10</mn> </mrow> </semantics></math> mm for LASiP 1–4 at room-SiPM noise. The blue dashed lines mark the borders of the LASiPs.</p>
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<p>Normalized projections in the <span class="html-italic">x</span> axis of the reconstructed images of <a href="#sensors-24-06052-f005" class="html-fig">Figure 5</a>. The projections were performed in a narrow region around the center of the pixel.</p>
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<p>Mean FWHM of the reconstructed capillary as a function of the normalized distance to the pixel center <math display="inline"><semantics> <mover accent="true"> <mi>d</mi> <mo stretchy="false">^</mo> </mover> </semantics></math> (<math display="inline"><semantics> <mrow> <mover accent="true"> <mi>d</mi> <mo stretchy="false">^</mo> </mover> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> describes a capillary passing by the center of a pixel, <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>d</mi> <mo stretchy="false">^</mo> </mover> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> describes a capillary at the edge of a pixel).</p>
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<p>Reconstructed image of a simile-Derenzo source (the diameter of the circles are 1, 2, 3, 4, 6 and 8 mm) at room-SiPM noise. The dashed lines mark the edges of the LASiPs.</p>
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<p>Normalized projections in the <span class="html-italic">x</span> axis of the reconstructed images of capillaries passing through the center of the LASiP 3, LASiP 5 and LASiP 8 pixels. The projections were centered at different <span class="html-italic">y</span> values: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> mm, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math> mm, (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> mm.</p>
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<p>Reconstructed images of a 1-D capillary source using LASiP 8 at room-SiPM noise. The dashed lines mark the edges of the LASiPs.</p>
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<p>Reconstructed image of the simile-Derenzo source at different noise levels using LASiP 3. The dashed lines mark the edges of the LASiPs.</p>
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14 pages, 501 KiB  
Article
Microdosimetric Simulation of Gold-Nanoparticle-Enhanced Radiotherapy
by Maxim Azarkin, Martin Kirakosyan and Vladimir Ryabov
Int. J. Mol. Sci. 2024, 25(17), 9525; https://doi.org/10.3390/ijms25179525 - 2 Sep 2024
Cited by 1 | Viewed by 897
Abstract
Conventional X-ray therapy (XRT) is commonly applied to suppress cancerous tumors; however, it often inflicts collateral damage to nearby healthy tissue. In order to provide a better conformity of the dose distribution in the irradiated tumor, proton therapy (PT) is increasingly being used [...] Read more.
Conventional X-ray therapy (XRT) is commonly applied to suppress cancerous tumors; however, it often inflicts collateral damage to nearby healthy tissue. In order to provide a better conformity of the dose distribution in the irradiated tumor, proton therapy (PT) is increasingly being used to treat solid tumors. Furthermore, radiosensitization with gold nanoparticles (GNPs) has been extensively studied to increase the therapeutic ratio. The mechanism of radiosensitization is assumed to be connected to an enhancement of the absorbed dose due to huge photoelectric cross-sections with gold. Nevertheless, numerous theoretical studies, mostly based on Monte Carlo (MC) simulations, did not provide a consistent and thorough picture of dose enhancement and, therefore, the radiosensitization effect. Radiosensitization by nanoparticles in PT is even less studied than in XRT. Therefore, we investigate the physics picture of GNP-enhanced RT using an MC simulation with Geant4 equipped with the most recent physics models, taking into account a wide range of physics processes relevant for realistic PT and XRT. Namely, we measured dose enhancement factors in the vicinity of GNP, with diameters ranging from 10 nm to 80 nm. The dose enhancement in the vicinity of GNP reaches high values for XRT, while it is very modest for PT. The macroscopic dose enhancement factors for realistic therapeutic GNP concentrations are rather low for all RT scenarios; therefore, other physico-chemical and biological mechanisms should be additionally invoked for an explanation of the radiosensitization effect observed in many experiments. Full article
(This article belongs to the Special Issue Nanoparticles in Nanobiotechnology and Nanomedicine: 2nd Edition)
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<p>Spatial energy density enhancement factor (<math display="inline"><semantics> <mrow> <mi>D</mi> <mi>E</mi> <msub> <mi>F</mi> <mi>SE</mi> </msub> </mrow> </semantics></math>) as a function of distance from the center of the gold nanoparticle (GNP) immersed in a homogeneous water system. The <math display="inline"><semantics> <mrow> <mi>D</mi> <mi>E</mi> <msub> <mi>F</mi> <mi>SE</mi> </msub> </mrow> </semantics></math>s are measured for GNPs with diameters of 10 nm (<b>a</b>), 20 nm (<b>b</b>), 40 nm (<b>c</b>), and 80 nm (<b>d</b>). The vertical red line marks the GNP surface.</p>
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<p>A schematic (not to scale) layout of the simulated system: (<b>a</b>) The macroscopic setup represents a cube consisting of human soft tissue. The dark orange layer represents a tumor, the smaller blue cubes represent the microscopic volume with a gold nanoparticle inside, either on frontal or distal parts of the tumor layer. (<b>b</b>) A close-up of a microscopic volume represents a water cube with gold nanoparticles of various (10 to 80 nm) radii inside. The thick dark red arrows denote the primary beam particles, and the thin black arrows indicate the secondary particles that can interact with the GNP as well.</p>
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<p>Relative dose versus tissue depth for different types of radiation.</p>
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<p>Energy distribution of 140 kVp (<b>a</b>), 6 MV (<b>b</b>), and photons and protons (<b>c</b>) at different depths in tissue. These distributions are normalized by the number of initial beam photons (<math display="inline"><semantics> <msubsup> <mi>N</mi> <mrow> <mi>γ</mi> </mrow> <mi>init</mi> </msubsup> </semantics></math>) and protons (<math display="inline"><semantics> <msubsup> <mi>N</mi> <mrow> <mi mathvariant="normal">p</mi> </mrow> <mi>init</mi> </msubsup> </semantics></math>).</p>
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21 pages, 1820 KiB  
Article
Enhanced Particle Classification in Water Cherenkov Detectors Using Machine Learning: Modeling and Validation with Monte Carlo Simulation Datasets
by Ticiano Jorge Torres Peralta, Maria Graciela Molina, Hernan Asorey, Ivan Sidelnik, Antonio Juan Rubio-Montero, Sergio Dasso, Rafael Mayo-Garcia, Alvaro Taboada, Luis Otiniano and for the LAGO Collaboration
Atmosphere 2024, 15(9), 1039; https://doi.org/10.3390/atmos15091039 - 28 Aug 2024
Cited by 1 | Viewed by 905
Abstract
The Latin American Giant Observatory (LAGO) is a ground-based extended cosmic rays observatory designed to study transient astrophysical events, the role of the atmosphere on the formation of secondary particles, and space-weather-related phenomena. With the use of a network of Water Cherenkov Detectors [...] Read more.
The Latin American Giant Observatory (LAGO) is a ground-based extended cosmic rays observatory designed to study transient astrophysical events, the role of the atmosphere on the formation of secondary particles, and space-weather-related phenomena. With the use of a network of Water Cherenkov Detectors (WCDs), LAGO measures the secondary particle flux, a consequence of the interaction of astroparticles impinging on the atmosphere of Earth. This flux can be grouped into three distinct basic constituents: electromagnetic, muonic, and hadronic components. When a particle enters a WCD, it generates a measurable signal characterized by unique features correlating to the particle’s type and the detector’s specific response. The resulting charge histograms from these signals provide valuable insights into the flux of primary astroparticles and their key characteristics. However, these data are insufficient to effectively distinguish between the contributions of different secondary particles. In this work, we extend our previous research by using detailed simulations of the expected atmospheric response to the primary flux and the corresponding response of our WCDs to atmospheric radiation. This dataset, which was created through the combination of the outputs of the ARTI and Meiga simulation frameworks, simulated the expected WCD signals produced by the flux of secondary particles during one day at the LAGO site in Bariloche, Argentina, situated at 865 m above sea level. This was achieved by analyzing the real-time magnetospheric and local atmospheric conditions for February and March of 2012, where the resultant atmospheric secondary-particle flux was integrated into a specific Meiga application featuring a comprehensive Geant4 model of the WCD at this LAGO location. The final output was modified for effective integration into our machine-learning pipeline. With an implementation of Ordering Points to Identify the Clustering Structure (OPTICS), a density-based clustering algorithm used to identify patterns in data collected by a single WCD, we have further refined our approach to implement a method that categorizes particle groups using advanced unsupervised machine learning techniques. This allowed for the differentiation among particle types and utilized the detector’s nuanced response to each, thus pinpointing the principal contributors within each group. Our analysis has demonstrated that applying our enhanced methodology can accurately identify the originating particles with a high degree of confidence on a single-pulse basis, highlighting its precision and reliability. These promising results suggest the feasibility of future implementations of machine-leaning-based models throughout LAGO’s distributed detection network and other astroparticle observatories for semi-automated, onboard and real-time data analysis. Full article
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<p>The diagonal shows the overall shape of the distribution of each PCA component while, the lower diagonal graphs show 2D projections between the different components obtained using PCA for the selected features in [<a href="#B18-atmosphere-15-01039" class="html-bibr">18</a>]. By visual exploration, it different ‘groups’ can be observed, aggregated within other groups of varying density, showing the complexity of the data.</p>
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<p>Construction of a reachability plot. Cluster 1 can be observed within the reachability plot as a valley (see the marked blue arrow). The algorithm can distinguish clusters with different densities of points in different hierarchies. Figure adapted from Wang et al., 2019 [<a href="#B43-atmosphere-15-01039" class="html-bibr">43</a>].</p>
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<p>General data pipeline for the ML modeling.</p>
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<p>Cross-correlation matrix of initial feature set. A darker shade of orange corresponds to a higher value for the cross-correlation between two features.</p>
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<p>Example of a reachability plot from one run of the ML pipeline. The x-axis shows every point in the dataset ordered such as from smallest to greatest reachability distance, <math display="inline"><semantics> <msub> <mi>ϵ</mi> <mi>r</mi> </msub> </semantics></math>, with respect to the point’s closest core group. The y-axis is the <math display="inline"><semantics> <msub> <mi>ϵ</mi> <mi>r</mi> </msub> </semantics></math> value. A consistent cut-off threshold of 0.08 was used in every run where each independent run produced a similar plot. Points belonging to a cluster are colored and labeled accordingly, while any point in black did not gain membership to any cluster. A total of eight clusters were found.</p>
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<p>Histogram of charge, in black, with resulting clusters labeled and colored. The Y-axis is in a logarithmic scale for clarity.</p>
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<p>Stacked bar chart showing the percentage particle compositions for each cluster. It can be seen that the algorithm is very accurate in grouping muonic contributions.</p>
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<p>Zoomed in version of the histogram of charge, in black, highlighting clusters 0, 1 and 2.</p>
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<p>Zoomed-in version of the histogram of charge, in black. Clusters have been combined into three groups: group 1 is clusters 0, 1, and 2; group 2 is clusters 3 and 4; while group 3 is clusters 5, 6, and 7.</p>
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20 pages, 5088 KiB  
Article
Skin Absorbed Dose Coefficients for Human Legs from Beta Radiation as a Function of Height
by Mohammad Yosofvand, Rabin Dhakal, Ali Nejat and Hanna Moussa
Appl. Sci. 2024, 14(16), 7363; https://doi.org/10.3390/app14167363 - 21 Aug 2024
Viewed by 617
Abstract
External exposure to skin from beta-emitter radionuclides following severe reactor accidents or nuclear testing can result in beta burning and other health complications. The skin absorbed dose coefficient (SADC) measures the energy deposition into the skin during such accidents. The U.S. Environmental Protection [...] Read more.
External exposure to skin from beta-emitter radionuclides following severe reactor accidents or nuclear testing can result in beta burning and other health complications. The skin absorbed dose coefficient (SADC) measures the energy deposition into the skin during such accidents. The U.S. Environmental Protection Agency has published several reports to measure the possible energy deposition into the skin in such accidents. However, the most recent SADC published by Federal Guidance Report (FGR) 12 was computed only at one meter above the contaminated surface. Therefore, it was necessary to develop a model to estimate the absorbed dose coefficients for skin at different heights. In this manuscript, Geant4, a Monte Carlo simulator toolkit, was used to estimate the absorbed dose coefficients from electron sources located on the soil surface with energies ranging from 0.1 to 4 MeV. The energy deposited from primary electrons, secondary electrons, and photons in a 50 µm thick layer of epidermis tissue (Basal Cells Layer) located at a depth of 50 µm from the skin surface was estimated at several discrete heights of human leg phantom. More than 40% of the total energy deposited comes from secondary electrons and photons in energy sources of 0.1 and 0.2 MeV on average, but for higher energies, this percentage is less than 1%, which indicates primary electrons are the main source of the deposited energy in the skin. Furthermore, the results showed the energy deposited into skin closer to the ground was 50–100% higher than the previously estimated doses for 1 m above the ground. The results from Geant4 showed a great correlation (R2 = 0.972) with the FGR 12 data at one meter height, and they were aligned with the published values from FGR 12, which validated the simulation results. Therefore, the calculated dose coefficients for different energy sources and different heights could be used in radiation protection measurements. Full article
(This article belongs to the Section Applied Physics General)
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<p>Simulation Universe Configuration. The blue hemisphere shows the soil and the above hemisphere is the air where the leg phantom is located.</p>
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<p>Leg phantom model.</p>
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<p>Cell layers for each truncated cone.</p>
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<p>Skin absorbed dose coefficient for 0.1 MeV source for electrons only and total deposited energy.</p>
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<p>Skin absorbed dose coefficient for 0.2 MeV source for electrons only and total deposited energy.</p>
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<p>Total skin absorbed dose coefficient for 0.4, 0.8, and 1.0 MeV.</p>
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<p>Total skin absorbed dose coefficient for 1.5, 2.0, and 2.5 MeV.</p>
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<p>Total skin absorbed dose coefficient for 3.0 and 4.0 MeV.</p>
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<p>Secondary electrons and generated photons share from the deposited energy in the phantom legs. The percentages for energy sources from 0.4 MeV to 4.0 MeV overlapping as they are close to zero.</p>
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<p>Skin absorbed dose comparison between FGR and Geant4.</p>
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13 pages, 3673 KiB  
Article
Design and Computational Validation of γ-Ray Shielding Effectiveness in Heavy Metal/Rare Earth Oxide–Natural Rubber Composites
by Yongkang Liu, Xiaopeng Li, Yilin Yin, Zhen Li, Huisheng Yao, Zenghe Li and Heguo Li
Polymers 2024, 16(15), 2130; https://doi.org/10.3390/polym16152130 - 26 Jul 2024
Viewed by 975
Abstract
This study involved the preparation of natural rubber-based composites incorporating varying proportions of heavy metals and rare earth oxides (Sm2O3, Ta2O5, and Bi2O3). The investigation analyzed several parameters of the samples, [...] Read more.
This study involved the preparation of natural rubber-based composites incorporating varying proportions of heavy metals and rare earth oxides (Sm2O3, Ta2O5, and Bi2O3). The investigation analyzed several parameters of the samples, including mass attenuation coefficients (general, photoelectric absorption, and scattering), linear attenuation coefficients (μ), half-value layers (HVLs), tenth-value layers (TVLs), mean free paths (MFPs), and radiation protection efficiencies (RPEs), utilizing the Monte Carlo simulation software Geant4 and the WinXCom database across a gamma-ray energy spectrum of 40–150 keV. The study also compared the computational discrepancies among these measurements. Compared to rubber composites doped with single-component fillers, multi-component mixed shielding materials significantly mitigate the shielding deficiencies observed with single-component materials, thereby broadening the γ-ray energy spectrum for which the composites provide effective shielding. Subsequently, the simulation outcomes were juxtaposed with experimental data derived from a 133Ba (80 keV) γ-source. The findings reveal that the simulated results align closely with the experimental observations. When compared to the WinXCom database, the Geant4 software demonstrates superior accuracy in deriving radiation shielding parameters and notably enhances experimental efficiency. Full article
(This article belongs to the Section Polymer Physics and Theory)
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<p>The mechanism of interaction between γ-photons and nuclei. (<b>a</b>) Photoelectric effect. (<b>b</b>) Compton scattering. (<b>c</b>) Electron-pair production.</p>
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<p>Variation of three types of interactions with γ-photon energy and atomic number.</p>
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<p>Schematic diagram of γ-ray shielding performance testing device.</p>
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<p>Generated through Geant4 simulations, these results delineate the radiation shielding efficiencies of composite materials composed of varying formulations at energies of (<b>a</b>) 40 keV, (<b>b</b>) 60 keV, (<b>c</b>) 80 keV, (<b>d</b>) 100 keV, and (<b>e</b>) 150 keV.</p>
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<p>Mass attenuation coefficients due to photoelectric absorption (<b>a</b>–<b>c</b>), mass attenuation coefficients from scattering (<b>d</b>–<b>f</b>), and scattering efficiencies (<b>g</b>–<b>i</b>) of composites doped with samarium oxide, tantalum pentoxide, and bismuth oxide, as computed using Geant4 and WinXCom.</p>
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<p>Mass attenuation coefficients due to photoelectric absorption (<b>a</b>–<b>c</b>), mass attenuation coefficients from scattering (<b>d</b>–<b>f</b>), and scattering efficiencies (<b>g</b>–<b>i</b>) of composites with mixed components in ratios of 3/5/2, 5/3/2, and 7/2/1, as computed using Geant4 and WinXCom.</p>
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<p>The shielding performance of materials with different compositions as determined through Geant4 simulations and experimental testing. (<b>a</b>) Radiation protection efficiencies. (<b>b</b>) Linear attenuation coefficients. (<b>c</b>) Mean free path values. (<b>d</b>) Half-value layers. (<b>e</b>) Tenth-value layers.</p>
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15 pages, 4711 KiB  
Article
Monte Carlo-Based Nanoscale Dosimetry Holds Promise for Radiopharmaceutical Therapy Involving Auger Electron Emitters
by Ohyun Kwon, Sabrina L. V. Hoffman, Paul A. Ellison and Bryan P. Bednarz
Cancers 2024, 16(13), 2349; https://doi.org/10.3390/cancers16132349 - 26 Jun 2024
Viewed by 1427
Abstract
Radiopharmaceutical therapy (RPT) is evolving as a promising strategy for treating cancer. As interest grows in short-range particles, like Auger electrons, understanding the dose–response relationship at the deoxyribonucleic acid (DNA) level has become essential. In this study, we used the Geant4-DNA toolkit to [...] Read more.
Radiopharmaceutical therapy (RPT) is evolving as a promising strategy for treating cancer. As interest grows in short-range particles, like Auger electrons, understanding the dose–response relationship at the deoxyribonucleic acid (DNA) level has become essential. In this study, we used the Geant4-DNA toolkit to evaluate DNA damage caused by the Auger-electron-emitting isotope I-125. We compared the energy deposition and single strand break (SSB) yield at each base pair location in a short B-form DNA (B-DNA) geometry with existing simulation and experimental data, considering both physical direct and chemical indirect hits. Additionally, we evaluated dosimetric differences between our high-resolution B-DNA target and a previously published simple B-DNA geometry. Overall, our benchmarking results for SSB yield from I-125 decay exhibited good agreement with both simulation and experimental data. Using this simulation, we then evaluated the SSB and double strand break (DSB) yields caused by a theranostic Br-77-labeled poly ADP ribose polymerase (PARP) inhibitor radiopharmaceutical. The results indicated a predominant contribution of chemical indirect hits over physical direct hits in generating SSB and DSB. This study lays the foundation for future investigations into the nano-dosimetric properties of RPT. Full article
(This article belongs to the Section Cancer Drug Development)
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<p>DNA target models used in this work. The left panel (<b>a</b>) depicts a high-resolution representation of an 82-nucleotide segment of the B-DNA double helix (white: hydrogen, red: oxygen, blue: nitrogen), and (<b>b</b>) shows the B-DNA with a bounding box, benchmarking Pomplun et al. [<a href="#B7-cancers-16-02349" class="html-bibr">7</a>]. The right panel (<b>c</b>) features simplified semi-cylindrical geometries representing a double-strand (one green, one blue) B-DNA backbone structure, benchmarking Thompson et al. [<a href="#B9-cancers-16-02349" class="html-bibr">9</a>].</p>
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<p>Geant4-DNA physics and chemistry stage visualization. The physics stage (<b>left</b> panel) has shown emitted particle tracks from the I-125 radioisotope for 1 decay, and the chemistry stage (<b>right</b> panel) has shown generated radical diffusion tracks by Brownian motion.</p>
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<p>Schematic representation of the computation process for different DSB scenarios resulting from diverse radiation-induced damage types.</p>
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<p>PARP1-bound SSB DNA with Br-77-labeled PARP inhibitor rucaparib (pink molecule) (<b>a</b>), PARP1 F1F2 domains in complex with SSB DNA geometries in PyMOL (<b>b</b>) and Geant4-DNA (<b>c</b>), and the DNA portion alone in Geant4-DNA (red line: electron tracks) (<b>d</b>).</p>
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<p>The mean energy deposition per decay from the I-125 radionuclide at the 0 position was evaluated for base positions (<b>a</b>) and sugar–phosphate positions (<b>b</b>) on the labeled strand. This assessment considered physical direct hits and utilized Geant4-DNA Physics options 2, 4, and 6 on (top), (middle), and (bottom), respectively.</p>
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<p>The mean energy deposition per decay from the I-125 radionuclide was evaluated for base positions (<b>a</b>) and sugar–phosphate positions (<b>b</b>). This assessment utilized Geant4-DNA Physics option 4 alone, considering (top) physical indirect hits on the labeled strand, (middle) physical direct hits on the non-labeled strand, and (bottom) physical indirect hits on the non-labeled strand.</p>
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<p>The probabilities of SSB occurring per decay at sugar–phosphate regions on the radionuclide source labeled strand, with (top) illustrating the results of SSB induced by physical direct energy deposition from our study with other references, (middle) depicting the results of SSB induced by a combination of physical direct energy deposition and chemical indirect reactions from our study with other references, and (bottom) demonstrating a comparison between the high-resolution and simplified B-DNA structure from our study against the corresponding benchmarking experimental data by Kandaiya et al. [<a href="#B8-cancers-16-02349" class="html-bibr">8</a>].</p>
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11 pages, 5012 KiB  
Article
Research on a Neutron Detector with a Boron-Lined Multilayer Converter
by Chao Deng, Qin Hu, Pengcheng Li, Qibiao Wang, Bo Xie, Jianbo Yang and Xianguo Tuo
Appl. Sci. 2024, 14(10), 4269; https://doi.org/10.3390/app14104269 - 17 May 2024
Viewed by 1232
Abstract
3He is a splendid neutron detection material due to its high neutron reaction cross section, gaseous state, and nonelectronegative and nonpoisonous nature. With the worldwide problem of the “3He supply crisis” arising, boron-lined gaseous neutron detectors are being widely used [...] Read more.
3He is a splendid neutron detection material due to its high neutron reaction cross section, gaseous state, and nonelectronegative and nonpoisonous nature. With the worldwide problem of the “3He supply crisis” arising, boron-lined gaseous neutron detectors are being widely used in neutron detection to replace 3He neutron detectors. In this work, to reduce the scattering neutron background coming from the substrate of a boron-lined neutron detector in the application of neutron scattering, a new design of the boron-lined gaseous neutron detector composed of a boron-lined multichip converter and a multiwire proportional chamber was proposed. The electron drift efficiency matrix simulated by Garfield++ (Version 2023.4) and the values and positions of electron energy deposition simulated by Geant4 were obtained. The α, 7Li, and total charged particle energy deposition spectra were acquired via coupling calculations of the electron drift efficiency matrix and the values and positions of electron energy deposition, and the width of the slit was selected as 3 mm. The boron-lined multilayer converter neutron detector (BMCND) was tested using a 241Am–239Pu mixture α source, and the total count rate of α charged particles was measured as 599.5 s−1, which is 89% of the theoretical α particle emission rate of 672.9 s−1. The drift voltage experiments showed that 1200 V is enough to acquire a relatively ideal count, and a 2500 V drift voltage was confirmed, considering the higher count and instrument safety. We also performed the neutron detection experiments using a photo-neutron source, and a characteristic spectrum shape of “two stairs” was measured. When borated polyethylene was used to shield the BMCND, the detected total count decreased while keeping the characteristic spectrum shape, demonstrating that the BMCND was equipped with the ability to detect neurons, indicating that BMCNDs have the potential to be an outstanding 3He alternative neutron detector. Full article
(This article belongs to the Section Applied Physics General)
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<p>Physical structure of the BMCND.</p>
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<p>Neutron detection schematic diagram of the BMCND.</p>
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<p>Schematic diagram of electrons’ initial positions.</p>
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<p>EDEM with a 3 mm slit.</p>
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<p>Charged particle EDS of different slits.</p>
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<p>EDS of different particles.</p>
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<p>BMCND packaging process.</p>
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<p>Schematic diagram of α radioactive source experiment.</p>
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<p>Layout of α radioactive source experiment.</p>
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<p>Results of α radioactive source experiment.</p>
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<p>Results of different drift voltage experiments.</p>
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<p>Layout of photo-neutron source experiment.</p>
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<p>Results of photo-neutron source experiments.</p>
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13 pages, 4647 KiB  
Article
New Estimates of Nitrogen Fixation on Early Earth
by Madeline Christensen, Danica Adams, Michael L. Wong, Patrick Dunn and Yuk L. Yung
Life 2024, 14(5), 601; https://doi.org/10.3390/life14050601 - 8 May 2024
Cited by 1 | Viewed by 1365
Abstract
Fixed nitrogen species generated by the early Earth’s atmosphere are thought to be critical to the emergence of life and the sustenance of early metabolisms. A previous study estimated nitrogen fixation in the Hadean Earth’s N2/CO2-dominated atmosphere; however, that [...] Read more.
Fixed nitrogen species generated by the early Earth’s atmosphere are thought to be critical to the emergence of life and the sustenance of early metabolisms. A previous study estimated nitrogen fixation in the Hadean Earth’s N2/CO2-dominated atmosphere; however, that previous study only considered a limited chemical network that produces NOx species (i.e., no HCN formation) via the thermochemical dissociation of N2 and CO2 in lightning flashes, followed by photochemistry. Here, we present an updated model of nitrogen fixation on Hadean Earth. We use the Chemical Equilibrium with Applications (CEA) thermochemical model to estimate lightning-induced NO and HCN formation and an updated version of KINETICS, the 1-D Caltech/JPL photochemical model, to assess the photochemical production of fixed nitrogen species that rain out into the Earth’s early ocean. Our updated photochemical model contains hydrocarbon and nitrile chemistry, and we use a Geant4 simulation platform to consider nitrogen fixation stimulated by solar energetic particle deposition throughout the atmosphere. We study the impact of a novel reaction pathway for generating HCN via HCN2, inspired by the experimental results which suggest that reactions with CH radicals (from CH4 photolysis) may facilitate the incorporation of N into the molecular structure of aerosols. When the HCN2 reactions are added, we find that the HCN rainout rate rises by a factor of five in our 1-bar case and is about the same in our 2- and 12-bar cases. Finally, we estimate the equilibrium concentration of fixed nitrogen species under a kinetic steady state in the Hadean ocean, considering loss by hydrothermal vent circulation, photoreduction, and hydrolysis. These results inform our understanding of environments that may have been relevant to the formation of life on Earth, as well as processes that could lead to the emergence of life elsewhere in the universe. Full article
(This article belongs to the Special Issue Feature Papers in Origins of Life)
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Figure 1

Figure 1
<p>Diagram of the nitrogen oxide reaction pathways. As the pressure and heat of lightning strike these chemicals, it forces them to react and form nitrogen oxides which then react with other species to form nitroxyl (HNO), nitrous acid (HNO<sub>2</sub>), or nitric acid (HNO<sub>3</sub>), which then rain out into the atmosphere.</p>
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<p>Diagram displaying the hydrogen cyanide pathways. Diatomic nitrogen is broken down into atomic nitrogen, which then reacts with CH<sub>3</sub> to form methylene amidogen (H<sub>2</sub>CN), which then breaks down via reactions with primarily hydrogen into hydrogen cyanide, which then rains out into the ocean. The left side of the diagram shows the alternate pathway via HCN<sub>2</sub> added in this study. Some hydrogen photolyzes to CN, which then turns back into hydrogen cyanide through reactions with methane.</p>
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<p>Graph of the CEA results, showing NO and HCN flux over different concentrations of atmospheric composition. Solid, dashed, and dotted lines refer to our 12-bar, 2-bar, and 1-bar cases, respectively. On the left side, H<sub>2</sub> is the only chemical that increases in concentration, as CH<sub>4</sub> stays constant at 0.1%, and the same is true for CH<sub>4</sub> in the middle. On the right, H<sub>2</sub> and CH<sub>4</sub> increase together. There is an inverse relationship present between NO and HCN, even though NO is almost six orders of magnitude more prevalent than HCN.</p>
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<p>The boundary conditions of KINETICS based on altitude (km) of our 1-bar atmosphere: pressure (mbar); temperature (K); <span class="html-italic">K</span><sub>zz</sub> or eddy diffusion (cm<sup>2</sup> s<sup>−1</sup>); and the species concentrations for N<sub>2</sub>, CO<sub>2</sub>, and H<sub>2</sub>O (cm<sup>−3</sup>).</p>
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<p>Mixing ratios of NO<span class="html-italic"><sub>x</sub></span>, HCN, HNO, HNO<sub>2</sub>, HNO<sub>3</sub>, N, and N(<sup>2</sup>D) vs. altitude for the (<b>left</b>) 1-bar atmosphere, (<b>middle</b>) 2-bar atmosphere, and (<b>right</b>) 12-bar atmosphere. A different line style presented for each assumed atmospheric concentration. HNO, HNO<sub>2</sub>, and HNO<sub>3</sub> all tend to mirror each other, and both HCN and NO<span class="html-italic"><sub>x</sub></span> are in relation with their original CEA values.</p>
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<p>Rainout rates of NO<span class="html-italic"><sub>x</sub></span> (magenta) and HCN (green) for the various atmospheric compositions tested in this study, both with (bold) and without (dashed) the HCN<sub>2</sub> pathway. The HCN rainout rate rose by a factor of five in the 1-bar case and is about the same in the 2- and 12-bar cases when the HCN<sub>2</sub> reactions were added. These results present more accurate estimates of how much fixed nitrogen may have been available to early life.</p>
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<p>The oceanic concentrations of both NO<sub>3</sub><sup>−</sup>, in purple, and HCN, in green, in relation to the original atmospheric concentrations of H<sub>2</sub> and CH<sub>4</sub>. Similar to the original CEA graph (<a href="#life-14-00601-f003" class="html-fig">Figure 3</a>), NO<sub>3</sub><sup>−</sup> and HCN are in an inverse relationship. Note that the magnitude at which these chemicals are present is completely different, as NO<sub>3</sub><sup>−</sup> is &lt;10<sup>−8</sup> M, whereas HCN is &gt;10<sup>−5</sup> M.</p>
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<p>The mixing ratio of carbon monoxide vs. altitude for a 1 bar atmospheric case, where H<sub>2</sub> and CH<sub>4</sub> content match. CO is a photochemical product, and the total density is computed from the ideal gas law. While physically a mixing ratio cannot exceed 1, numerically this results in our code due to CO buildup. Since CO<sub>2</sub> is fixed, whenever CO<sub>2</sub> is lost to CO<sub>2</sub> + <span class="html-italic">hv</span> → CO + O (and this CO<sub>2</sub> is not replenished via CO + HO<span class="html-italic"><sub>x</sub></span>), an infinite source of CO<sub>2</sub> replenishes the profile (numerically). This photolysis therefore occurs over time and CO builds until eventually CO exceeds the total density predicted by the ideal gas law. Again, while we predict the CO buildup is real, its mixing ratio exceeding unity is completely a numerical effect due to fixing CO<sub>2</sub>.</p>
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21 pages, 1285 KiB  
Article
Ionization Detail Parameters for DNA Damage Evaluation in Charged Particle Radiotherapy: Simulation Study Based on Cell Survival Database
by Monika Mietelska, Marcin Pietrzak, Aleksandr Bancer, Antoni Ruciński, Zygmunt Szefliński and Beata Brzozowska
Int. J. Mol. Sci. 2024, 25(10), 5094; https://doi.org/10.3390/ijms25105094 - 7 May 2024
Viewed by 1128
Abstract
Details of excitation and ionization acts hide a description of the biological effects of charged particle traversal through living tissue. Nanodosimetry enables the introduction of novel quantities that characterize and quantify the particle track structure while also serving as a foundation for assessing [...] Read more.
Details of excitation and ionization acts hide a description of the biological effects of charged particle traversal through living tissue. Nanodosimetry enables the introduction of novel quantities that characterize and quantify the particle track structure while also serving as a foundation for assessing biological effects based on this quantification. This presents an opportunity to enhance the planning of charged particle radiotherapy by taking into account the ionization detail. This work uses Monte Carlo simulations with Geant4-DNA code for a wide variety of charged particles and their radiation qualities to analyze the distribution of ionization cluster sizes within nanometer-scale volumes, similar to DNA diameter. By correlating these results with biological parameters extracted from the PIDE database for the V79 cell line, a novel parameter R2 based on ionization details is proposed for the evaluation of radiation quality in terms of biological consequences, i.e., radiobiological cross section for inactivation. By incorporating the probability p of sub-lethal damage caused by a single ionization, we address limitations associated with the usually proposed nanodosimetric parameter Fk for characterizing the biological effects of radiation. We show that the new parameter R2 correlates well with radiobiological data and can be used to predict biological outcomes. Full article
(This article belongs to the Section Molecular Biology)
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Figure 1
<p>Ratio <math display="inline"><semantics> <msub> <mi>R</mi> <mn>2</mn> </msub> </semantics></math> to mean cluster size <math display="inline"><semantics> <msub> <mi>M</mi> <mn>1</mn> </msub> </semantics></math> as a function of linear energy transfer (<b>A</b>) and mean cluster size (<b>B</b>). <math display="inline"><semantics> <msub> <mi>M</mi> <mn>1</mn> </msub> </semantics></math> values were simulated for a 2.3 × 3.4 nm<sup>2</sup> target size. Data points are indicated by shades of gray and shapes representing ion type. “Hydrogen” includes protons and deuterons, while “Helium” includes <math display="inline"><semantics> <mrow> <mmultiscripts> <mi>He</mi> <none/> <mrow> <mn>2</mn> <mo>+</mo> </mrow> <mprescripts/> <none/> <mn>3</mn> </mmultiscripts> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mmultiscripts> <mi>He</mi> <none/> <mrow> <mn>2</mn> <mo>+</mo> </mrow> <mprescripts/> <none/> <mn>4</mn> </mmultiscripts> </mrow> </semantics></math> ions, along with alpha particles. Other used ions (<sup>7</sup>Li, <sup>11</sup>B, <sup>12</sup>C, <sup>14</sup>N, <sup>16</sup>O, <sup>20</sup>Ne) are classified as heavy ions.</p>
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<p>The RBE for a survival level of 5%, calculated as the ratio of dose from reference photon radiation to dose from ions, as a function of LET (<b>A</b>) and mean cluster size <math display="inline"><semantics> <msub> <mi>M</mi> <mn>1</mn> </msub> </semantics></math> (<b>B</b>). The reference dose is computed using fitted survival parameters from the PIDE database for reference radiation, while the ion dose is determined using corrected survival parameters from PIDE for ion radiation. The simulation are performed for a 2.3 × 3.4 nm<sup>2</sup> target size. Data points are differentiated by shades of gray and shapes representing ion type. The category “Hydrogen” includes protons and deuterons, while “Helium” includes <math display="inline"><semantics> <mrow> <mmultiscripts> <mi>He</mi> <none/> <mrow> <mn>2</mn> <mo>+</mo> </mrow> <mprescripts/> <none/> <mn>3</mn> </mmultiscripts> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mmultiscripts> <mi>He</mi> <none/> <mrow> <mn>2</mn> <mo>+</mo> </mrow> <mprescripts/> <none/> <mn>4</mn> </mmultiscripts> </mrow> </semantics></math> ions, along with alpha particles. Results for 8 experiments were not included due to insufficient information regarding reference radiation.</p>
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<p>Inactivation cross section of the V79 cell line as a function of linear energy transfer (<b>A</b>) and simulated mean cluster size <math display="inline"><semantics> <msub> <mi>M</mi> <mn>1</mn> </msub> </semantics></math> (<b>B</b>). LET was calculated using SRIM-2013 software based on energy from PIDE. Simulations are performed for a cylindrical target size of 2.3 × 3.4 nm<sup>2</sup>. The shape of each data point indicates the ion used for irradiating the cells. “Hydrogen” includes protons and deuterons, while “Helium” includes <math display="inline"><semantics> <mrow> <mmultiscripts> <mi>He</mi> <none/> <mrow> <mn>2</mn> <mo>+</mo> </mrow> <mprescripts/> <none/> <mn>3</mn> </mmultiscripts> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mmultiscripts> <mi>He</mi> <none/> <mrow> <mn>2</mn> <mo>+</mo> </mrow> <mprescripts/> <none/> <mn>4</mn> </mmultiscripts> </mrow> </semantics></math> ions, along with alpha particles. All other particles are considered heavy ions.</p>
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<p>The heatmap, which displays the relationship between probability <span class="html-italic">p</span> and target size <span class="html-italic">d</span>, highlights the best fit between <math display="inline"><semantics> <msub> <mi>σ</mi> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>R</mi> <mn>2</mn> </msub> </semantics></math> with <span class="html-italic">K</span> set to 57 µm<sup>2</sup>. Yellow areas indicate the region of optimal fit <span class="html-italic">d</span> and <span class="html-italic">p</span> parameters. <math display="inline"><semantics> <msup> <mi>χ</mi> <mn>2</mn> </msup> </semantics></math> is normalized to the highest <math display="inline"><semantics> <msup> <mi>χ</mi> <mn>2</mn> </msup> </semantics></math> value obtained within the specified range of <span class="html-italic">p</span> and <span class="html-italic">d</span>.</p>
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<p>Probabilities <math display="inline"><semantics> <msub> <mi>R</mi> <mn>2</mn> </msub> </semantics></math> and chosen <math display="inline"><semantics> <msub> <mi>F</mi> <mi>k</mi> </msub> </semantics></math> as a function of <math display="inline"><semantics> <msub> <mi>M</mi> <mn>1</mn> </msub> </semantics></math> for a target size of 1 × 1 nm<sup>2</sup> (<b>A</b>) and 2.3 × 3.4 nm<sup>2</sup> (<b>B</b>). Black stars represent the <math display="inline"><semantics> <msub> <mi>R</mi> <mn>2</mn> </msub> </semantics></math> for <span class="html-italic">p</span> value corresponding to the best agreement with <math display="inline"><semantics> <msub> <mi>σ</mi> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </msub> </semantics></math>.</p>
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<p>Inactivation cross section as a function of mean cluster size <math display="inline"><semantics> <msub> <mi>M</mi> <mn>1</mn> </msub> </semantics></math>, representing considered biological data, alongside the <math display="inline"><semantics> <msub> <mi>R</mi> <mn>2</mn> </msub> </semantics></math> based model results represented by <math display="inline"><semantics> <msub> <mi>R</mi> <mn>2</mn> </msub> </semantics></math> obtained for <span class="html-italic">p</span> equal to 0.35 and multiplied by the optimal <span class="html-italic">K</span> factor for a target size of 2.3 × 3.4 nm<sup>2</sup>.</p>
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<p>Comparison of weighting functions of <math display="inline"><semantics> <msub> <mi>F</mi> <mn>5</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>R</mi> <mn>2</mn> </msub> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.35</mn> </mrow> </semantics></math>, along with two examples of ICSD (<math display="inline"><semantics> <msub> <mi>P</mi> <mi>ν</mi> </msub> </semantics></math>).</p>
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<p>Linear fit between <math display="inline"><semantics> <msub> <mi>σ</mi> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>R</mi> <mn>2</mn> </msub> </semantics></math> (<b>A</b>) and regular residuals of <math display="inline"><semantics> <msub> <mi>σ</mi> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </msub> </semantics></math> in a function of an independent variable <math display="inline"><semantics> <msub> <mi>R</mi> <mn>2</mn> </msub> </semantics></math> (<b>B</b>). Coefficient of determination equal to 0.971.</p>
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<p>Comparison of <math display="inline"><semantics> <msub> <mi>R</mi> <mn>2</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>F</mi> <mi>k</mi> </msub> </semantics></math> curves yielding the best fit with radiobiological data for target sizes of 1 × 1 nm<sup>2</sup> (<b>A</b>) and 2.3 × 3.4 nm<sup>2</sup> (<b>B</b>). In (<b>A</b>), the optimal fit is achieved with <math display="inline"><semantics> <msub> <mi>F</mi> <mn>2</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>R</mi> <mn>2</mn> </msub> </semantics></math> for <span class="html-italic">p</span> equal to 0.8, while in (<b>B</b>), the best fit is observed for <math display="inline"><semantics> <msub> <mi>F</mi> <mn>5</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>R</mi> <mn>2</mn> </msub> </semantics></math> for <span class="html-italic">p</span> equal to 0.35. In both cases, <math display="inline"><semantics> <msub> <mi>R</mi> <mn>2</mn> </msub> </semantics></math> provides a better fit than <math display="inline"><semantics> <msub> <mi>F</mi> <mi>k</mi> </msub> </semantics></math>.</p>
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<p>Linear fit between <math display="inline"><semantics> <msub> <mi>σ</mi> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>F</mi> <mn>5</mn> </msub> </semantics></math> (<b>A</b>) and regular residuals of <math display="inline"><semantics> <msub> <mi>σ</mi> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </msub> </semantics></math> in a function of an independent variable <math display="inline"><semantics> <msub> <mi>F</mi> <mn>5</mn> </msub> </semantics></math> (<b>B</b>). Coefficient of determination equal to 0.955.</p>
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<p>Linear fit between <math display="inline"><semantics> <msub> <mi>σ</mi> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>F</mi> <mn>2</mn> </msub> </semantics></math> (<b>A</b>) and regular residuals of <math display="inline"><semantics> <msub> <mi>σ</mi> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </msub> </semantics></math> in a function of an independent variable <math display="inline"><semantics> <msub> <mi>F</mi> <mn>2</mn> </msub> </semantics></math> (<b>B</b>). Coefficient of determination equal to 0.839.</p>
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<p>Comparison of ionization cluster size distributions obtained in experiments (Exp) with the Jet Counter device (empty shapes) and in Monte Carlo (MC) simulations (filled shapes) for hydrogen, helium, and carbon ions with different energies. All data are for a 2.3 × 3.4 nm<sup>2</sup> target size. Uncertainties are due to statistical fluctuations.</p>
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12 pages, 885 KiB  
Article
A Multiple Scattering-Based Technique for Isotopic Identification in Cosmic Rays
by Francesco Dimiccoli and Francesco Maria Follega
Particles 2024, 7(2), 477-488; https://doi.org/10.3390/particles7020027 - 2 May 2024
Viewed by 1528
Abstract
Analyzing the isotopic composition of cosmic rays (CRs) provides valuable insights into the galactic environment and helps refine existing propagation models. A particular interest is devoted to secondary-to-primary ratios of light isotopic components of CRs, the measurement of which can provide complementary information [...] Read more.
Analyzing the isotopic composition of cosmic rays (CRs) provides valuable insights into the galactic environment and helps refine existing propagation models. A particular interest is devoted to secondary-to-primary ratios of light isotopic components of CRs, the measurement of which can provide complementary information with respect to secondary-to-primary ratios like B/C. Given the complexity of the concurrent measurement of velocity and momentum required to differentiate isotopes of the same Z, a task typically accomplished using magnetic spectrometers, existing measurements of these ratios only effectively characterize the low-energy region (below 1 GeV/nucl). This study introduces a novel technique for isotopic distinction in CRs at high energies up to 100 GeV/nucl based on multiple scattering, which, combined with the proposed measurement of velocity, represent an interesting alternative to magnetic spectrometers. The performance of this technique was assessed through a dedicated simulation using the GEANT4 package, with specific emphasis on Z = 1 isotopes. Full article
(This article belongs to the Special Issue Innovative Techniques for Particle Physics in Space)
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Figure 1

Figure 1
<p>On the left, a graphical scheme of the RICH design devised for this work is shown. The dimensions of the components are not to scale for display purposes. On the right, visualization of the RICH geometry implemented in the GEANT4 simulation, with a simulated D event of generated energy of 50 GeV/nucleon. In the visualization, the hits from Cherenkov photons produced in the aerogel radiator (yellow circle) by the incoming particles are visible in red on the SiPM plane (yellow semitransparent square). The three colored arrows indicate the reference frame used for the visualization, with the labels representing the dimension scale.</p>
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<p>Performance of the proposed RICH design: reconstructed <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>k</mi> </msub> <mo>/</mo> <mi>n</mi> </mrow> </semantics></math> for four monochromatic D beams of 16, 32, 52, and 80 GeV/nucleon. Measured resolution <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <msub> <mi>E</mi> <mi>k</mi> </msub> </msub> <mo>/</mo> <msub> <mi>E</mi> <mi>k</mi> </msub> </mrow> </semantics></math> is also quoted close to each distribution.</p>
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<p>In this figure, a schematic of the working principle of the MSIS is shown. Three subsequent PPT modules are depicted. In red, the scattered track is shown with the hits on each silicon module (gray). The lead layers are drawn in orange. The three depicted modules allow for two independent displacement measurements. In a realistic detector, more modules would be present. To exploit the scattering induced by the last lead target, an extra silicon layer is needed.</p>
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<p>Graphical view of the GEANT4 simulation of the MSIS, with a simulated D event of generated energy 50 GeV/nucleon. Yellow volumes represent the tracking planes, while red volumes represent Pb targets. The blue track represents the trajectory of the particle and the red circles are hits on the detector materials. The three colored arrows indicate the reference frame used for the visualization, with the labels representing the dimension scale.</p>
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<p>The blue line shows the distribution of calculated displacement between the interaction point of the primary particle on a given tracking layer and the extrapolated position of the primary trajectory, reconstructed with the previous PPT module. The black line shows the distribution of calculated displacement between the interaction points of the secondary electrons on the same tracking layer and the extrapolation of the primary trajectory measured in the previous PPT module.</p>
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<p>Distributions of average displacements measured in four simulated beams of D (blue) and p (red) in different <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>k</mi> </msub> <mo>/</mo> <mi>A</mi> </mrow> </semantics></math> ranges by the MSIS. (<b>Top left</b>): 16–17 GeV/nucleon, (<b>top right</b>): 30–34 GeV/nucleon, (<b>bottom left</b>): 49–53 GeV/nucleon, (<b>bottom right</b>): 74–84 GeV/nucleon.</p>
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<p>(<b>Top left</b>): performance of the MS displacement discrimination in terms of background rejection (1 − <math display="inline"><semantics> <msub> <mi>ϵ</mi> <mi>B</mi> </msub> </semantics></math>) of p as a function of efficiency on D signal. (<b>Top right</b>): performance in terms of rejection power of p background (<math display="inline"><semantics> <msub> <mi>ϵ</mi> <mi>S</mi> </msub> </semantics></math>/<math display="inline"><semantics> <msub> <mi>ϵ</mi> <mi>B</mi> </msub> </semantics></math>) as a function of efficiency on D signal. (<b>Bottom</b>): expected signal/noise ratio (<math display="inline"><semantics> <mrow> <mi>D</mi> <mo>/</mo> <msqrt> <mi>p</mi> </msqrt> </mrow> </semantics></math>) for an injected D/p ratio of 0.025 as a function of efficiency on D, assuming to have statistical error on D <math display="inline"><semantics> <mrow> <mo>≤</mo> <mn>1</mn> </mrow> </semantics></math>% (D counts ≥ 10,000).</p>
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<p>A GEANT4 simulation for a detector prototype. A deuterium nucleus is shot and traverses the entire the detector apparatus. In the figure, the blue line represents the trajectory of the primary particle, and the red dots are the energy deposits in the sensitive detectors. The three colored arrows indicate the reference frame used for the visualization, with the labels representing the dimension scale.</p>
Full article ">
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