Measurement of ionization quenching in plastic scintillators

Pöschl, Thomas; Greenwald, D.; Losekamm, Martin J.; Paul, S.

doi:10.1016/j.nima.2020.164865

Cited by 17 publications

(8 citation statements)

References 17 publications

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“…20 Using a proton beam with energies of 30 to 100 MeV, Po ¨schl et al recently measured ionization quenching in two different plastic scintillating fibers, SCSF-78 and BC-408. 21 Several ionization quenching models were tested, including those of Birks, Chou, and Voltz et al examined in this work. The authors concluded that none of the models provided an accurate description of the data for both scintillating materials over the full energy range.…”

Section: Introductionmentioning

confidence: 99%

Modeling ionization quenching in organic scintillators

et al. 2022

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Section: Introductionmentioning

confidence: 99%

Modeling ionization quenching in organic scintillators

et al. 2022

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“…The nonlinear response of a scintillator, which results in reduced brightness under pulsed and high dose-rate radiation environments, − is crucial for accurately measuring radiation source intensity. Numerous studies have investigated the factors influencing the nonlinear response of 2D perovskite single crystals, such as biexciton formation, , triexciton effects, , and delayed PL emission. − However, most of these studies have focused on laser excitation, which primarily affects the crystal surface and may not provide sufficient guidance for understanding the nonlinear response under radiation excitation.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Response and Origin of Slow Component of Two-Dimensional Perovskite Scintillator

Yan,

Duan,

Song

et al. 2024

J. Phys. Chem. C

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Two-dimensional organic−inorganic perovskites have displayed significant potential as scintillators in radiation detection, owing to their outstanding scintillation properties, including fast decay time and high light yield. Understanding the nonlinear response and the origin of the slow component is crucial for their application in pulsed radiation environments, necessitating an in-depth investigation. In this study, we successfully synthesized lithium-doped phenylethylammonium lead bromide (LPEA) perovskite single crystals and conducted a comprehensive analysis of their scintillation performance. LPEA exhibited intense blue emission peaks at 411 and 434 nm when excited by a 320 nm laser and X-rays, respectively, with a relatively narrow emission spectrum (full width at half-maximum of 11 and 27 nm). Under 137 Cs excitation, LPEA displayed two decay components: an 8 ns component accounting for 90% of the decay and a 22 ns component accounting for the remaining 10%. We investigated the nonlinear response under pulsed radiation excitation with varying energies and dose rates. Our results demonstrated that LPEA exhibited a nonlinear response under high dose-rate excitation, with the nonlinearity becoming more pronounced as the energy and dose rate of the radiation source increased. To provide a detailed explanation of the nonlinear response, we developed a novel photophysical model consisting of bright and dark states. According to our findings, the increased contribution of the dark state accounted for the nonlinear response of LPEA. Additionally, we confirmed that the dark state was responsible for the slow component observed in the decay behavior.

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“…Scintillator detector systems must account for scaling of water-equivalent depths, quenching effect, and optical phenomena. 4,6,7 However, it is challenging to obtain fully corrected dose maps for optical effects in the detecting environment and beam conditions. To improve previous analytic methods, the deep learning approach without additional corrections has been employed using datasets prepared by MC simulations including optical photon simulations to predict the luminescence in water for particle ion beams.…”

Section: Introductionmentioning

confidence: 99%

“…Scintillator detector systems must account for scaling of water‐equivalent depths, quenching effect, and optical phenomena 4,6,7 . However, it is challenging to obtain fully corrected dose maps for optical effects in the detecting environment and beam conditions.…”

Section: Introductionmentioning

confidence: 99%

Deep learning proton beam range estimation model for quality assurance based on two‐dimensional scintillated light distributions in simulations

et al. 2023

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BackgroundMany studies have utilized optical camera systems with volumetric scintillators for quality assurances (QA) to estimate the proton beam range. However, previous analytically driven range estimation methods have the difficulty to derive the dose distributions from the scintillation images with quenching and optical effects.PurposeIn this study, a deep learning method utilized to QA was used to predict the beam range and spread‐out Bragg peak (SOBP) for two‐dimensional (2D) map conversion from the scintillation light distribution (LD) into the dose distribution in a water phantom.MethodsThe 2D residual U‐net modeling for deep learning was used to predict the 2D water dose map from a 2D scintillation LD map. Monte Carlo simulations for dataset preparation were performed with varying monoenergetic proton beam energies, field sizes, and beam axis shifts. The LD was reconstructed using photons backpropagated from the aperture as a virtual lens. The SOBP samples were constructed based on monoenergetic dose distributions. The training set, including the validation set, consisted of 8659 image pairs of LD and water dose maps. After training, dose map prediction was performed using a 300 image pair test set generated under random conditions. The pairs of simulated and predicted dose maps were analyzed by Bragg peak fitting and gamma index maps to evaluate the model prediction.ResultThe estimated beam range and SOBP width resolutions were 0.02 and 0.19 mm respectively for varying beam conditions, and the beam range and SOBP width deviations from the reference simulation result were less than 0.1 and 0.8 mm respectively. The simulated and predicted distributions showed good agreement in the gamma analysis, except for rare cases with failed gamma indices in the proximal and field‐marginal regions.ConclusionThe deep learning conversion method using scintillation LDs in an optical camera system with a scintillator is feasible for estimating proton beam range and SOBP width with high accuracy.

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Measurement of ionization quenching in plastic scintillators

Cited by 17 publications

References 17 publications

Modeling ionization quenching in organic scintillators

Modeling ionization quenching in organic scintillators

Nonlinear Response and Origin of Slow Component of Two-Dimensional Perovskite Scintillator

Deep learning proton beam range estimation model for quality assurance based on two‐dimensional scintillated light distributions in simulations

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