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MCNP benchmark of a 252Cf source-based PGNAA system for bulk sample analysis.

Can Cheng1, Zhiyong Wei2, Daqian Hei3

  • 1College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China; Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Suzhou, 215000, China.

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Summary

A validated MCNP model accurately simulates a Californium-252 (252Cf) based prompt gamma-ray neutron activation analysis (PGNAA) system. This simulation tool is reliable for bulk sample analysis, showing good agreement with experimental measurements.

Keywords:
BenchmarkMonte Carlo simulationPGNAA

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Area of Science:

  • Nuclear Physics
  • Analytical Chemistry
  • Computational Modeling

Background:

  • Prompt Gamma-ray Neutron Activation Analysis (PGNAA) is a powerful technique for elemental analysis.
  • Accurate modeling is crucial for optimizing PGNAA system design and performance.
  • Californium-252 (252Cf) is a common neutron source for PGNAA applications.

Purpose of the Study:

  • To benchmark a Monte Carlo N-Particle (MCNP) model of a 252Cf source-based PGNAA system.
  • To validate the MCNP model's ability to represent the physical system for bulk sample analysis.
  • To assess the accuracy of the model by comparing simulation results with experimental data.

Main Methods:

  • Development of an MCNP model for a 252Cf PGNAA system.
  • Experimental measurements using indium foils and aqueous chlorine samples.
  • Comparison of simulated thermal neutron flux and prompt gamma-ray response with experimental data.

Main Results:

  • Discrepancies between simulation and measurement were within 6% for thermal neutron flux.
  • Discrepancies were within 20% for prompt gamma-ray response.
  • The MCNP model demonstrated good agreement with experimental results.

Conclusions:

  • The developed MCNP model accurately represents the 252Cf PGNAA system.
  • The validated model can be reliably used for simulating and optimizing PGNAA systems for bulk sample analysis.
  • This work confirms the utility of MCNP for PGNAA system characterization.