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Radiation: Applications01:17

Radiation: Applications

The average temperature of Earth is the subject of much current discussion. Earth is in radiative contact with both the Sun and dark space; it receives almost all its energy from the radiation of the Sun and reflects some of it into outer space. Dark space is very cold, about 3 K, so Earth radiates energy into it. For instance, heat transfer occurs from soil and grasses, the rate of which can be so rapid that frost can occur on clear summer evenings, even in warm latitudes.
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Related Experiment Video

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Surrogate Model Development for Digital Experiments in Welding
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Development of a model for design radiation shielding composite aprons using machine learning.

M R Alipoor1, M Eshghi2, R Razavi3

  • 1Faculty of Basic Sciences, Imam Hossein University, Tehran, Iran. mohamadrezaalipoor1997@gmail.com.

Scientific Reports
|May 25, 2026
PubMed
Summary

A novel lead-free composite, optimized using artificial neural networks (ANNs), offers superior radiation shielding. This sustainable material provides high attenuation, outperforming commercial alternatives for medical and industrial applications.

Keywords:
Artificial neural networksGeant4Machine learningOptimizingPhotoelectricShieldingX-ray

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Area of Science:

  • Materials Science
  • Nuclear Engineering
  • Computational Physics

Background:

  • Traditional lead-based materials pose environmental and health risks.
  • Development of effective, lead-free radiation shielding is crucial for medical and industrial applications.
  • Artificial intelligence offers novel approaches to material design and optimization.

Purpose of the Study:

  • To develop and evaluate a novel lead-free composite for radiation shielding.
  • To utilize artificial neural networks (ANNs) for optimizing material composition and predicting shielding performance.
  • To assess the shielding efficacy and compare it with existing lead-free and lead-based materials.

Main Methods:

  • Design and training of an artificial neural network (ANN) model to predict the mass attenuation coefficient.
  • Optimization of a Barium-Gadolinium-Iodine-Antimony (Ba-Gd-I-Sb) composite using the ANN model.
  • Validation of ANN predictions against Geant4 Monte Carlo simulations.
  • Experimental evaluation of the shielding performance of the optimized composite.

Main Results:

  • The ANN model achieved high accuracy (R=0.997, MAPE=0.9-1.8%) in predicting mass attenuation coefficients.
  • The optimized Ba-Gd-I-Sb composite demonstrated superior shielding properties due to synergistic multi-edge absorption.
  • A 1-mm thickness provided >10⁵ attenuation at 50 keV and >95% absorption at 1.0 mm.
  • The composite's 99% attenuation at 80 keV surpassed commercial non-lead alternatives (97-98%).

Conclusions:

  • The developed lead-free composite exhibits excellent radiation shielding performance, comparable or superior to lead.
  • The use of ANNs significantly aids in the efficient design and optimization of radiation shielding materials.
  • This novel composite presents a sustainable and high-performance alternative for radiation protection in medical and industrial settings.