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Title: Utilization of Artificial Neural Network to explore the compositional space of hollandite-structured materials for radionuclide Cs incorporation

Abstract

Hollandite, general formula A2B8O16, is known for its potential to immobilize radionuclide Cs in the tunnel along the z-axis of the crystal structure. The effective Cs incorporation in a hollandite phase with an optimal loading capacity and the long term stability depends significantly on the B-site cations, which, in addition to providing optimal structural compatibility, must ensure the phase's resistance to chemical weathering in an aqueous environment that includes external thermodynamic conditions such as temperature and solution chemistry. Based on the importance of the B-site cations, we explored in detail the possible B-site compositions by employing Artificial Neural Network (ANN) simulations and crystal chemistry principles. With a set of 91 experimentally determined data collected on hollandite that is available in open literature, we trained the network and subsequently tested the predictive power of the trained network. Relying on the successful outcomes of the trained network at the testing phase, we further utilized the trained network to map the dependence of the tunnel size, which was used as a criterion for Cs compatibility in the channel, in a wide compositional space encompassing eighteen 3+ cations and fifteen 4+ cations. By combining the Cs compatibility and the structural tolerance factor for hollanditemore » structure, the predicted B-site compositions, comprising of cations spanning across the depth and breadth of the periodic table, can be employed as a guide in the search for optimal hollandite composition for Cs immobilization.« less

Authors:
 [1];  [2];  [3]
+ Show Author Affiliations
  1. Louisiana State Univ., Baton Rouge, LA (United States). School of Electrical Engineering and Computer Science; Louisiana State Univ., Baton Rouge, LA (United States). Dept. of Geology and Geophysics
  2. Louisiana State Univ., Baton Rouge, LA (United States). School of Electrical Engineering and Computer Science; Louisiana State Univ., Baton Rouge, LA (United States). Dept. of Geology and Geophysics; Louisiana State Univ., Baton Rouge, LA (United States). Center for Computation and Technology
  3. Louisiana State Univ., Baton Rouge, LA (United States). Dept. of Geology and Geophysics; Louisiana State Univ., Baton Rouge, LA (United States). Center for Computation and Technology
Publication Date:
Research Org.:
Energy Frontier Research Centers (EFRC) (United States). Center for Performance and Design of Nuclear Waste Forms and Containers (WastePD); Louisiana State Univ., Baton Rouge, LA (United States); Energy Frontier Research Center (United States). Center for Performance and Design of Nuclear Waste Forms and Containers; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1594080
Alternate Identifier(s):
OSTI ID: 1703246
Grant/Contract Number:  
SC0016584; AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Nuclear Materials
Additional Journal Information:
Journal Volume: 530; Journal Issue: C; Journal ID: ISSN 0022-3115
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS; 97 MATHEMATICS AND COMPUTING; Artificial Neural Network; hollandite; Cs immobilization

Citation Formats

Ghosh, Dipta B., Karki, Bijaya B., and Wang, Jianwei. Utilization of Artificial Neural Network to explore the compositional space of hollandite-structured materials for radionuclide Cs incorporation. United States: N. p., 2019. Web. doi:10.1016/j.jnucmat.2019.151957.
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Ghosh, Dipta B., Karki, Bijaya B., & Wang, Jianwei. Utilization of Artificial Neural Network to explore the compositional space of hollandite-structured materials for radionuclide Cs incorporation. United States. https://doi.org/10.1016/j.jnucmat.2019.151957
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Ghosh, Dipta B., Karki, Bijaya B., and Wang, Jianwei. Wed . "Utilization of Artificial Neural Network to explore the compositional space of hollandite-structured materials for radionuclide Cs incorporation". United States. https://doi.org/10.1016/j.jnucmat.2019.151957. https://www.osti.gov/servlets/purl/1594080.
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@article{osti_1594080,
title = {Utilization of Artificial Neural Network to explore the compositional space of hollandite-structured materials for radionuclide Cs incorporation},
author = {Ghosh, Dipta B. and Karki, Bijaya B. and Wang, Jianwei},
abstractNote = {Hollandite, general formula A2B8O16, is known for its potential to immobilize radionuclide Cs in the tunnel along the z-axis of the crystal structure. The effective Cs incorporation in a hollandite phase with an optimal loading capacity and the long term stability depends significantly on the B-site cations, which, in addition to providing optimal structural compatibility, must ensure the phase's resistance to chemical weathering in an aqueous environment that includes external thermodynamic conditions such as temperature and solution chemistry. Based on the importance of the B-site cations, we explored in detail the possible B-site compositions by employing Artificial Neural Network (ANN) simulations and crystal chemistry principles. With a set of 91 experimentally determined data collected on hollandite that is available in open literature, we trained the network and subsequently tested the predictive power of the trained network. Relying on the successful outcomes of the trained network at the testing phase, we further utilized the trained network to map the dependence of the tunnel size, which was used as a criterion for Cs compatibility in the channel, in a wide compositional space encompassing eighteen 3+ cations and fifteen 4+ cations. By combining the Cs compatibility and the structural tolerance factor for hollandite structure, the predicted B-site compositions, comprising of cations spanning across the depth and breadth of the periodic table, can be employed as a guide in the search for optimal hollandite composition for Cs immobilization.},
doi = {10.1016/j.jnucmat.2019.151957},
journal = {Journal of Nuclear Materials},
number = C,
volume = 530,
place = {United States},
year = {Wed Dec 18 00:00:00 EST 2019},
month = {Wed Dec 18 00:00:00 EST 2019}
}
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https://doi.org/10.1016/j.jnucmat.2019.151957
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