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Utilization of Artificial Neural Network to explore the compositional space of hollandite-structured materials for radionuclide Cs incorporation
Journal of Nuclear Materials ( IF 2.8 ) Pub Date : 2019-12-18 , DOI: 10.1016/j.jnucmat.2019.151957
Dipta B. Ghosh , Bijaya B. Karki , Jianwei Wang

Hollandite with the 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.



中文翻译:

利用人工神经网络探究钙铝石结构材料掺入放射性核素Cs的组成空间

通式为A 2 B 8 O 16的铝矾土以其沿z方向的通道中固定放射性核素Cs的潜力而著称。结构的-轴。有效地掺入具有最佳负载量和长期稳定性的方钠石相中的Cs很大程度上取决于B-位阳离子,B-位阳离子除了提供最佳的结构相容性外,还必须确保该相在水性环境中对化学风化的抵抗力。包括外部热力学条件,例如温度和溶液化学性质。基于B位阳离子的重要性,我们通过使用人工神经网络(ANN)模拟和晶体化学原理详细探讨了可能的B位组成。利用开放文献中收集到的以重晶石为基础的91个实验确定的数据集,我们对网络进行了训练,随后测试了训练后的网络的预测能力。在测试阶段依靠训练有素的网络的成功成果,我们进一步利用训练有素的网络来映射隧道大小的依赖性,该隧道大小被用作信道中Cs兼容性的标准,涵盖了18个3 +阳离子和15个4 +阳离子。通过结合Cs的相容性和对白铁矿结构的结构耐受性因子,可以将预测的B部位组成(包括跨越元素周期表的深度和宽度的阳离子)用作寻找Cs最佳白铁矿组成的指南。固定。在一个广阔的组成空间中,包含18个3 +阳离子和15个4 +阳离子。通过结合Cs的相容性和对白铁矿结构的结构耐受性因子,可以将预测的B部位组成(包括跨越元素周期表深度和宽度的阳离子)用作寻找Cs最佳白铁矿组成的指南。固定。在一个广阔的组成空间中,包含18个3 +阳离子和15个4 +阳离子。通过结合Cs的相容性和对白铁矿结构的结构耐受性因子,可以将预测的B部位组成(包括跨越元素周期表的深度和宽度的阳离子)用作寻找Cs最佳白铁矿组成的指南。固定。

更新日期:2019-12-19
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