Surface reconstruction and cleavage of phyllosilicate clay edge by density functional theory

被引:2
作者
Ta, An T. [1 ]
Ullberg, R. Seaton [1 ]
Phillpot, Simon R. [1 ]
机构
[1] Univ Florida, Dept Mat Sci & Engn, Gainesville, FL 32611 USA
关键词
Clay mineral; Density functional theory; Surface formation; Edge reconstruction; Cleavage; Pyrophyllite; TOTAL-ENERGY CALCULATIONS; AB-INITIO; MOLECULAR-DYNAMICS; ION ADSORPTION; PYROPHYLLITE; WATER; MONTMORILLONITE; BASAL; DISPERSION; MORPHOLOGY;
D O I
10.1016/j.clay.2023.107178
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Phyllosilicate materials (e.g., smectite) have been proposed to act as a waste barrier within long-term deep geological repositories. Aside from longevity, these clay materials must also limit the mobility of radionuclides, which is achieved through intermolecular interaction involving clay basal planes and edge surfaces. We aim to contribute to the fundamental knowledge of clay edges by investigating surface reconstruction in a dehydrated context. Using density functional theory, surface reconstruction, charge redistribution, and formation energies have been explored for pyrophyllite edges resulting from thirteen terminations. Three primary reconstruction mechanisms were identified: (1) the displacement of Al into the tetrahedral layer, (2) proton shift, and (3) sub-surface reconstruction involving sub-surface-OH or apical O and surface Si. Reduction in overall relative bond deficiency and charge redistribution to becoming more bulk-like were found to be the main driving force for reconstruction. Formation energies were also determined with dispersion correction for dehydrated and hydrated surfaces. The presence of water terminating agents reduced energetic costs by approximately one order of magnitude and the inclusion of dispersion correction resulted in indistinguishable formation energies between different hydrated surfaces. Linear regression models were also explored to predict surface formation energies based on relative bond deficiency and charge. These models were able to identify the key contributions to the surface energy; however, due to the small dataset they were not found to be predictive.
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页数:12
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