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DFT-and-Thermodynamincs-Surface-Cation-Release-LCO-acs.pdf (1.86 MB)

DFT and Thermodynamics Calculations of Surface Cation Release in LiCoO2

submitted on 03.09.2019, 19:58 and posted on 06.09.2019, 01:37 by ali abbaspour tamijani, Joseph W. Bennett, Diamond T. Jones, Natalia Cartagena-Gonzalez, Zachary R. Jones, Elizabeth D. Laudadio, Robert Hamers, Juan A. Santana, Sara E. Mason
When exposed to environmental conditions, LCO can release Co cations, a known toxicant. In this study, we build on previous work (Bennett et al., Environ. Sci. Technol., 52, 5792-5802, 2018, Bennett et al., Inorg. Chem., 57, 13300-13311, 2018) using theory and modeling to understand the thermodynamic driving forces of ion release in water. We assess how the calculated predictions for ion release depend on aspects of the structural surface model. For example, we vary the number of atomic layers used to form the slab, we explore different surface terminations and hydroxyl group coverages, and we vary the periodic in-plane supercell to assess how ion release depends on the density of formed vacancies. We also benchmark the DFT + Thermodynamics modeling across a range of computational factors such as the choice of exchange correlation functional and pseudopotential type. Such assessment is critical, as there is no direct experimental information for comparison. We devise a generalizable scheme for predicting a threshold pH at which Co release from LCO becomes favorable. We put forward that this scheme could provide information about how much Co is released from LCO under variable pH conditions, and could therefore be used to inform macroscopic contaminant fate models.


This work was supported by National Science Foundation Center for Chemical Innovation Program grant CHE-1503408 for the Center for Sustainable Nanotechnology. This research was supported in part through computational resources provided by The University of Iowa, Iowa City, Iowa and the National Science Foundation grant CHE-0840494. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562. Computational resources were also provided in part by the High-Performance Computing Facility at the University of Puerto Rico, supported by an Institutional Development Award (IDeA) INBRE Grant Number P20GM103475 from the National Institute of General Medical Sciences (NIGMS), a component of the National Institutes of Health (NIH), and the Bioinformatics Research Core of the INBRE. Its contents are solely the responsibility of the authors and do not necessarily represent the offcial view of NIGMS or NIH. E. D. L. acknowledges support by National Science Foundation Graduate Research Fellowship under DGE-1256259. The authors thank Dr. Chenyu Wang, Profs. Christy Haynes, Qiang Cui and Rigoberto Hernandez for useful discussions of this work.


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University of Iowa



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Declaration of Conflict of Interest

The authors declare no competing financial interest.