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Nanoscale complex metal oxides have transformed how technology is used around the world. A ubiquitous example is the class of electroreactive cathodes used in Li-ion batteries, found in portable electronics and electric cars. Lack of recycling infrastructure and financial drivers contribute to improper disposal, and ultimately, introduction of these materials into the environment. Outside of sealed operational conditions, it has been demonstrated that complex metal oxides can transform in the environment, and cause negative biological impact through leaching of cations into aqueous phases. Using a combined DFT and thermodynamics methodology, insights into the mechanism and driving forces of cation release can be studied at the molecular-level. Here, we describe design principles that can be drawn from previous collaborative research on complex metal oxide dissolution of the Li(Ni y Mn z Co 1−y−z )O 2 family of materials, and go on to posit ternary complex metal oxides in the delafossite structure type with controlled release behavior. Using equistoichiometric formulations in the delfossite structure, we use DFT and thermodynamics to model cation release. The release trends are discussed in terms of lattice stability, solution chemistry/solubility limits, and electronic/magnetic properties. Intercalation voltages are calculated and discussed as a predictive metric for potential functionality of the model materials.
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Metal Release Mechanism and Electrochemical Properties of Lix(Ni1/3Mn1/3Co1/3)O2
Complex metal oxides (CMOs) are used broadly in applications including electroreactive forms found in lithium-ion battery technology. Computational chemistry can provide unique information about how the properties of CMO cathode materials change in response to changes in stoichiometry, for example, changes of the lithium (Li) content during the charge–discharge cycle of the battery. However, this is difficult to measure experimentally due to the small cross-sectional area of the cations. Outside of operational conditions, the Li content can influence the transformations of the CMO when exposed to the environment. For example, metal release from CMOs in aqueous settings has been identified as a cross-cutting mechanism important to CMO degradation. Computational studies investigating metal release from CMOs show that the thermodynamics depend on the oxidation states of lattice cations, which is expected to vary with the lithium content. In this work, computational studies track changes in metal release trends as a function of Li content in Lix(Ni1/3Mn1/3Co1/3)O2 (NMC). The resulting dataset is used to construct a random forest tree (RFT) machine learning (ML) model. A modeling challenge in delithiation studies is the large configurational space to sample. Through investigating multiple configurations at each lithium fraction, we find structural features associated with favorable energies to chemically guide the identification of relevant structures and adequately predict voltage values.
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- Award ID(s):
- 2001611
- PAR ID:
- 10322156
- Date Published:
- Journal Name:
- Applied Sciences
- Volume:
- 12
- Issue:
- 8
- ISSN:
- 2076-3417
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/app12084065
