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The transformations of complex metal oxides in aqueous settings must be studied to form a chemical understanding of how technologically relevant nanomaterials impact the environment upon disposal. Owing to the inherent heterogeneity and structural complexity of the ternary intercalation material Li(NixMnyCo1-x-y)O2 (NMC), the mechanisms of chemical processes at the solid–water interface are challenging to model. Here, density functional theory (DFT) + solvent ion methodology is used to study the energetics of stepwise release of two surface metals following unique pathways. The study spans different combinations of metal removal and also considers unique patterns of defects formed by modeling the NMC surface in supercells. The approach here also considers the equilibration of the surface with the surroundings between successive metal removals. A key finding is that a second metal removal prefers to proceed at a metal lattice site adjacent to the initial defect, and this is attributed in part to how the resulting slab with two metal vacancies maintains the most antiferromagnetic couplings between the remaining Ni/Mn. 

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. 

Seafood mislabeling occurs when a market label is inaccurate, primarily in terms of species identity, but also regarding weight, geographic origin, or other characteristics. This widespread problem allows cheaper or illegally-caught species to be marketed as species desirable to consumers. Previous studies have identified red snapper (Lutjanus campechanus) as one of the most frequently mislabeled seafood species in the United States. To quantify how common mislabeling of red snapper is across North Carolina, the Seafood Forensics class at the University of North Carolina at Chapel Hill used DNA barcoding to analyze samples sold as “red snapper” from restaurants, seafood markets, and grocery stores purchased in ten counties. Of 43 samples successfully sequenced and identified, 90.7% were mislabeled. Only one grocery store chain (of four chains tested) accurately labeled red snapper. The mislabeling rate for restaurants and seafood markets was 100%. Vermilion snapper (Rhomboplites aurorubens) and tilapia (Oreochromis aureusandO. niloticus) were the species most frequently substituted for red snapper (13 of 39 mislabeled samples for both taxa, or 26 of 39 mislabeled total). This study builds on previous mislabeling research by collecting samples of a specific species in a confined geographic region, allowing local vendors and policy makers to better understand the scope of red snapper mislabeling in North Carolina. This methodology is also a model for other academic institutions to engage undergraduate researchers in mislabeling data collection, sample processing, and analysis.