Nickel manganese cobalt oxide (NMC) nanoparticles have attracted attention as a possible energy storage medium. However, as these nanoparticles are used in increasing number, their environmental impact will become ever more salient. Although studies on the effects of exposing the environment to NMC nanoparticles are generating a great amount of data, they are often analyzed in isolation. In this work, data science and machine learning techniques, including artificial neural networks, have been used to extract additional information from a curated dataset obtained from published work in the field primarily by investigators from the Center of Sustainable Nanotechnology. These methods are compared and contrasted according to their ability to successfully predict cell viability upon exposure to nanoparticles and are used jointly to build a greater understanding of the effects nanoparticles have on cells, with a particular focus on NMC nanoparticles.<br/>

Uncovering design rules for environmentally friendly nanoparticle enabled energy storage via machine learning

2019 Fall National Meeting

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