2021 Annual Meeting
(364h) Predicting Chemical Adsorption Onto Montmorillonite Clays through a Combination of Experimental Isotherms, Minimalistic Simulations and Data-Driven Models
Authors
In this study, we aimed to close the loop between experiments and computations, providing the potential for future experiments to be guided by computational predictions estimating the capacity of calcium montmorillonite clay to serve as sorbents for different toxic chemicals. We performed molecular dynamics (MD) simulations of montmorillonite clay in the presence of toxic compounds, and revealed key structural and physicochemical properties associated with chemicals that can be adsorbed to the clay. Using data derived from simulations and experiments, we developed a parametric, minimalistic model with the capacity to predict a compoundâs affinity to the clay with high accuracy. Thus, such a model can be used as a screening tool to predict and guide experiments on the efficacy of clay to bind other toxic compounds. Additionally, such predictive approaches, combining simulations and data-driven modeling, can be used to enable the design of clay-based sorbents to bind to toxic compounds of interest that are otherwise difficult to mitigate. This research is supported by NIEHS Superfund Research Program P42 ES027704.
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