CDS&E: Development of Computational Library for Accurate Binding Energies of Emerging Organic Contaminants on Environmental Interfaces

University Of Memphis, Memphis TN

Investigators

Abstract

When a chemical spill incident happens, emergency responders act to protect human health and the environment. However, information is not always available about how the spilled chemical will interact and move through the environment, where it will end up, or how it might be filtered out. After a spill, methods that can quickly predict these behaviors would be useful. With support from the Environmental Chemical Sciences (ECS) Program of the Chemistry Division, Professor William Alexander of The University of Memphis is using computer models to quickly estimate how contaminants may act after a spill. Working with his students, Professor Alexander develops automated computational chemistry tools to predict the physical properties of contaminants, and to predict "where they might stick?" on environmental or infrastructure surfaces. The group is building a data library of environmentally-relevant surface models and using these models to computationally screen what surfaces the contaminants may stick to. The group's resulting models and tools could allow responders to obtain rapid estimates of the behavior of novel contaminant compounds. This will help aid and direct remediation strategies that could have a significant impact on public safety and post crisis economic recovery. The project is also providing multidisciplinary collaborative scientific training for graduate and undergraduate students from underrepresented student populations. Outreach efforts are designed to expand access to laboratory science for home-schooled students, with a focus on environmental science and chemistry laboratory experiences. About 172,00 chemical spills impacting US bodies of water were reported to the US National Response Center from 2004-2014. Some of these are due to chemicals with known properties and toxicity, whereas for others, relatively little information is available. Qualitative models are often used to predict contaminant fate and transport. These models generally do not treat important dynamical aspects of contaminant behavior such as solvation effects and conformational averaging. Also, most relevant surfaces that contaminants interact with in the environment (silts, clays, etc.) and within the water system (filter media, polymer plumbing, etc.) are dynamical and amorphous in nature. Incorporating these dynamical aspects into prediction models will increase the accuracy of the resulting physical properties and binding estimates. In the project, input files and job management are automated for rapid quantum mechanical (QM) prediction of contaminant physical properties, including conformation and solvation effects. A library of relevant amorphous surface models (i.e. amorphous silica, carbon, polymers) derived from molecular mechanics (MM) simulations is generated. These approaches are combined into QM/MM schemes to enable rapid computation of contaminant binding energies to direct filter media selection and to predict where contaminants may concentrate. The methods are validated using a large test set of known organic contaminant molecules in a range of compound classes, including dozens of compounds selected from public health agency priority lists. Selected molecules are used to experimentally validate computed molecular properties by new partitioning and adsorption studies. Throughout the investigations, new workflows are developed to manage the large datasets. In addition to accurately predicting contaminant behavior, the resulting methods may help to reduce human exposure to contaminant compounds in the field by requiring less field samples and time. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.