Objective

A strategic research priority for the U.S. Department of Defense is the development and deployment of munitions formulations based on insensitive munition compounds (IMC) rather than traditional explosives such as TNT, RDX, and HMX. To ensure that these materials do not create new environmental impact issues, there is a need to understand the environmental fate and the risk associated with the manufacturing and use of the new generation of IMC before putting these chemicals into widespread use. This project aimed to provide modeling tools that use methods that are versatile, flexible, and robust to predict reaction products, transformation pathways, and fate-determining physicochemical properties of existing as well as candidate/future (I)MCs.

Technical Approach

The methods employed in this project included: (i) determination of IMC transformation pathways and products, (ii) determination of the fate-determining properties of IMC transformation products, and (iii) development of models that describe the fate and transport of IMC transformation products.

Results

For the identification of transformation products (i), data on experimentally observed products and reaction rates were mined from the literature and existing models for the prediction of reaction products and transformation pathways were evaluated. The results from these tasks guided the development of this project’s own machine-learning based predictive model, Arrows.

For the determination of product properties (ii), data on physicochemical properties were collected from the literature and a few models were tested. The data suggest that IMC and their transformation products have an increased environmental mobility relative to legacy munition compounds (MC). The models were able to estimate properties of legacy MC and IMC with similar molecular structures within reasonable prediction errors, but yielded larger uncertainties for IMC with unconventional molecular structures.

With respect to the fate and transport models (iii), it was determined that the transformation in surface soils was the most important capability lacking in the pre-existing Training Range Environmental Evaluation and Characterization System (TREECS) model framework. The TREECS Soil Surface Contaminant Fate Model was upgraded to include the capability to track mass from parent to daughter species and through multiple succeeding reaction pathways. The capability to transmit fluxes of multiple tracked species to downgradient media (vadose zone, surface water) was also added.

Benefits

The benefits of this project span both specific and general applications. The specific application is enabling Department of Defense training site managers to include consideration of (I)MC transformation and products into the modeling they do for evaluation of exposure assessment scenarios, risk, remediation, etc. General applications are through the advancement of methods for prediction of transformation pathways, chemical properties, and reactive transport. (Project Completion – 2023) 

Publications

Bylaska, E.J., D. Song, E.S. Ilton, S. O’Leary, T.L. Torralba-Sánchez, and P.G. Tratnyek. 2021. Building Toward the Future in Chemical and Materials Simulation with Accessible and Intelligently Designed Web Applications. Annual Reports in Computational Chemistry, 17:163-208. doi.org/10.1016/bs.arcc.2021.09.003.

Torralba-Sanchez, T.L., E.J. Bylaska, A.J. Salter-Blanc, D.E. Meisenheimer, M.A. Lyon, and P.G. Tratnyek. 2020. Reduction of 1,2,3-trichloropropane (TCP): Pathways and Mechanisms from Computational Chemistry Calculations. Environmental Science: Process and Impacts, 22(3):606-616. doi.org/10.1039/C9EM00557A.

Torralba-Sanchez, T.L. and P.G. Tratnyek. 2020. R Script for the Automated Generation of Reaction Coordinate Diagrams (RCDs) of Chemical Reaction Energies and Transformation Networks. Zenodo. doi.org/10.5281/zenodo.3611472.