p prediction of per- and polyfluoroalkyl acids in water using gas phase stretching vibrational frequencies and infrared intensities.

To successfully understand and model the environmental fate of per- and polyfluoroalkyl substances (PFAS), it is necessary to know key physicochemical properties (PChPs) such as p; however, measured PChPs of PFAS are scarce and of uncertain reliability. In this study, quantitative structure-activity relationships (QSARs) were developed by correlating calculated (M062-X/aug-cc-pVDZ) vibrational frequencies (VF) and corresponding infrared intensities (IR) to the p of carboxylic acids, sulfonic acids, phosphonic acids, sulfonamides, betaines, and alcohols. Antisymmetric stretching VF of the anionic species were used for all subclasses except for alcohols where the OH stretching VF performed better.

Modeling the Partitioning of Anionic Carboxylic and Perfluoroalkyl Carboxylic and Sulfonic Acids to Octanol and Membrane Lipid.

Perfluoroalkyl carboxylic and sulfonic acids (PFCAs and PFSAs, respectively) have low acid dissociation constant values and are, therefore, deprotonated under most experimental and environmental conditions. Hence, the anionic species dominate their partitioning between water and organic phases, including octanol and phospholipid bilayers which are often used as model systems for environmental and biological matrices. However, data for solvent-water (SW) and membrane-water partition coefficients of the anion species are only available for a few per- and polyfluoroalkyl substances (PFAS).

Computational Predictions of the Hydrolysis of 2,4,6-Trinitrotoluene (TNT) and 2,4-Dinitroanisole (DNAN).

Hydrolysis is a common transformation reaction that can affect the environmental fate of many organic compounds. In this study, three proposed mechanisms of alkaline hydrolysis of 2,4,6-trinitrotoluene (TNT) and 2,4-dinitroaniline (DNAN) were investigated with plane-wave density functional theory (DFT) combined with ab initio and classical molecular dynamics (AIMD/MM) free energy simulations, Gaussian basis set DFT calculations, and correlated molecular orbital theory calculations. Most of the computations in this study were carried out using the Arrows web-based tools.

Quantitative structure activity relationships (QSARs) and machine learning models for abiotic reduction of organic compounds by an aqueous Fe(II) complex.

Due to the increasing diversity of organic contaminants discharged into anoxic water environments, reactivity prediction is necessary for chemical persistence evaluation for water treatment and risk assessment purposes. Almost all quantitative structure activity relationships (QSARs) that describe rates of contaminant transformation apply only to narrowly-defined, relatively homogenous families of reactants (e.g.

Reduction of 1,2,3-trichloropropane (TCP): pathways and mechanisms from computational chemistry calculations.

The characteristic pathway for degradation of halogenated aliphatic compounds in groundwater or other environments with relatively anoxic and/or reducing conditions is reductive dechlorination. For 1,2-dihalocarbons, reductive dechlorination can include hydrogenolysis and dehydrohalogenation, the relative significance of which depends on various structural and energetic factors. To better understand how these factors influence the degradation rates and products of the lesser halogenated hydrocarbons (in contrast to the widely studied per-halogenated hydrocarbons, like trichloroethylene and carbon tetrachloride), density functional theory calculations were performed to compare all of the possible pathways for reduction and elimination of 1,2,3-trichloropropane (TCP).

Estimating system parameters for solvent-water and plant cuticle-water using quantum chemically estimated Abraham solute parameters.

Polyparameter Linear Free Energy Relationships (pp-LFERs) using Abraham system parameters have many useful applications. However, developing the Abraham system parameters depends on the availability and quality of the Abraham solute parameters. Using Quantum Chemically estimated Abraham solute Parameters (QCAP) is shown to produce pp-LFERs that have lower root mean square errors (RMSEs) of predictions for solvent-water partition coefficients than parameters that are estimated using other presently available methods.

Bioconcentration factors and plant-water partition coefficients of munitions compounds in barley.

Plants growing in the soils at military ranges and surrounding locations are exposed, and potentially able to uptake, munitions compounds (MCs). The extent to which a compound is transferred from the environment into organisms such as plants, referred to as bioconcentration, is conventionally measured through uptake experiments with field/synthetic soils. Multiple components/phases that vary among different soil types and affect the bioavailability of the MC, however, hinder the ability to separate the effects of soil characteristics from the MC chemical properties on the resulting plant bioconcentration.

Estimating Grass-Soil Bioconcentration of Munitions Compounds from Molecular Structure.

A partitioning-based model is presented to estimate the bioconcentration of five munitions compounds and two munition-like compounds in grasses. The model uses polyparameter linear free energy relationships (pp-LFERs) to estimate the partition coefficients between soil organic carbon and interstitial water and between interstitial water and the plant cuticle, a lipid-like plant component. Inputs for the pp-LFERs are a set of numerical descriptors computed from molecular structure only that characterize the molecular properties that determine the interaction with soil organic carbon, interstitial water, and plant cuticle.