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Published on: October 18, 2018
A method to calculate the one-electron reduction potentials for nitroaromatic compounds based on gas-phase quantum
Kathy L Phillips1, Stanley I Sandler, Pei C Chiu
1Department of Chemical Engineering, Center for Molecular and Engineering Thermodynamics, University of Delaware, 150 Academy Street, Newark, Delaware 19716, USA.
A new quantum mechanics (QM) method accurately predicts the one-electron reduction potential (E°H) for nitroaromatic compounds (NACs). This efficient approach improves environmental fate modeling for these widespread contaminants.
Area of Science:
- Environmental Chemistry
- Computational Chemistry
- Quantum Mechanics
Background:
- Nitroaromatic compounds (NACs) are prevalent environmental contaminants.
- The one-electron reduction potential (E°H) is crucial for modeling NAC environmental fate.
- Accurate prediction of E°H is essential for environmental risk assessment.
Purpose of the Study:
- To develop an accurate and efficient method for predicting the E°H of NACs.
- To improve the reliability of environmental fate models for NACs.
- To overcome limitations of existing quantum mechanics (QM) methods in predicting E°H.
Main Methods:
- Utilized gas-phase quantum mechanics (QM) calculations combined with empirical correlations.
- Calculated adiabatic electron affinity (EA) at 0 K using the B98/MG3S method.
- Scaled EA predictions by 0.802 to correct for density functional calculation errors.
- Developed a linear correlation between E°H and EA for prediction.
Main Results:
- Achieved a mean absolute deviation of 0.021 V for 14 NACs used in correlation.
- Predicted E°H for six additional NACs with a mean absolute deviation of 0.029 V.
- Demonstrated substantial accuracy improvement over other QM methods, especially regarding solvation effects.
Conclusions:
- The developed QM-based method provides accurate and efficient E°H predictions for NACs.
- This method offers a significant advancement over existing QM approaches for environmental applications.
- Improved E°H predictions will enhance the accuracy of environmental fate modeling and risk assessment for NACs.
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