Prediction of the thermochemical properties of nitrogen-containing species: a quantum chemical calculation and group additivity approach
With the growing demand for nitrogen-containing sustainable fuels and propellants, accurately predicting their thermochemical properties has become increasingly important. While quantum chemical calculation (QC) methods and calorimetric experiments offer high precision, they are often time-consuming...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Published: |
American Chemical Society
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/120741/ https://pubs.acs.org/doi/10.1021/acs.jpca.5c01264 |
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| Summary: | With the growing demand for nitrogen-containing sustainable fuels and propellants, accurately predicting their thermochemical properties has become increasingly important. While quantum chemical calculation (QC) methods and calorimetric experiments offer high precision, they are often time-consuming and computationally intensive. In contrast, the group additivity (GA) method provides a faster alternative. However, its accuracy typically declines for complex nitrogen-containing compounds. In this study, we calculated the thermochemical properties of 283 nitrogen-containing species using ab initio composite methods (G3, G4, CBS-APNO, CBS-QB3). The QC results were used to optimize 43 existing GA groups and to develop 32 new groups for nitrogen-containing structures. Compared to Active Thermochemical Tables (ATcT), the QC methods achieved a 95% confidence interval (CI) of ±1.173 kcal/mol for ΔfH°0K. The optimized GA model (without the newly developed groups) achieved CIs of ±1.645 kcal/mol for ΔfH°298Kand ±4.222 cal/(mol·K) for entropy, with specific heat capacity (Cp) uncertainties ranging from ±1.144 to ±1.441 cal/(mol·K) over 300–1000 K. After adding the newly developed groups, the GA model improved, yielding CIs of ±1.894 kcal/mol for ΔfH°298Kand ±3.221 cal/(mol·K) for entropy. This work demonstrates an efficient framework for enhancing GA-based thermochemistry predictions using quantum data. This study’s results could enable more accurate combustion modeling, better control of nitrogen oxide emissions, and the design of advanced nitrogen-containing materials. |
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