Modeling the thermochemistry of nitrogen-containing compounds via group additivity†

IF 2.9 3区 化学 Q3 CHEMISTRY, PHYSICAL Physical Chemistry Chemical Physics Pub Date : 2024-07-02 DOI:10.1039/D4CP00727A
Cato A. R. Pappijn, Ruben Van de Vijver, Marie-Françoise Reyniers, Maarten K. Sabbe, Guy B. Marin and Kevin M. Van Geem
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Abstract

First-principles based kinetic modeling is essential to gain insight into the governing chemistry of nitrogen-containing compounds over a wide range of technologically important processes, e.g. pyrolysis, oxidation and combustion. It also enables the development of predictive, fundamental models key to improving understanding of the influence of nitrogen-containing compounds present as impurities or process additives, considering safety, operability and quality of the product streams. A prerequisite for the generation of detailed fundamental kinetic models is the availability of accurate thermodynamic properties. To address the scarcity of thermodynamic properties for nitrogen-containing compounds, a consistent set of 91 group additive values and three non-nearest-neighbor interactions has been determined from a dataset of CBS-QB3 calculations for 300 species, including 104 radicals. This dataset contains a wide range of nitrogen-containing functionalities, i.e. imine, nitrile, nitro, nitroso, nitrite, nitrate and azo functional groups. The group additivity model enables the approximation of the standard enthalpy of formation and standard entropy at 298 K as well as the standard heat capacities over a large temperature range, i.e. 300–1500 K. For a test set of 27 nitrogen-containing compounds, the group additivity model succeeds in approximating the ab initio calculated values for the standard enthalpy of formation with a MAD of 2.3 kJ mol−1. The MAD for the standard entropy and heat capacity is lower than 4 and 2 J mol−1 K−1, respectively. For a test set of 11 nitrogen-containing compounds, the MAD between experimental and group additivity approximated values for the standard enthalpy of formation amounts to 2.8 kJ mol−1.

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通过基团相加性建立含氮化合物热化学模型
基于第一性原理的动力学建模对于深入了解含氮化合物在热解、氧化和燃烧等各种重要技术过程中的化学规律至关重要。此外,考虑到产品流的安全性、可操作性和质量,该方法还能开发预测性基本模型,这对于更好地理解作为杂质或工艺添加剂存在的含氮化合物的影响至关重要。生成详细的基本动力学模型的先决条件是获得准确的热力学特性。为了解决含氮化合物热力学性质稀缺的问题,我们从 CBS-QB3 计算数据集中为 300 种物质(包括 104 个自由基)确定了一套一致的 91 个基团加成值和三种非近邻相互作用。该数据集包含多种含氮官能团,即亚胺、腈、硝基、亚硝基、亚硝酸盐和偶氮官能团。对于 27 种含氮化合物的测试集,基团加性模型成功地逼近了 298 K 时的标准形成焓和标准熵以及较大温度范围(即 300-1500 K)内的标准热容。标准熵和热容的 MAD 分别低于 4 和 2 J mol-1 K-1。在一组 11 种含氮化合物的测试中,标准形成焓的实验值和组加性近似值之间的 MAD 值为 2.8 kJ mol-1。
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来源期刊
Physical Chemistry Chemical Physics
Physical Chemistry Chemical Physics 化学-物理:原子、分子和化学物理
CiteScore
5.50
自引率
9.10%
发文量
2675
审稿时长
2.0 months
期刊介绍: Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.
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