Yu Guangfei, Wu Yiqiu, Cao Hongbin, Ge Qingfeng, Dai Qin, Sun Sihan, Xie Yongbing
Chemistry & Chemical Engineering Data Center, Beijing Engineering Research Center of Process Pollution Control, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Environ Sci Technol. 2022 Jun 21;56(12):7853-7863. doi: 10.1021/acs.est.1c08666. Epub 2022 May 26.
N-doped defective nanocarbon (N-DNC) catalysts have been widely studied due to their exceptional catalytic activity in many applications, but the O activation mechanism in catalytic ozonation of N-DNCs has yet to be established. In this study, we systematically mapped out the detailed reaction pathways of O activation on 10 potential active sites of 8 representative configurations of N-DNCs, including the pyridinic N, pyrrolic N, N on edge, and porphyrinic N, based on the results of density functional theory (DFT) calculations. The DFT results indicate that O decomposes into an adsorbed atomic oxygen species (O) and an O on the active sites. The atomic charge and spin population on the O species indicate that it may not only act as an initiator for generating reactive oxygen species (ROS) but also directly attack the organics on the pyrrolic N. On the N site and C site of the NV system (quadri-pyridinic N with two vacancies) and the pyridinic N site at edge, O could be activated into O in addition to O. The NV system was predicted to have the best activity among the N-DNCs studied. Based on the DFT results, machine learning models were utilized to correlate the O activation activity with the local and global properties of the catalyst surfaces. Among the models, XGBoost performed the best, with the condensed dual descriptor being the most important feature.
氮掺杂缺陷纳米碳（N-DNC）催化剂因其在许多应用中具有出色的催化活性而受到广泛研究，但N-DNCs催化臭氧化中氧的活化机制尚未明确。在本研究中，基于密度泛函理论（DFT）计算结果，我们系统地描绘了8种代表性构型的N-DNCs的10个潜在活性位点上氧活化的详细反应路径，包括吡啶氮、吡咯氮、边缘氮和卟啉氮。DFT结果表明，氧在活性位点上分解为吸附的原子氧物种（O）和一个O。O物种上的原子电荷和自旋布居表明，它不仅可能作为产生活性氧物种（ROS）的引发剂，还可能直接攻击吡咯氮上的有机物。在NV体系（具有两个空位的四吡啶氮）的氮位点和碳位点以及边缘的吡啶氮位点上，除了O之外，O还可以被活化为O。在所研究的N-DNCs中，NV体系被预测具有最佳活性。基于DFT结果，利用机器学习模型将氧活化活性与催化剂表面的局部和全局性质相关联。在这些模型中，XGBoost表现最佳，凝聚双描述符是最重要的特征。
