Insights into the Mechanism of Ozone Activation and Singlet Oxygen Generation on N-Doped Defective Nanocarbons: A DFT and Machine Learning Study

单线态氧 催化作用 密度泛函理论 兴奋剂 光化学 吸附 活性氧 氧气 活动站点 化学 反应机理 单重态 氧原子 计算化学 材料科学 分子 物理化学 激发态 有机化学 物理 原子物理学 生物化学 光电子学
作者
Guangfei Yu,Yiqiu Wu,Hongbin Cao,Qingfeng Ge,Qin Dai,Sihan Sun,Yongbing Xie
出处
期刊:Environmental Science & Technology [American Chemical Society]
卷期号:56 (12): 7853-7863 被引量:47
标识
DOI:10.1021/acs.est.1c08666
摘要

N-doped defective nanocarbon (N-DNC) catalysts have been widely studied due to their exceptional catalytic activity in many applications, but the O3 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 O3 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 O3 decomposes into an adsorbed atomic oxygen species (Oads) and an 3O2 on the active sites. The atomic charge and spin population on the Oads 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 N4V2 system (quadri-pyridinic N with two vacancies) and the pyridinic N site at edge, O3 could be activated into 1O2 in addition to 3O2. The N4V2 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 O3 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.
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