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

Guangfei Yu; Yiqiu Wu; Hongbin Cao

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

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

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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 (O_(ads)) and an ~3O2 on the active sites. The atomic charge and spin population on the O_(ads) species indicate that it may not only act as an initiator for generating reactive oxygen species (ROS) but also direcdy 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 'O2 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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《Environmental Science & Technology: ES&T》 |2022年第12期|7853-7863|共11页
作者
Guangfei Yu; Yiqiu Wu; Hongbin Cao;
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作者单位

Chemistry & Chemical Engineering Data Center, Beijing Engineering Research Center of Process Pollution Control, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
原文格式 PDF
正文语种英语
中图分类环境科学、安全科学;
关键词
catalytic ozonation; DFT; machine learning; N-doped nanocarbons; ozone activation mechanism;

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

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